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NAG Toolbox: nag_mip_iqp_sparse (h02ce)
Purpose
nag_mip_iqp_sparse (h02ce) obtains integer solutions to sparse linear programming and quadratic programming problems.
Syntax
[
ns,
xs,
istate,
miniz,
minz,
obj,
clamda,
ifail] = h02ce(
n,
m,
iobj,
ncolh,
qphx,
a,
ha,
ka,
bl,
bu,
start,
names,
crname,
ns,
xs,
intvar,
istate,
strtgy,
leniz,
lenz,
monit, 'nnz',
nnz, 'nname',
nname, 'lintvr',
lintvr, 'mdepth',
mdepth)
[
ns,
xs,
istate,
miniz,
minz,
obj,
clamda,
ifail] = nag_mip_iqp_sparse(
n,
m,
iobj,
ncolh,
qphx,
a,
ha,
ka,
bl,
bu,
start,
names,
crname,
ns,
xs,
intvar,
istate,
strtgy,
leniz,
lenz,
monit, 'nnz',
nnz, 'nname',
nname, 'lintvr',
lintvr, 'mdepth',
mdepth)
Description
nag_mip_iqp_sparse (h02ce) is designed to obtain integer solutions to a class of quadratic programming problems addressed by
nag_opt_qpconvex1_sparse_solve (e04nk). Specifically it solves the following problem:
where
x = (x_{1},x_{2}, … ,x_{n})^{T}$x={({x}_{1},{x}_{2},\dots ,{x}_{n})}^{\mathrm{T}}$ is a set of variables (some of which may be required to be integer),
A$A$ is an
m$m$ by
n$n$ matrix and the objective function
f(x)$f\left(x\right)$ may be specified in a variety of ways depending upon the particular problem to be solved. The optional parameter
Maximize may be used to specify an alternative problem in which
f(x)$f\left(x\right)$ is maximized. The possible forms for
f(x)$f\left(x\right)$ are listed in
Table 1, in which the prefixes LP and QP stand for ‘linear programming’ and ‘quadratic programming’ respectively,
c$c$ is an
n$n$element vector and
H$H$ is the
n$n$ by
n$n$ secondderivative matrix
∇^{2}f(x)${\nabla}^{2}f\left(x\right)$ (the
Hessian matrix).
Problem type 
Objective function
f(x)$f\left(x\right)$ 
Hessian matrix
H$H$ 
LP 
c^{T}x${c}^{\mathrm{T}}x$ 
Not applicable 
QP 
c^{T}x + (1/2)x^{T}Hx${c}^{\mathrm{T}}x+\frac{1}{2}{x}^{\mathrm{T}}Hx$ 
Symmetric positive semidefinite 
Table 1
For LP and QP problems, the unique global minimum value of f(x)$f\left(x\right)$ is found. For QP problems, you must also provide a function that computes Hx$Hx$ for any given vector x$x$. (H$H$ need not be stored explicitly.)
(It is not expected that the feasibility problem of
nag_opt_qpconvex1_sparse_solve (e04nk) would be relevant here.)
The function employs a ‘Branch and Bound’ technique to enforce the integer constraints. In this technique the problem is first solved without the integer constraints. If a variable is found to be nonintegral when it is required to have an integer value then two additional problems are constructed. One bounds the variable above by the nearest integer value below the optimal value previously obtained. The second problem is formed by bounding the variable below by the nearest integer value above the optimal value. This process is continued until an integer solution is found. At this point you may elect to stop, or may continue to search for better integer solutions by examining any other subproblems that remain to be explained.
In practice the function tries to compute an integer solution as quickly as possible using a depthfirst approach, since this helps determine a realistic cutoff value. If we have a cutoff value, say the value of the function at this first integer solution, and any subproblem,
W$W$ say, has a solution value greater than this cutoff value, then subsequent subproblems of
W$W$ must have solutions greater than the value of the solution at
W$W$ and therefore need not be computed. Thus a knowledge of a good cutoff value can result in fewer subproblems being solved and thus speed up the operation of the function. (See the description of
monit in
Section [Parameters] for details of how you can supply your own cutoff value.)
Each subproblem is solved using
(e04nk). You are referred to the function document for
nag_opt_qpconvex1_sparse_solve (e04nk) for details of the algorithm used.
References
Gill P E, Hammarling S, Murray W, Saunders M A and Wright M H (1986) Users' guide for LSSOL (Version 1.0) Report SOL 861 Department of Operations Research, Stanford University
Gill P E and Murray W (1978) Numerically stable methods for quadratic programming Math. Programming 14 349–372
Gill P E, Murray W, Saunders M A and Wright M H (1986) Some theoretical properties of an augmented Lagrangian merit function Report SOL 86–6R Department of Operations Research, Stanford University
Gill P E, Murray W, Saunders M A and Wright M H (1989) A practical anticycling procedure for linearly constrained optimization Math. Programming 45 437–474
Gill P E, Murray W, Saunders M A and Wright M H (1991) Inertiacontrolling methods for general quadratic programming SIAM Rev. 33 1–36
Hock W and Schittkowski K (1981) Test Examples for Nonlinear Programming Codes. Lecture Notes in Economics and Mathematical Systems 187 Springer–Verlag
Lawson C L, Hanson R J, Kincaid D R and Krogh F T (1979) Basic linear algebra supbrograms for Fortran usage ACM Trans. Math. Software 5 308–325
Murtagh B A and Saunders M A (1983) MINOS 5.0 user's guide Report SOL 8320 Department of Operations Research, Stanford University
Parameters
Compulsory Input Parameters
 1:
n – int64int32nag_int scalar
n$n$, the number of variables (excluding slacks). This is the number of columns in the linear constraint matrix A$A$.
Constraint:
n ≥ 1${\mathbf{n}}\ge 1$.
 2:
m – int64int32nag_int scalar
m$m$, the number of general linear constraints (or slacks). This is the number of rows in
A$A$, including the free row (if any; see
iobj).
Constraint:
m ≥ 1${\mathbf{m}}\ge 1$.
 3:
iobj – int64int32nag_int scalar
If
iobj > 0${\mathbf{iobj}}>0$, row
iobj of
A$A$ is a free row containing the nonzero elements of the vector
c$c$ appearing in the linear objective term
c^{T}x${c}^{\mathrm{T}}x$.
If
iobj = 0${\mathbf{iobj}}=0$, there is no free row, i.e., the problem is either an FP problem (in which case
iobj must be set to zero), or a QP problem with
c = 0$c=0$.
Constraint:
0 ≤ iobj ≤ m$0\le {\mathbf{iobj}}\le {\mathbf{m}}$.
 4:
ncolh – int64int32nag_int scalar
n_{H}${n}_{H}$, the number of leading nonzero columns of the Hessian matrix
H$H$. For FP and LP problems,
ncolh must be set to zero.
Constraint:
0 ≤ ncolh ≤ n$0\le {\mathbf{ncolh}}\le {\mathbf{n}}$.
 5:
qphx – function handle or string containing name of mfile
For QP problems, you must supply a version of
qphx to compute the matrix product
Hx$Hx$. If
H$H$ has rows and columns consisting entirely of zeros, it is most efficient to order the variables
x = (y z)^{T}$x={\left(y\text{\hspace{1em}}z\right)}^{\mathrm{T}}$ so that
where the nonlinear variables
y$y$ appear first as shown. For LP problems,
qphx will never be called by
nag_mip_iqp_sparse (h02ce).
[hx] = qphx(nstate, ncolh, x)
Input Parameters
 1:
nstate – int64int32nag_int scalar
If
nstate = 1${\mathbf{nstate}}=1$, then
nag_mip_iqp_sparse (h02ce) is calling
qphx for the first time on a subproblem. This parameter setting allows you to save computation time if certain data must be read or calculated only once.
If
nstate ≥ 2${\mathbf{nstate}}\ge 2$, then
nag_mip_iqp_sparse (h02ce) is calling
qphx for the last time. This parameter setting allows you to perform some additional computation on the final subproblem solution. In general, the last call to
qphx is made with
nstate = 2 + ifail${\mathbf{nstate}}=2+{\mathbf{ifail}}$ (see
Section [Error Indicators and Warnings]).
Otherwise,
nstate = 0${\mathbf{nstate}}=0$.
 2:
ncolh – int64int32nag_int scalar
This is the same parameter
ncolh as supplied to
nag_mip_iqp_sparse (h02ce).
 3:
x(ncolh) – double array
The first
ncolh elements of the vector
x$x$.
Output Parameters
 1:
hx(ncolh) – double array
The product Hx$Hx$.
 6:
a(nnz) – double array
nnz, the dimension of the array, must satisfy the constraint
1 ≤ nnz ≤ n × m$1\le {\mathbf{nnz}}\le {\mathbf{n}}\times {\mathbf{m}}$.
The nonzero elements of A$A$, ordered by increasing column index. Note that multiple elements with the same row and column indices are not allowed.
 7:
ha(nnz) – int64int32nag_int array
nnz, the dimension of the array, must satisfy the constraint
1 ≤ nnz ≤ n × m$1\le {\mathbf{nnz}}\le {\mathbf{n}}\times {\mathbf{m}}$.
ha(i)${\mathbf{ha}}\left(\mathit{i}\right)$ must contain the row index of the nonzero element stored in
a(i)${\mathbf{a}}\left(\mathit{i}\right)$, for
i = 1,2, … ,nnz$\mathit{i}=1,2,\dots ,{\mathbf{nnz}}$. Note that the row indices for a column may be supplied in any order.
Constraint:
1 ≤ ha(i) ≤ m$1\le {\mathbf{ha}}\left(\mathit{i}\right)\le {\mathbf{m}}$, for
i = 1,2, … ,nnz$\mathit{i}=1,2,\dots ,{\mathbf{nnz}}$.
 8:
ka(n + 1${\mathbf{n}}+1$) – int64int32nag_int array
ka(j)${\mathbf{ka}}\left(\mathit{j}\right)$ must contain the index in
a of the start of the
j$\mathit{j}$th column, for
j = 1,2, … ,n$\mathit{j}=1,2,\dots ,{\mathbf{n}}$. To specify the
j$j$th column as empty, set
ka(j) = ka(j + 1)${\mathbf{ka}}\left(j\right)={\mathbf{ka}}\left(j+1\right)$. Note that the first and last elements of
ka must be such that
ka(1) = 1${\mathbf{ka}}\left(1\right)=1$ and
ka(n + 1) = nnz + 1${\mathbf{ka}}\left({\mathbf{n}}+1\right)={\mathbf{nnz}}+1$.
Constraints:
 ka(1) = 1${\mathbf{ka}}\left(1\right)=1$;
 ka(j) ≥ 1${\mathbf{ka}}\left(\mathit{j}\right)\ge 1$, for j = 2,3, … ,n$\mathit{j}=2,3,\dots ,{\mathbf{n}}$;
 ka(n + 1) = nnz + 1${\mathbf{ka}}\left({\mathbf{n}}+1\right)={\mathbf{nnz}}+1$;
 0 ≤ ka(j + 1) − ka(j) ≤ m$0\le {\mathbf{ka}}\left(\mathit{j}+1\right){\mathbf{ka}}\left(\mathit{j}\right)\le {\mathbf{m}}$, for j = 1,2, … ,n$\mathit{j}=1,2,\dots ,{\mathbf{n}}$.
 9:
bl(n + m${\mathbf{n}}+{\mathbf{m}}$) – double array
l$l$, the lower bounds for all the variables and general constraints, in the following order. The first
n elements of
bl must contain the bounds on the variables
x$x$, and the next
m elements the bounds for the general linear constraints
Ax$Ax$ (or slacks
s$s$) and the free row (if any). To specify a nonexistent lower bound (i.e.,
l_{j} = − ∞${l}_{j}=\infty $), set
bl(j) ≤ − bigbnd${\mathbf{bl}}\left(j\right)\le \mathit{bigbnd}$, where
bigbnd$\mathit{bigbnd}$ is the value of the optional parameter
Infinite Bound Size (
default value = 10^{20}$\text{default value}={10}^{20}$). To specify the
j$j$th constraint as an
equality, set
bl(j) = bu(j) = β${\mathbf{bl}}\left(j\right)={\mathbf{bu}}\left(j\right)=\beta $, say, where
β < bigbnd$\left\beta \right<\mathit{bigbnd}$. Note that the lower bound corresponding to the free row must be set to
− ∞$\infty $ and stored in
bl(n + iobj)${\mathbf{bl}}\left({\mathbf{n}}+{\mathbf{iobj}}\right)$.
Constraint:
if
iobj > 0${\mathbf{iobj}}>0$,
bl(n + iobj) ≤ − bigbnd${\mathbf{bl}}\left({\mathbf{n}}+{\mathbf{iobj}}\right)\le \mathit{bigbnd}$(See also the description for
bu.)
 10:
bu(n + m${\mathbf{n}}+{\mathbf{m}}$) – double array
u$u$, the upper bounds for all the variables and general constraints, in the following order. The first
n elements of
bl must contain the bounds on the variables
x$x$, and the next
m elements the bounds for the general linear constraints
Ax$Ax$ (or slacks
s$s$) and the free row (if any). To specify a nonexistent upper bound (i.e.,
u_{j} = + ∞${u}_{j}=+\infty $), set
bu(j) ≥ bigbnd${\mathbf{bu}}\left(j\right)\ge \mathit{bigbnd}$. Note that the upper bound corresponding to the free row must be set to
+ ∞$+\infty $ and stored in
bu(n + iobj)${\mathbf{bu}}\left({\mathbf{n}}+{\mathbf{iobj}}\right)$.
Constraints:
 if iobj > 0${\mathbf{iobj}}>0$, bu(n + iobj) ≥ bigbnd${\mathbf{bu}}\left({\mathbf{n}}+{\mathbf{iobj}}\right)\ge \mathit{bigbnd}$;
 bl(j) ≤ bu(j)${\mathbf{bl}}\left(\mathit{j}\right)\le {\mathbf{bu}}\left(\mathit{j}\right)$, for j = 1,2, … ,n + m$\mathit{j}=1,2,\dots ,{\mathbf{n}}+{\mathbf{m}}$;
 if bl(j) = bu(j) = β${\mathbf{bl}}\left(j\right)={\mathbf{bu}}\left(j\right)=\beta $, β < bigbnd$\left\beta \right<\mathit{bigbnd}$.
 11:
start – string (length ≥ 1)
Indicates how a starting basis is to be obtained.
 start = 'C'${\mathbf{start}}=\text{'C'}$
 An internal crash procedure will be used to choose an initial basis matrix B$B$.
 start = 'W'${\mathbf{start}}=\text{'W'}$
 A basis is already defined in istate (probably from a previous call).
Constraint:
start = 'C'${\mathbf{start}}=\text{'C'}$ or
'W'$\text{'W'}$.
 12:
names(5$5$) – cell array of strings
A set of names associated with the socalled MPSX form of the problem.
 names(1)${\mathbf{names}}\left(1\right)$
 Must contain the name for the problem (or be blank).
 names(2)${\mathbf{names}}\left(2\right)$
 Must contain the name for the free row (or be blank).
 names(3)${\mathbf{names}}\left(3\right)$
 Must contain the name for the constraint righthand side (or be blank).
 names(4)${\mathbf{names}}\left(4\right)$
 Must contain the name for the ranges (or be blank).
 names(5)${\mathbf{names}}\left(5\right)$
 Must contain the name for the bounds (or be blank).
(These names are used in the monitoring file output; see
Section [Description of Monitoring Information].)
 13:
crname(nname) – cell array of strings
nname, the dimension of the array, must satisfy the constraint
nname = 1${\mathbf{nname}}=1$ or
n + m${\mathbf{n}}+{\mathbf{m}}$.
The optional column and row names.
If
nname = 1${\mathbf{nname}}=1$,
crname is not referenced and the printed output will use default names for the columns and rows.
If
nname = n + m${\mathbf{nname}}={\mathbf{n}}+{\mathbf{m}}$, the first
n elements must contain the names for the columns and the next
m elements must contain the names for the rows. Note that the name for the free row (if any) must be stored in
crname(n + iobj)${\mathbf{crname}}\left({\mathbf{n}}+{\mathbf{iobj}}\right)$.
 14:
ns – int64int32nag_int scalar
n_{S}${n}_{S}$, the number of superbasics. For QP problems,
ns need not be specified if
start = 'C'${\mathbf{start}}=\text{'C'}$, but must retain its value from a previous call when
start = 'W'${\mathbf{start}}=\text{'W'}$. For FP and LP problems,
ns need not be initialized.
 15:
xs(n + m${\mathbf{n}}+{\mathbf{m}}$) – double array
The initial values of the variables and slacks
(x,s)$(x,s)$. (See the description for
istate.)
 16:
intvar(lintvr) – int64int32nag_int array
Specifies which components of the solution vector
x$x$ are constrained to be integer. Specifically, if
k$k$ elements of
x$x$ are required to take integer values then
intvar(i) = l_{i}${\mathbf{intvar}}\left(\mathit{i}\right)={l}_{\mathit{i}}$, for
i = 1,2, … ,k$\mathit{i}=1,2,\dots ,k$, where
l_{i}${l}_{i}$ is the integer index such that
x_{li}${x}_{{l}_{i}}$ is integer. If
k < lintvr$k<{\mathbf{lintvr}}$ then
intvar(k + 1)${\mathbf{intvar}}\left(k+1\right)$ must be set to
− 1$1$ to signal the end of the integer variable indices.
The order in which the indices of those components of
x$x$ required to be integer is presented determines the order in which the subproblems are treated and solved. As such it can be a powerful tool to assist the function in achieving a solution efficiently. The general advice is to enter the important integer variables in the model early in
intvar; secondary or less important variables should be entered near the end of the list. However some experimentation might be required to find the optimal order.
 17:
istate(n + m${\mathbf{n}}+{\mathbf{m}}$) – int64int32nag_int array
If
start = 'C'${\mathbf{start}}=\text{'C'}$, the first
n elements of
istate and
xs must specify the initial states and values, respectively, of the variables
x$x$. (The slacks
s$s$ need not be initialized.) An internal crash procedure is then used to select an initial basis matrix
B$B$. The initial basis matrix will be triangular (neglecting certain small elements in each column). It is chosen from various rows and columns of columns of
(A − I)$(AI)$. Possible values for
istate(j)${\mathbf{istate}}\left(j\right)$ are as follows:
istate(j)${\mathbf{istate}}\left(j\right)$  State of xs(j)${\mathbf{xs}}\left(j\right)$ during crash procedure 
0 or 1$1$  Eligible for the basis 
2  Ignored 
3  Eligible for the basis (given preference over 0$0$ or 1$1$) 
4 or 5$5$  Ignored 
If nothing special is known about the problem, or there is no wish to provide special information, you may set
istate(j) = 0${\mathbf{istate}}\left(\mathit{j}\right)=0$ and
xs(j) = 0.0${\mathbf{xs}}\left(\mathit{j}\right)=0.0$, for
j = 1,2, … ,n$\mathit{j}=1,2,\dots ,{\mathbf{n}}$. All variables will then be eligible for the initial basis. Less trivially, to say that the
j$j$th variable will probably be equal to one of its bounds, set
istate(j) = 4${\mathbf{istate}}\left(j\right)=4$ and
xs(j) = bl(j)${\mathbf{xs}}\left(j\right)={\mathbf{bl}}\left(j\right)$ or
istate(j) = 5${\mathbf{istate}}\left(j\right)=5$ and
xs(j) = bu(j)${\mathbf{xs}}\left(j\right)={\mathbf{bu}}\left(j\right)$ as appropriate.
Following the crash procedure, variables for which
istate(j) = 2${\mathbf{istate}}\left(j\right)=2$ are made superbasic. Other variables not selected for the basis are then made nonbasic at the value
xs(j)${\mathbf{xs}}\left(j\right)$ if
bl(j) ≤ xs(j) ≤ bu(j)${\mathbf{bl}}\left(j\right)\le {\mathbf{xs}}\left(j\right)\le {\mathbf{bu}}\left(j\right)$, or at the value
bl(j)${\mathbf{bl}}\left(j\right)$ or
bu(j)${\mathbf{bu}}\left(j\right)$ closest to
xs(j)${\mathbf{xs}}\left(j\right)$.
If
start = 'W'${\mathbf{start}}=\text{'W'}$,
istate and
xs must specify the initial states and values, respectively, of the variables and slacks
(x,s)$(x,s)$. If
nag_mip_iqp_sparse (h02ce) has been called previously with the same values of
n and
m,
istate already contains satisfactory information.
Constraints:
 if start = 'C'${\mathbf{start}}=\text{'C'}$, 0 ≤ istate(j) ≤ 5$0\le {\mathbf{istate}}\left(\mathit{j}\right)\le 5$, for j = 1,2, … ,n$\mathit{j}=1,2,\dots ,{\mathbf{n}}$;
 if start = 'W'${\mathbf{start}}=\text{'W'}$, 0 ≤ istate(j) ≤ 3$0\le {\mathbf{istate}}\left(\mathit{j}\right)\le 3$, for j = 1,2, … ,n + m$\mathit{j}=1,2,\dots ,{\mathbf{n}}+{\mathbf{m}}$.
 18:
strtgy – int64int32nag_int scalar
Defines the branching strategy adopted by the function.
 strtgy = 0${\mathbf{strtgy}}=0$
 Each subproblem first explored imposes a tighter upper bound on the component of x$x$.
 strtgy = 1${\mathbf{strtgy}}=1$
 Each subproblem first explored imposes a tighter lower bound on the component of x$x$.
 strtgy = 2${\mathbf{strtgy}}=2$
 Each branch explored imposes a tighter upper bound on the component of x$x$ if its fractional part is less than 0.5$0.5$, otherwise it imposes a tighter lower bound.
 strtgy = 3${\mathbf{strtgy}}=3$
 Random choice is made between first exploring a tighter lower bound or a tighter upper bound subproblem.
Constraint:
strtgy = 0${\mathbf{strtgy}}=0$,
1$1$,
2$2$ or
3$3$.
 19:
leniz – int64int32nag_int scalar
The dimension of the array
iz as declared in the (sub)program from which
nag_mip_iqp_sparse (h02ce) is called.
Constraint:
leniz ≥ 1${\mathbf{leniz}}\ge 1$.
 20:
lenz – int64int32nag_int scalar
The dimension of the array
z as declared in the (sub)program from which
nag_mip_iqp_sparse (h02ce) is called.
Constraint:
lenz ≥ 1${\mathbf{lenz}}\ge 1$.
The amounts of workspace provided (i.e.,
leniz and
lenz) and required (i.e.,
miniz and
minz) are (by default) output on the current advisory message unit (as defined by
nag_file_set_unit_advisory (x04ab)). Since the minimum values of
leniz and
lenz required to start solving the problem are returned in
miniz and
minz, respectively, you may prefer to obtain appropriate values from the output of a preliminary run with
leniz and
lenz set to
1$1$. (
nag_mip_iqp_sparse (h02ce) will then terminate with
ifail = 14${\mathbf{ifail}}={\mathbf{14}}$.)
 21:
monit – function handle or string containing name of mfile
To provide feedback on the progress of the branch and bound process. Additionally
monit provides, via its parameter
halt, the ability to terminate the process. (You might choose to do this when an integer solution is found, rather than search for a better solution.) If you do not require any intermediate output then
monit may be the string
'h02cey'.
[bstval, halt, count] = monit(intfnd, nodes, depth, obj, x, bstval, bstsol, bl, bu, n, halt, count)
Input Parameters
 1:
intfnd – int64int32nag_int scalar
Contains the number of integer solutions obtained so far.
 2:
nodes – int64int32nag_int scalar
Contains the number of nodes (subproblems) solved so far.
 3:
depth – int64int32nag_int scalar
Contains the depth reached in the tree of problems.
 4:
obj – double scalar
Contains the solution value to the subproblem at this node.
 5:
x(n) – double array
Contains the solution vector to the subproblem at this node.
 6:
bstval – double scalar
Contains the value of the objective function corresponding to the best integer solution obtained so far. If no integer solution has been found
bstval contains the largest machine representable number (see
nag_machine_real_largest (x02al)).
 7:
bstsol(n) – double array
Contains the value of the best integer solution obtained so far.
 8:
bl(n) – double array
Contains the current lower bounds on the variables at this point.
 9:
bu(n) – double array
Contains the current upper bounds on the variables at this point.
 10:
n – int64int32nag_int scalar
Contains the number of variables in the minimization problem.
 11:
halt – logical scalar
Will have the value false.
 12:
count – int64int32nag_int scalar
count may be used to save the last value of
intfnd. If a subsequent call of
monit has a value of
intfnd which is greater than
count, then you know that a new integer solution has been found at this node.
Output Parameters
 1:
bstval – double scalar
May be set to a cutoff value, if you are a sophisticated user, as follows. Before an integer solution has been found
bstval will be set by
nag_mip_iqp_sparse (h02ce) to the largest machine representable number (see
nag_machine_real_largest (x02al)). If you know that the solution being sought is a much smaller number, then
bstval may be set to this number as a cutoff value (see
Section [Description]). Beware of setting
bstval too small, since then no integer solutions will be discovered. Also make sure that
bstval is set using a statement of the form
IF (intfnd.EQ.0) bstval = ${\mathbf{bstval}}=\text{}$ cutoff value
on entry to
monit. This statement will not prevent the normal operation of the algorithm when subsequent integer solutions are found. It would be a grievous mistake to unconditionally set
bstval and if you have any doubts whatsoever about the correct use of this parameter then you are strongly recommended to leave it unchanged.
 2:
halt – logical scalar
If
halt is set to
true,
nag_opt_qpconvex1_sparse_solve (e04nk) will be brought to a halt with
ifail exit
− 1$1$. This facility may be useful if you are content with
any integer solution, or with any integer solution that fits certain criteria. Under these circumstances setting
halt = true${\mathbf{halt}}=\mathbf{true}$ can save considerable unnecessary computation.
 3:
count – int64int32nag_int scalar
Optional Input Parameters
 1:
nnz – int64int32nag_int scalar
Default:
The dimension of the arrays
a,
ha. (An error is raised if these dimensions are not equal.)
The number of nonzero elements in A$A$.
Constraint:
1 ≤ nnz ≤ n × m$1\le {\mathbf{nnz}}\le {\mathbf{n}}\times {\mathbf{m}}$.
 2:
nname – int64int32nag_int scalar
Default:
The dimension of the array
crname.
The number of column (i.e., variable) and row names supplied in the array
names.
 nname = 1${\mathbf{nname}}=1$
 There are no names. Default names will be used in the printed output.
 nname = n + m${\mathbf{nname}}={\mathbf{n}}+{\mathbf{m}}$
 All names must be supplied.
Constraint:
nname = 1${\mathbf{nname}}=1$ or
n + m${\mathbf{n}}+{\mathbf{m}}$.
 3:
lintvr – int64int32nag_int scalar
Default:
The dimension of the array
intvar.
k$k$, the number of components of
x$x$ required to be integer. If
k = 0$k=0$, then
lintvr must be set to
1$1$ and
intvar(1)${\mathbf{intvar}}\left(1\right)$ set to
− 1$1$.
 4:
mdepth – int64int32nag_int scalar
Specifies the maximum depth the tree of subproblems may be developed.
Default:
2 × n + 20$2\times {\mathbf{n}}+20$ Constraint:
mdepth > 0${\mathbf{mdepth}}>0$.
Input Parameters Omitted from the MATLAB Interface
 iz z
Output Parameters
 1:
ns – int64int32nag_int scalar
The final number of superbasics. This will be zero for FP and LP problems.
 2:
xs(n + m${\mathbf{n}}+{\mathbf{m}}$) – double array
xs(i)${\mathbf{xs}}\left(\mathit{i}\right)$ contains the final value of
x_{i}${x}_{\mathit{i}}$, for
i = 1,2, … ,n$\mathit{i}=1,2,\dots ,n$.
 3:
istate(n + m${\mathbf{n}}+{\mathbf{m}}$) – int64int32nag_int array
The final states of the variables and slacks
(x,s)$(x,s)$ from the solution of the last subproblem tackled. The significance of each possible value of
istate(j)${\mathbf{istate}}\left(j\right)$ is as follows:
istate(j)${\mathbf{istate}}\left(j\right)$  State of variable j$j$  Normal value of xs(j)${\mathbf{xs}}\left(j\right)$ 
0$0$  Nonbasic  bl(j)${\mathbf{bl}}\left(j\right)$ 
1$1$  Nonbasic  bu(j)${\mathbf{bu}}\left(j\right)$ 
2$2$  Superbasic  Between bl(j)${\mathbf{bl}}\left(j\right)$ and bu(j)${\mathbf{bu}}\left(j\right)$ 
3$3$  Basic  Between bl(j)${\mathbf{bl}}\left(j\right)$ and bu(j)${\mathbf{bu}}\left(j\right)$ 
If
Ninf = 0$\mathtt{Ninf}=0$ (see
Section [Description of the Printed Output]), basic and superbasic variables may be outside their bounds by as much as the value of the optional parameter
Feasibility Tolerance (
default value = max (10^{ − 6},sqrt(ε))$\text{default value}=\mathrm{max}\phantom{\rule{0.125em}{0ex}}({10}^{6},\sqrt{\epsilon})$, where
ε$\epsilon $ is the
machine precision). Note that unless the optional parameter
Scale Option = 0${\mathbf{Scale\; Option}}=0$ (
default value = 2$\text{default value}=2$) is specified, the
Feasibility Tolerance applies to the variables of the scaled problem. In this case, the variables of the original problem may be as much as
0.1$0.1$ outside their bounds, but this is unlikely unless the problem is very badly scaled.
Very occasionally some nonbasic variables may be outside their bounds by as much as the
Feasibility Tolerance, and there may be some nonbasic variables for which
xs(j)${\mathbf{xs}}\left(j\right)$ lies strictly between its bounds.
If
Ninf > 0$\mathtt{Ninf}>0$, some basic and superbasic variables may be outside their bounds by an arbitrary amount (bounded by
Sinf (see
Section [Description of the Printed Output]) if
Scale Option = 0${\mathbf{Scale\; Option}}=0$).
 4:
miniz – int64int32nag_int scalar
The minimum value of
leniz required to start solving the problem. If
ifail = 14${\mathbf{ifail}}={\mathbf{14}}$,
nag_mip_iqp_sparse (h02ce) may be called again with
leniz suitably larger than
miniz. (The bigger the better, since it is not certain how much workspace the basis factors need.)
 5:
minz – int64int32nag_int scalar
The minimum value of
lenz required to start solving the problem. If
ifail = 15${\mathbf{ifail}}={\mathbf{15}}$,
nag_mip_iqp_sparse (h02ce) may be called again with
lenz suitably larger than
minz. (The bigger the better, since it is not certain how much workspace the basis factors need.)
 6:
obj – double scalar
The value of the objective function.
If
Ninf = 0$\mathtt{Ninf}=0$,
obj includes the quadratic objective term
(1/2)x^{T}Hx$\frac{1}{2}{x}^{\mathrm{T}}Hx$ (if any).
If
Ninf > 0$\mathtt{Ninf}>0$,
obj is just the linear objective term
c^{T}x${c}^{\mathrm{T}}x$ (if any). For FP problems,
obj is set to zero.
 7:
clamda(n + m${\mathbf{n}}+{\mathbf{m}}$) – double array
A set of Lagrangemultipliers for the bounds on the variables and the general constraints. More precisely, the first
n elements contain the multipliers (
reduced costs) for the bounds on the variables, and the next
m elements contain the multipliers (
shadow prices) for the general linear constraints.
 8:
ifail – int64int32nag_int scalar
ifail = 0${\mathrm{ifail}}={\mathbf{0}}$ unless the function detects an error (see
[Error Indicators and Warnings]).
Error Indicators and Warnings
Errors or warnings detected by the function:
Cases prefixed with W are classified as warnings and
do not generate an error of type NAG:error_n. See nag_issue_warnings.
 W ifail = − 1${\mathbf{ifail}}=1$
Halted at your request.
 ifail = 0${\mathbf{ifail}}=0$
Successful exit.
 ifail = 1${\mathbf{ifail}}=1$
Input parameter error immediately detected.
 ifail = 2${\mathbf{ifail}}=2$
No integer solution found.
 ifail = 3${\mathbf{ifail}}=3$
 ifail = 4${\mathbf{ifail}}=4$
The problem is unbounded (or badly scaled). The objective function is not bounded below in the feasible region.
 ifail = 5${\mathbf{ifail}}=5$
The problem is infeasible. The general constraints cannot all be satisfied simultaneously to within the value of the optional parameter
Feasibility Tolerance (
default value = max (10^{ − 6},sqrt(ε))$\text{default value}=\mathrm{max}\phantom{\rule{0.125em}{0ex}}({10}^{6},\sqrt{\epsilon})$, where
ε$\epsilon $ is the
machine precision).
 ifail = 6${\mathbf{ifail}}=6$
Too many iterations. The value of the optional parameter
Iteration Limit (
default value = max (50,5(n + m))$\text{default value}=\mathrm{max}\phantom{\rule{0.125em}{0ex}}(50,5(n+m))$) is too small.
 ifail = 7${\mathbf{ifail}}=7$
The reduced Hessian matrix
z^{T}HZ${\mathit{z}}^{\mathrm{T}}HZ$ (see
Section [Definition of the Working Set and Search Direction]) exceeds its assigned dimension. The value of the optional parameter
Superbasics Limit (
default value = min (n_{H} + 1,n)$\text{default value}=\mathrm{min}\phantom{\rule{0.125em}{0ex}}({n}_{H}+1,n)$) is too small.
 ifail = 8${\mathbf{ifail}}=8$
The Hessian matrix
H$H$ appears to be indefinite. Check that
qphx has been coded correctly and that all relevant elements of
Hx$Hx$ have been assigned their correct values.
 ifail = 9${\mathbf{ifail}}=9$
An input parameter is invalid for an internal call to
nag_opt_qpconvex1_sparse_solve (e04nk).
 ifail = 10${\mathbf{ifail}}=10$
Numerical error in trying to satisfy the general constraints. The basis is very illconditioned.
 ifail = 11${\mathbf{ifail}}=11$
Not enough integer workspace for the basis factors. Increase
leniz and rerun
nag_mip_iqp_sparse (h02ce).
 ifail = 12${\mathbf{ifail}}=12$
Not enough real workspace for the basis factors. Increase
lenz and rerun
nag_mip_iqp_sparse (h02ce).
 ifail = 13${\mathbf{ifail}}=13$
The basis is singular after
15$15$ attempts to factorize it (adding slacks where necessary). Either the problem is badly scaled or the value of the optional parameter
LU Factor Tolerance (
default value = 100.0$\text{default value}=100.0$) is too large.
 ifail = 14${\mathbf{ifail}}=14$
Not enough integer workspace to start solving the problem. Increase
leniz to at least
miniz and rerun
nag_mip_iqp_sparse (h02ce).
 ifail = 15${\mathbf{ifail}}=15$
Not enough real workspace to start solving the problem. Increase
lenz to at least
minz and rerun
nag_mip_iqp_sparse (h02ce).
 ifail = 16${\mathbf{ifail}}=16$
An internal error has occurred. Contact NAG with details of your program.
Accuracy
nag_mip_iqp_sparse (h02ce) implements a numerically stable activeset strategy and returns solutions that are as accurate as the condition of the problem warrants on the machine.
Further Comments
This section contains a description of the printed output.
Description of the Printed Output
This section describes the (default) intermediate printout and final printout produced by
nag_mip_iqp_sparse (h02ce). The intermediate printout is a subset of the monitoring information produced by the function at every iteration (see
Section [Description of Monitoring Information]). You can control the level of printed output (see the description of the optional parameter
Print Level in
Section [Description of the Optional s]). Note that the intermediate printout and final printout are produced only if
Print Level ≥ 10${\mathbf{Print\; Level}}\ge 10$ (the default).
The following line of summary output ( < 80$\text{}<80$ characters) is produced at every iteration. In all cases, the values of the quantities printed are those in effect on
completion of the given iteration.
Itn 
is the iteration count.

Step 
is the step taken along the computed search direction. If a constraint is added during the current iteration, Step will be the step to the nearest constraint. When the problem is of type LP, the step can be greater than one during the optimality phase.

Ninf 
is the number of violated constraints (infeasibilities). This will be zero during the optimality phase.

Sinf/Objective 
is the value of the current objective function. If x$x$ is not feasible, Sinf gives a weighted sum of the magnitudes of constraint violations. If x$x$ is feasible, Objective is the value of the objective function. The output line for the final iteration of the feasibility phase (i.e., the first iteration for which Ninf is zero) will give the value of the true objective at the first feasible point. During the optimality phase, the value of the objective function will be nonincreasing. During the feasibility phase, the number of constraint infeasibilities will not increase until either a feasible point is found, or the optimality of the multipliers implies that no feasible point exists. Once optimal multipliers are obtained, the number of infeasibilities can increase, but the sum of infeasibilities will either remain constant or be reduced until the minimum sum of infeasibilities is found.

Norm rg 
is ‖d_{S}‖$\Vert {d}_{S}\Vert $, the Euclidean norm of the reduced gradient (see Section [The Main Iteration]). During the optimality phase, this norm will be approximately zero after a unit step. For FP and LP problems, Norm rg is not printed.

The final printout includes a listing of the status of every variable and constraint.
The following describes the printout for each variable. A full stop (.) is printed for any numerical value that is zero.
Variable 
gives the name of the variable. If nname = 1${\mathbf{nname}}=1$, a default name is assigned to the j$\mathit{j}$th variable, for j = 1,2, … ,n$\mathit{j}=1,2,\dots ,n$. If nname = n + m${\mathbf{nname}}={\mathbf{n}}+{\mathbf{m}}$, the name supplied in crname(j)${\mathbf{crname}}\left(j\right)$ is assigned to the j$j$th variable.

State 
gives the state of the variable (LL if nonbasic on its lower bound, UL if nonbasic on its upper bound, EQ if nonbasic and fixed, FR if nonbasic and strictly between its bounds, BS if basic and SBS if superbasic).
A key is sometimes printed before State to give some additional information about the state of a variable. Note that unless the optional parameter Scale Option = 0${\mathbf{Scale\; Option}}=0$ ( default value = 2$\text{default value}=2$) is specified, the tests for assigning a key are applied to the variables of the scaled problem.
A 
Alternative optimum possible. The variable is nonbasic, but its reduced gradient is essentially zero. This means that if the variable were allowed to start moving away from its bound, there would be no change to the objective function. The values of the other free variables might change, giving a genuine alternative solution. However, if there are any degenerate variables (labelled D), the actual change might prove to be zero, since one of them could encounter a bound immediately. In either case the values of the Lagrangemultipliers might also change.

D 
Degenerate. The variable is basic or superbasic, but it is equal to (or very close to) one of its bounds.

I 
Infeasible. The variable is basic or superbasic and is currently violating one of its bounds by more than the value of the optional parameter Feasibility Tolerance (default value = max (10^{ − 6},sqrt(ε))$\text{default value}=\mathrm{max}\phantom{\rule{0.125em}{0ex}}({10}^{6},\sqrt{\epsilon})$, where ε$\epsilon $ is the machine precision).

N 
Not precisely optimal. The variable is nonbasic or superbasic. If the value of the reduced gradient for the variable exceeds the value of the optional parameter Optimality Tolerance (default value = max (10^{ − 6},sqrt(ε))$\text{default value}=\mathrm{max}\phantom{\rule{0.125em}{0ex}}({10}^{6},\sqrt{\epsilon})$), the solution would not be declared optimal because the reduced gradient for the variable would not be considered negligible.


Value 
is the value of the variable at the final iterate.

Lower Bound 
is the lower bound specified for the variable. None indicates that bl(j) ≤ − bigbnd${\mathbf{bl}}\left(j\right)\le \mathit{bigbnd}$.

Upper Bound 
is the upper bound specified for the variable. None indicates that bu(j) ≥ bigbnd${\mathbf{bu}}\left(j\right)\ge \mathit{bigbnd}$.

Lagr Mult 
is the Lagrangemultiplier for the associated bound. This will be zero if State is FR. If x$x$ is optimal, the multiplier should be nonnegative if State is LL, nonpositive if State is UL, and zero if State is BS or SBS.

Residual 
is the difference between the variable Value and the nearer of its (finite) bounds bl(j)${\mathbf{bl}}\left(j\right)$ and bu(j)${\mathbf{bu}}\left(j\right)$. A blank entry indicates that the associated variable is not bounded (i.e., bl(j) ≤ − bigbnd${\mathbf{bl}}\left(j\right)\le \mathit{bigbnd}$ and bu(j) ≥ bigbnd${\mathbf{bu}}\left(j\right)\ge \mathit{bigbnd}$).

The meaning of the printout for linear constraints is the same as that given above for variables, with ‘variable’ replaced by ‘constraint’,
n$n$ replaced by
m$m$,
crname(j)${\mathbf{crname}}\left(j\right)$ replaced by
crname(n + j)${\mathbf{crname}}\left(n+j\right)$,
bl(j)${\mathbf{bl}}\left(j\right)$ and
bu(j)${\mathbf{bu}}\left(j\right)$ are replaced by
bl(n + j)${\mathbf{bl}}\left(n+j\right)$ and
bu(n + j)${\mathbf{bu}}\left(n+j\right)$ respectively, and with the following change in the heading.
Constrnt 
gives the name of the linear constraint.

Note that movement off a constraint (as opposed to a variable moving away from its bound) can be interpreted as allowing the entry in the Residual column to become positive.
Numerical values are output with a fixed number of digits; they are not guaranteed to be accurate to this precision.
Example
Open in the MATLAB editor:
nag_mip_iqp_sparse_example
function nag_mip_iqp_sparse_example
n = int64(7);
m = int64(8);
iobj = int64(8);
ncolh = int64(7);
a = [0.02; 0.02; 0.03; 1; 0.7; 0.02; 0.15; 200; 0.06; 0.75; 0.03; ...
0.04; 0.05; 0.04; 1; 2000; 0.02; 1; 0.01; 0.08; 0.08; 0.8; 2000; ...
1; 0.12; 0.02; 0.02; 0.75; 0.04; 2000; 0.01; 0.8; 0.02; 1; 0.02; ...
0.06; 0.02; 2000; 1; 0.01; 0.01; 0.97; 0.01; 400; 0.97; 0.03; 1; 400];
ha = [int64(7);5;3;1;6;4;2;8;7;6;5;4;3;2;1;8;2;1;4;3;7;6;8;1;7;3;4; ...
6;2;8;5;6;7;1;2;3;4;8;1;2;3;6;7;8;7;2;1;8];
ka = [int64(1);9;17;24;31;39;45;49];
bl = [0; 0; 400; 100; 0; 0; 0; 2000; 1e25; 1e25; ...
1e25; 1e25; 1500; 250; 1e25];
bu = [200; 2500; 800; 700; 1500; 1e25; 1e25; ...
2000; 60; 100; 40; 30; 1e25; 300; 1e25];
start = 'C';
names = {' '; ' '; ' '; ' '; ' '};
crname = {'...X1...'; '...X2...'; '...X3...'; '...X4...'; '...X5...'; ...
'...X6...'; '...X7...'; '..ROW1..'; '..ROW2..'; '..ROW3..'; ...
'..ROW4..'; '..ROW5..'; '..ROW6..'; '..ROW7..'; '..COST..'};
ns = int64(24641422);
xs = [0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0];
intvar = [int64(2);3;4;5;6;7;1;0;0;0];
istate = zeros(15, 1, 'int64');
strtgy = int64(3);
leniz = int64(100000);
lenz = int64(100000);
nag_mip_iqp_sparse_optstr('Nolist');
nag_mip_iqp_sparse_optstr('Print level = 0');
[nsOut, xsOut, istateOut, miniz, minz, obj, clamda, ifail] = ...
nag_mip_iqp_sparse(n, m, iobj, ncolh, @qphx, a, ha, ka, bl, bu, start, names, ...
crname, ns, xs, intvar, istate, strtgy, leniz, lenz, @monit)
function [hx] = qphx(nstate, ncolh, x)
hx = zeros(ncolh,1);
hx(1) = 2*x(1);
hx(2) = 2*x(2);
hx(3) = 2*(x(3)+x(4));
hx(4) = hx(3);
hx(5) = 2*x(5);
hx(6) = 2*(x(6)+x(7));
hx(7) = hx(6);
function [bstval, halt, count] = monit(intfnd,nodes,depth,obj,x,bstval, ...
bstsol,bl,bu,n,halt,count)
halt = false;
if intfnd == 0
bstval = 1847510;
end
nsOut =
0
xsOut =
1.0e+06 *
0
0.0004
0.0006
0.0002
0.0004
0.0003
0.0002
0.0020
0.0000
0.0001
0.0000
0.0000
0.0015
0.0003
2.9800
istateOut =
0
0
3
0
0
3
3
0
3
3
3
3
0
0
3
miniz =
497
minz =
502
obj =
1.8475e+06
clamda =
1.0e+04 *
0.2812
0.0225
0.0000
0.0157
0.0204
0.0000
0.0000
1.4823
0
0
0
0
1.6400
1.6571
0.0001
ifail =
0
Open in the MATLAB editor:
h02ce_example
function h02ce_example
n = int64(7);
m = int64(8);
iobj = int64(8);
ncolh = int64(7);
a = [0.02; 0.02; 0.03; 1; 0.7; 0.02; 0.15; 200; 0.06; 0.75; 0.03; ...
0.04; 0.05; 0.04; 1; 2000; 0.02; 1; 0.01; 0.08; 0.08; 0.8; 2000; ...
1; 0.12; 0.02; 0.02; 0.75; 0.04; 2000; 0.01; 0.8; 0.02; 1; 0.02; ...
0.06; 0.02; 2000; 1; 0.01; 0.01; 0.97; 0.01; 400; 0.97; 0.03; 1; 400];
ha = [int64(7);5;3;1;6;4;2;8;7;6;5;4;3;2;1;8;2;1;4;3;7;6;8;1;7;3;4; ...
6;2;8;5;6;7;1;2;3;4;8;1;2;3;6;7;8;7;2;1;8];
ka = [int64(1);9;17;24;31;39;45;49];
bl = [0; 0; 400; 100; 0; 0; 0; 2000; 1e25; 1e25; ...
1e25; 1e25; 1500; 250; 1e25];
bu = [200; 2500; 800; 700; 1500; 1e25; 1e25; ...
2000; 60; 100; 40; 30; 1e25; 300; 1e25];
start = 'C';
names = {' '; ' '; ' '; ' '; ' '};
crname = {'...X1...'; '...X2...'; '...X3...'; '...X4...'; '...X5...'; ...
'...X6...'; '...X7...'; '..ROW1..'; '..ROW2..'; '..ROW3..'; ...
'..ROW4..'; '..ROW5..'; '..ROW6..'; '..ROW7..'; '..COST..'};
ns = int64(24641422);
xs = [0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0];
intvar = [int64(2);3;4;5;6;7;1;0;0;0];
istate = zeros(15, 1, 'int64');
strtgy = int64(3);
leniz = int64(100000);
lenz = int64(100000);
h02cg('Nolist');
h02cg('Print level = 0');
[nsOut, xsOut, istateOut, miniz, minz, obj, clamda, ifail] = ...
h02ce(n, m, iobj, ncolh, @qphx, a, ha, ka, bl, bu, start, names, ...
crname, ns, xs, intvar, istate, strtgy, leniz, lenz, @monit)
function [hx] = qphx(nstate, ncolh, x)
hx = zeros(ncolh,1);
hx(1) = 2*x(1);
hx(2) = 2*x(2);
hx(3) = 2*(x(3)+x(4));
hx(4) = hx(3);
hx(5) = 2*x(5);
hx(6) = 2*(x(6)+x(7));
hx(7) = hx(6);
function [bstval, halt, count] = monit(intfnd,nodes,depth,obj,x,bstval, ...
bstsol,bl,bu,n,halt,count)
halt = false;
if intfnd == 0
bstval = 1847510;
end
nsOut =
0
xsOut =
1.0e+06 *
0
0.0004
0.0006
0.0002
0.0004
0.0003
0.0002
0.0020
0.0000
0.0001
0.0000
0.0000
0.0015
0.0003
2.9800
istateOut =
0
0
3
0
0
3
3
0
3
3
3
3
0
0
3
miniz =
497
minz =
502
obj =
1.8475e+06
clamda =
1.0e+04 *
0.2812
0.0225
0.0000
0.0157
0.0204
0.0000
0.0000
1.4823
0
0
0
0
1.6400
1.6571
0.0001
ifail =
0
Note: the remainder of this document is intended for more advanced users. Section [Algorithmic Details] contains a detailed description of the algorithm which may be needed in order to understand Sections [Optional Parameters] and [Description of Monitoring Information]. Section [Optional Parameters] describes the optional parameters which may be set by calls to nag_mip_iqp_sparse_optstr (h02cg). Section [Description of Monitoring Information] describes the quantities which can be requested to monitor the course of the computation.
Algorithmic Details
This section contains a detailed description of the method used by nag_mip_iqp_sparse (h02ce).
Overview
nag_mip_iqp_sparse (h02ce) employs a Branch and Bound technique (see
Section [Description]) based on an inertiacontrolling method to solve the subproblems that maintains a Cholesky factorization of the reduced Hessian (see below). The method is similar to that of
Gill and Murray (1978), and is described in detail by
Gill et al. (1991). Here we briefly summarise the main features of the method. Where possible, explicit reference is made to the names of variables that are parameters of the function or appear in the printed output.
The method used has two distinct phases: finding an initial feasible point by minimizing the sum of infeasibilities (the
feasibility phase), and minimizing the quadratic objective function within the feasible region (the
optimality phase). The computations in both phases are performed by the same functions. The twophase nature of the algorithm is reflected by changing the function being minimized from the sum of infeasibilities (the printed quantity
Sinf; see
Section [Description of Monitoring Information]) to the quadratic objective function (the printed quantity
Objective; see
Section [Description of Monitoring Information]).
In general, an iterative process is required to solve a quadratic program. Given an iterate
(x,s)$(x,s)$ in both the original variables
x$x$ and the slack variables
s$s$, a new iterate
(x,s)$(\stackrel{}{x},\stackrel{}{s})$ is defined by
where the
step length
α$\alpha $ is a nonnegative scalar (the printed quantity
Step; see
Section [Description of Monitoring Information]), and
p$p$ is called the
search direction. (For simplicity, we shall consider a typical iteration and avoid reference to the index of the iteration.) Once an iterate is feasible (i.e., satisfies the constraints), all subsequent iterates remain feasible.
Definition of the Working Set and Search Direction
At each iterate
(x,s)$(x,s)$, a
working set of constraints is defined to be a linearly independent subset of the constraints that are satisfied ‘exactly’ (to within the value of the optional parameter
Feasibility Tolerance; see
Section [Description of the Optional s]). The working set is the current prediction of the constraints that hold with equality at a solution of the LP or QP problem. Let
m_{W}${m}_{W}$ denote the number of constraints in the working set (including bounds), and let
W$W$ denote the associated
m_{W}${m}_{W}$ by
(n + m)$(n+m)$ working set matrix consisting of the
m_{W}${m}_{W}$ gradients of the working set constraints.
The search direction is defined so that constraints in the working set remain
unaltered for any value of the step length. It follows that
p$p$ must satisfy the identity
This characterisation allows
p$p$ to be computed using any
n$n$ by
n_{z}${n}_{\mathit{z}}$ fullrank matrix
Z$Z$ that spans the null space of
W$W$. (Thus,
n_{z} = n − m_{W}${n}_{\mathit{z}}=n{m}_{W}$ and
WZ = 0$WZ=0$.) The null space matrix
Z$Z$ is defined from a sparse
LU$LU$ factorization of part of
W$W$ (see
(6) and
(7) below). The direction
p$p$ will satisfy
(3) if
where
p_{z}${p}_{\mathit{z}}$ is any
n_{z}${n}_{\mathit{z}}$vector.
The working set contains the constraints Ax − s = 0$Axs=0$ and a subset of the upper and lower bounds on the variables (x,s)$(x,s)$. Since the gradient of a bound constraint x_{j} ≥ l_{j}${x}_{j}\ge {l}_{j}$ or x_{j} ≤ u_{j}${x}_{j}\le {u}_{j}$ is a vector of all zeros except for ± 1$\pm 1$ in position j$j$, it follows that the working set matrix contains the rows of (A − I)$(AI)$ and the unit rows associated with the upper and lower bounds in the working set.
The working set matrix
W$W$ can be represented in terms of a certain column partition of the matrix
(A − I)$(AI)$. As in
Section [Description] we partition the constraints
Ax − s = 0$Axs=0$ so that
where
B$B$ is a square nonsingular basis and
x_{B}${x}_{B}$,
x_{S}${x}_{S}$ and
x_{n}${x}_{{\mathbf{n}}}$ are the basic, superbasic and nonbasic variables respectively. The nonbasic variables are equal to their upper or lower bounds at
(x,s)$(x,s)$, and the superbasic variables are independent variables that are chosen to improve the value of the current objective function. The number of superbasic variables is
n_{S}${n}_{S}$ (the printed quantity
Ns; see
Section [Description of Monitoring Information]). Given values of
x_{N}${x}_{N}$ and
x_{S}${x}_{S}$, the basic variables
x_{B}${x}_{B}$ are adjusted so that
(x,s)$(x,s)$ satisfies
(5).
If
P$P$ is a permutation matrix such that
(A − I)P = (B S N)$(AI)P=\left(B\text{\hspace{1em}}S\text{\hspace{1em}}N\right)$, then the working set matrix
W$W$ satisfies
where
I_{N}${I}_{N}$ is the identity matrix with the same number of columns as
N$N$.
The null space matrix
Z$Z$ is defined from a sparse
LU$LU$ factorization of part of
W$W$. In particular,
z$\mathit{z}$ is maintained in ‘reduced gradient’ form, using the LUSOL package (see
Gill et al. (1986)) to maintain sparse
LU$LU$ factors of the basis matrix
B$B$ that alters as the working set
W$W$ changes. Given the permutation
P$P$, the null space basis is given by
This matrix is used only as an operator, i.e., it is never computed explicitly. Products of the form
Zv$Zv$ and
Z^{T}g${Z}^{\mathrm{T}}g$ are obtained by solving with
B$B$ or
B^{T}${B}^{\mathrm{T}}$. This choice of
Z$Z$ implies that
n_{Z}${n}_{Z}$, the number of ‘degrees of freedom’ at
(x,s)$(x,s)$, is the same as
n_{S}${n}_{S}$, the number of superbasic variables.
Let
g_{Z}${g}_{Z}$ and
H_{Z}${H}_{Z}$ denote the
reduced gradient and
reduced Hessian of the objective function:
where
g$g$ is the objective gradient at
(x,s)$(x,s)$. Roughly speaking,
g_{z}${g}_{\mathit{z}}$ and
H_{z}${H}_{\mathit{z}}$ describe the first and second derivatives of an
n_{S}${n}_{S}$dimensional
unconstrained problem for the calculation of
p_{Z}${p}_{Z}$. (The condition estimator of
H_{Z}${H}_{Z}$ is the quantity
Cond Hz in the monitoring file output; see
Section [Description of Monitoring Information].)
At each iteration, an upper triangular factor R$R$ is available such that H_{Z} = R^{T}R${H}_{Z}={R}^{\mathrm{T}}R$. Normally, R$R$ is computed from R^{T}R = Z^{T}HZ${R}^{\mathrm{T}}R={Z}^{\mathrm{T}}HZ$ at the start of the optimality phase and then updated as the QP working set changes. For efficiency, the dimension of R$R$ should not be excessive (say, n_{S} ≤ 1000${n}_{S}\le 1000$). This is guaranteed if the number of nonlinear variables is ‘moderate’.
If the QP problem contains linear variables,
H$H$ is positive semidefinite and
R$R$ may be singular with at least one zero diagonal element. However, an inertiacontrolling strategy is used to ensure that only the last diagonal element of
R$R$ can be zero. (See
Gill et al. (1991) for a discussion of a similar strategy for indefinite quadratic programming.)
If the initial R$R$ is singular, enough variables are fixed at their current value to give a nonsingular R$R$. This is equivalent to including temporary bound constraints in the working set. Thereafter, R$R$ can become singular only when a constraint is deleted from the working set (in which case no further constraints are deleted until R$R$ becomes nonsingular).
The Main Iteration
If the reduced gradient is zero,
(x,s)$(x,s)$ is a constrained stationary point on the working set. During the feasibility phase, the reduced gradient will usually be zero only at a vertex (although it may be zero elsewhere in the presence of constraint dependencies). During the optimality phase, a zero reduced gradient implies that
x$x$ minimizes the quadratic objective function when the constraints in the working set are treated as equalities. At a constrained stationary point, Lagrangemultipliers
λ$\lambda $ are defined from the equations
A Lagrangemultiplier
λ_{j}${\lambda}_{j}$ corresponding to an inequality constraint in the working set is said to be
optimal if
λ_{j} ≤ σ${\lambda}_{j}\le \sigma $ when the associated constraint is at its
upper bound, or if
λ_{j} ≥ − σ${\lambda}_{j}\ge \sigma $ when the associated constraint is at its
lower bound, where
σ$\sigma $ depends on the value of the optional parameter
Optimality Tolerance (see
Section [Description of the Optional s]). If a multiplier is nonoptimal, the objective function (either the true objective or the sum of infeasibilities) can be reduced by continuing the minimization with the corresponding constraint excluded from the working set. (This step is sometimes referred to as ‘deleting’ a constraint from the working set.) If optimal multipliers occur during the feasibility phase but the sum of infeasibilities is nonzero, there is no feasible point and the function terminates immediately with
ifail = 3${\mathbf{ifail}}={\mathbf{3}}$ (see
Section [Error Indicators and Warnings]).
The special form
(6) of the working set allows the multiplier vector
λ$\lambda $, the solution of
(9), to be written in terms of the vector
where
π$\pi $ satisfies the equations
B^{T}π = g_{B}${B}^{\mathrm{T}}\pi ={g}_{B}$, and
g_{B}${g}_{B}$ denotes the basic elements of
g$g$. The elements of
π$\pi $ are the Lagrangemultipliers
λ_{j}${\lambda}_{j}$ associated with the equality constraints
Ax − s = 0$Axs=0$. The vector
d_{N}${d}_{N}$ of nonbasic elements of
d$d$ consists of the Lagrangemultipliers
λ_{j}${\lambda}_{j}$ associated with the upper and lower bound constraints in the working set. The vector
d_{S}${d}_{S}$ of superbasic elements of
d$d$ is the reduced gradient
g_{z}${g}_{\mathit{z}}$ in
(8). The vector
d_{B}${d}_{B}$ of basic elements of
d$d$ is zero, by construction. (The Euclidean norm of
d_{S}${d}_{S}$ and the final values of
d_{S}${d}_{S}$,
g$g$ and
π$\pi $ are the quantities
Norm rg,
Reduced Gradnt,
Obj Gradient and
Dual Activity in the monitoring file output; see
Section [Description of Monitoring Information].)
If the reduced gradient is not zero, Lagrangemultipliers need not be computed and the search direction is given by
p = Zp_{z}$p=Z{p}_{\mathit{z}}$ (see
(7) and
(11)). The step length is chosen to maintain feasibility with respect to the satisfied constraints.
There are two possible choices for
p_{z}${p}_{\mathit{z}}$, depending on whether or not
H_{z}${H}_{\mathit{z}}$ is singular. If
H_{z}${H}_{\mathit{z}}$ is nonsingular,
R$R$ is nonsingular and
p_{z}${p}_{\mathit{z}}$ in
(4) is computed from the equations
where
g_{z}${g}_{\mathit{z}}$ is the reduced gradient at
x$x$. In this case,
(x,s) + p$(x,s)+p$ is the minimizer of the objective function subject to the working set constraints being treated as equalities. If
(x,s) + p$(x,s)+p$ is feasible,
α$\alpha $ is defined to be unity. In this case, the reduced gradient at
(x,s)$(\stackrel{}{x},\stackrel{}{s})$ will be zero, and Lagrangemultipliers are computed at the next iteration. Otherwise,
α$\alpha $ is set to
α_{m}${\alpha}_{{\mathbf{m}}}$, the step to the ‘nearest’ constraint along
p$p$. This constraint is added to the working set at the next iteration.
If
H_{z}${H}_{\mathit{z}}$ is singular, then
R$R$ must also be singular, and an inertiacontrolling strategy is used to ensure that only the last diagonal element of
R$R$ is zero. (See
Gill et al. (1991) for a discussion of a similar strategy for indefinite quadratic programming.) In this case,
p_{z}${p}_{\mathit{z}}$ satisfies
which allows the objective function to be reduced by any step of the form
(x,s) + αp$(x,s)+\alpha p$, where
α > 0$\alpha >0$. The vector
p = Zp_{Z}$p=Z{p}_{Z}$ is a direction of unbounded descent for the QP problem in the sense that the QP objective is linear and decreases without bound along
p$p$. If no finite step of the form
(x,s) + αp$(x,s)+\alpha p$ (where
α > 0$\alpha >0$) reaches a constraint not in the working set, the QP problem is unbounded and the function terminates immediately with
ifail = 2${\mathbf{ifail}}={\mathbf{2}}$ (see
Section [Error Indicators and Warnings]). Otherwise,
α$\alpha $ is defined as the maximum feasible step along
p$p$ and a constraint active at
(x,s) + αp$(x,s)+\alpha p$ is added to the working set for the next iteration.
Miscellaneous
If the basis matrix is not chosen carefully, the condition of the null space matrix
Z$Z$ in
(7) could be arbitrarily high. To guard against this, the function implements a ‘basis repair’ feature in which the LUSOL package (see
Gill et al. (1986)) is used to compute the rectangular factorization
returning just the permutation
P$P$ that makes
PLP^{T}$PL{P}^{\mathrm{T}}$ unit lower triangular. The pivot tolerance is set to require
PLP^{T}
_{ij} ≤ 2${\leftPL{P}^{\mathrm{T}}\right}_{ij}\le 2$, and the permutation is used to define
P$P$ in
(6). It can be shown that
‖Z‖$\Vert Z\Vert $ is likely to be little more than unity. Hence,
z$\mathit{z}$ should be wellconditioned
regardless of the condition of
W$W$. This feature is applied at the beginning of the optimality phase if a potential
B − S$BS$ ordering is known.
The EXPAND procedure (see
Gill et al. (1989)) is used to reduce the possibility of cycling at a point where the active constraints are nearly linearly dependent. Although there is no absolute guarantee that cycling will not occur, the probability of cycling is extremely small (see
Gill et al. (1986)). The main feature of EXPAND is that the feasibility tolerance is increased at the start of every iteration. This allows a positive step to be taken at every iteration, perhaps at the expense of violating the bounds on
(x,s)$(x,s)$ by a small amount.
Suppose that the value of the optional parameter
Feasibility Tolerance is
δ$\delta $. Over a period of
K$K$ iterations (where
K$K$ is the value of the optional parameter
Expand Frequency; see
Section [Description of the Optional s]), the feasibility tolerance actually used by
nag_mip_iqp_sparse (h02ce) (i.e., the
working feasibility tolerance) increases from
0.5δ$0.5\delta $ to
δ$\delta $ (in steps of
0.5δ / K$0.5\delta /K$).
At certain stages the following ‘resetting procedure’ is used to remove small constraint infeasibilities. First, all nonbasic variables are moved exactly onto their bounds. A count is kept of the number of nontrivial adjustments made. If the count is nonzero, the basic variables are recomputed. Finally, the working feasibility tolerance is reinitialized to 0.5δ$0.5\delta $.
If a problem requires more than K$K$ iterations, the resetting procedure is invoked and a new cycle of iterations is started. (The decision to resume the feasibility phase or optimality phase is based on comparing any constraint infeasibilities with δ$\delta $.)
The resetting procedure is also invoked when nag_mip_iqp_sparse (h02ce) reaches an apparently optimal, infeasible or unbounded solution, unless this situation has already occurred twice. If any nontrivial adjustments are made, iterations are continued.
The EXPAND procedure not only allows a positive step to be taken at every iteration, but also provides a potential choice of constraints to be added to the working set. All constraints at a distance α$\alpha $ (where α ≤ α_{m}$\alpha \le {\alpha}_{{\mathbf{m}}}$) along p$p$ from the current point are then viewed as acceptable candidates for inclusion in the working set. The constraint whose normal makes the largest angle with the search direction is added to the working set. This strategy helps keep the basis matrix B$B$ wellconditioned.
Optional Parameters
Several optional parameters in nag_mip_iqp_sparse (h02ce) define choices in the problem specification or the algorithm logic. In order to reduce the number of formal parameters of nag_mip_iqp_sparse (h02ce) these optional parameters have associated default values that are appropriate for most problems. Therefore, you need only specify those optional parameters whose values are to be different from their default values.
The remainder of this section can be skipped if you wish to use the default values for all optional parameters.
The following is a list of the optional parameters available. A full description of each optional parameter is provided in
Section [Description of the Optional s].
Optional parameters may be specified by calling
nag_mip_iqp_sparse_optstr (h02cg) prior to a call to
nag_mip_iqp_sparse (h02ce).
nag_mip_iqp_sparse_optstr (h02cg) can be called to supply options directly, one call being necessary for each optional parameter. For example,
h02cg('Print Level = 5')
nag_mip_iqp_sparse_optstr (h02cg) should be consulted for a full description of this method of supplying optional parameters.
All optional parameters not specified by you are set to their default values. Optional parameters specified by you are unaltered by nag_mip_iqp_sparse (h02ce) (unless they define invalid values) and so remain in effect for subsequent calls unless altered by you.
Description of the Optional Parameters
For each option, we give a summary line, a description of the optional parameter and details of constraints.
The summary line contains:
 the keywords, where the minimum abbreviation of each keyword is underlined (if no characters of an optional qualifier are underlined, the qualifier may be omitted);
 a parameter value,
where the letters a$a$, i and r$i\text{ and}r$ denote options that take character, integer and real values respectively;
 the default value is used whenever the condition i ≥ 100000000$\lefti\right\ge 100000000$ is satisfied and where the symbol ε$\epsilon $ is a generic notation for machine precision (see nag_machine_precision (x02aj)).
Keywords and character values are case and white space insensitive.
Check Frequency i$i$Default = 60$\text{}=60$Every i$i$th iteration after the most recent basis factorization, a numerical test is made to see if the current solution (x,s)$(x,s)$ satisfies the linear constraints Ax − s = 0$Axs=0$. If the largest element of the residual vector r = Ax − s$r=Axs$ is judged to be too large, the current basis is refactorized and the basic variables recomputed to satisfy the constraints more accurately. If i < 0$i<0$, the default value is used. If i = 0$i=0$, the value i = 99999999$i=99999999$ is used and effectively no checks are made.
Crash Option i$i$Default = 2$\text{}=2$Note that this option does not apply when
start = 'W'${\mathbf{start}}=\text{'W'}$ (see
Section [Parameters]).
If
start = 'C'${\mathbf{start}}=\text{'C'}$, an internal crash procedure is used to select an initial basis from various rows and columns of the constraint matrix
(A − I)$(AI)$. The value of
i$i$ determines which rows and columns are initially eligible for the basis, and how many times the crash procedure is called. If
i = 0$i=0$, the allslack basis
B = − I$B=I$ is chosen. If
i = 1$i=1$, the crash procedure is called once (looking for a triangular basis in all rows and columns of the linear constraint matrix
A$A$). If
i = 2$i=2$, the crash procedure is called twice (looking at any
equality constraints first followed by any
inequality constraints). If
i < 0$i<0$ or
i > 2$i>2$, the default value is used.
If i = 1 or 2$i=1\text{ or}2$, certain slacks on inequality rows are selected for the basis first. (If i = 2$i=2$, numerical values are used to exclude slacks that are close to a bound.) The crash procedure then makes several passes through the columns of A$A$, searching for a basis matrix that is essentially triangular. A column is assigned to ‘pivot’ on a particular row if the column contains a suitably large element in a row that has not yet been assigned. (The pivot elements ultimately form the diagonals of the triangular basis.) For remaining unassigned rows, slack variables are inserted to complete the basis.
Crash Tolerance r$r$Default = 0.1$\text{}=0.1$This value allows the crash procedure to ignore certain ‘small’ nonzero elements in the constraint matrix A$A$ while searching for a triangular basis. For each column of A$A$, if a_{max}${a}_{\mathrm{max}}$ is the largest element in the column, other nonzeros in that column are ignored if they are less than (or equal to) a_{max} × r${a}_{\mathrm{max}}\times r$.
When r > 0$r>0$, the basis obtained by the crash procedure may not be strictly triangular, but it is likely to be nonsingular and almost triangular. The intention is to obtain a starting basis with more column variables and fewer (arbitrary) slacks. A feasible solution may be reached earlier for some problems. If r < 0$r<0$ or r ≥ 1$r\ge 1$, the default value is used.
Defaults This special keyword may be used to reset all optional parameters to their default values.
Expand Frequency i$i$Default = 10000$\text{}=10000$This option is part of an anticycling procedure (see
Section [Miscellaneous]) designed to allow progress even on highly degenerate problems.
For LP problems, the strategy is to force a positive step at every iteration, at the expense of violating the constraints by a small amount. Suppose that the value of the optional parameter
Feasibility Tolerance is
δ$\delta $. Over a period of
i$i$ iterations, the feasibility tolerance actually used by
nag_mip_iqp_sparse (h02ce) (i.e., the
working feasibility tolerance) increases from
0.5δ$0.5\delta $ to
δ$\delta $ (in steps of
0.5δ / i$0.5\delta /i$).
For QP problems, the same procedure is used for iterations in which there is only one superbasic variable. (Cycling can only occur when the current solution is at a vertex of the feasible region.) Thus, zero steps are allowed if there is more than one superbasic variable, but otherwise positive steps are enforced.
Increasing the value of
i$i$ helps reduce the number of slightly infeasible nonbasic basic variables (most of which are eliminated during the resetting procedure). However, it also diminishes the freedom to choose a large pivot element (see the description of the optional parameter
Pivot Tolerance).
If i < 0$i<0$, the default value is used. If i = 0$i=0$, the value i = 99999999$i=99999999$ is used and effectively no anticycling procedure is invoked.
Factorization Frequency i$i$Default = 100$\text{}=100$If
i > 0$i>0$, at most
i$i$ basis changes will occur between factorizations of the basis matrix. For LP problems, the basis factors are usually updated at every iteration. For QP problems, fewer basis updates will occur as the solution is approached. The number of iterations between basis factorizations will therefore increase. During these iterations a test is made regularly according to the value of optional parameter
Check Frequency to ensure that the linear constraints
Ax − s = 0$Axs=0$ are satisfied. If necessary, the basis will be refactorized before the limit of
i$i$ updates is reached. If
i ≤ 0$i\le 0$, the default value is used.
Feasibility Tolerance r$r$Default = max (10^{ − 6},sqrt(ε))$\text{}=\mathrm{max}\phantom{\rule{0.125em}{0ex}}({10}^{6},\sqrt{\epsilon})$If r ≥ ε$r\ge \epsilon $, r$r$ defines the maximum acceptable absolute violation in each constraint at a ‘feasible’ point (including slack variables). For example, if the variables and the coefficients in the linear constraints are of order unity, and the latter are correct to about five decimal digits, it would be appropriate to specify r$r$ as 10^{ − 5}${10}^{5}$. If r < ε$r<\epsilon $, the default value is used.
nag_mip_iqp_sparse (h02ce) attempts to find a feasible solution before optimizing the objective function. If the sum of infeasibilities cannot be reduced to zero, the problem is assumed to be infeasible. Let Sinf be the corresponding sum of infeasibilities. If Sinf is quite small, it may be appropriate to raise r$r$ by a factor of 10$10$ or 100$100$. Otherwise, some error in the data should be suspected. Note that the function does not attempt to find the minimum value of Sinf.
If the constraints and variables have been scaled (see the description of the optional parameter
Scale Option), then feasibility is defined in terms of the scaled problem (since it is more likely to be meaningful).
Infinite Bound Size r$r$Default = 10^{20}$\text{}={10}^{20}$If r > 0$r>0$, r$r$ defines the ‘infinite’ bound bigbnd$\mathit{bigbnd}$ in the definition of the problem constraints. Any upper bound greater than or equal to bigbnd$\mathit{bigbnd}$ will be regarded as + ∞$+\infty $ (and similarly any lower bound less than or equal to − bigbnd$\mathit{bigbnd}$ will be regarded as − ∞$\infty $). If r ≤ 0$r\le 0$, the default value is used.
Infinite Step Size r$r$Default = max (bigbnd,10^{20})$\text{}=\mathrm{max}\phantom{\rule{0.125em}{0ex}}(\mathit{bigbnd},{10}^{20})$If r > 0$r>0$, r$r$ specifies the magnitude of the change in variables that will be considered a step to an unbounded solution. (Note that an unbounded solution can occur only when the Hessian is not positive definite.) If the change in x$x$ during an iteration would exceed the value of r$r$, the objective function is considered to be unbounded below in the feasible region. If r ≤ 0$r\le 0$, the default value is used.
Iteration Limit i$i$Default = max (50,5(n + m))$\text{}=\mathrm{max}\phantom{\rule{0.125em}{0ex}}(50,5(n+m))$Iters Itns The value of
i$i$ specifies the maximum number of iterations allowed before termination. Setting
i = 0$i=0$ and
Print Level > 0${\mathbf{Print\; Level}}>0$ means that the workspace needed to start solving the problem will be computed and printed, but no iterations will be performed. If
i < 0$i<0$, the default value is used.
List DefaultNolist Normally each optional parameter specification is printed as it is supplied. Optional parameter
Nolist may be used to suppress the printing and optional parameter
List may be used to restore printing.
LU Factor Tolerance r_{1}${r}_{1}$Default = 100.0$\text{}=100.0$LU Update Tolerance r_{2}${r}_{2}$Default = 10.0$\text{}=10.0$The values of
r_{1}${r}_{1}$ and
r_{2}${r}_{2}$ affect the stability and sparsity of the basis factorization
B = LU$B=LU$, during refactorization and updates respectively. The lower triangular matrix
L$L$ is a product of matrices of the form
where the multipliers
μ$\mu $ will satisfy
μ ≤ r_{i}$\left\mu \right\le {r}_{i}$. The default values of
r_{1}${r}_{1}$ and
r_{2}${r}_{2}$ usually strike a good compromise between stability and sparsity. For large and relatively dense problems, setting
r_{1}${r}_{1}$ and
r_{2}${r}_{2}$ to
25$25$ (say) may give a marked improvement in sparsity without impairing stability to a serious degree. Note that for band matrices it may be necessary to set
r_{1}${r}_{1}$ in the range
1 ≤ r_{1} < 2$1\le {r}_{1}<2$ in order to achieve stability. If
r_{1} < 1${r}_{1}<1$ or
r_{2} < 1${r}_{2}<1$, the default value is used.
LU Singularity Tolerance r$r$Default = ε^{0.67}$\text{}={\epsilon}^{0.67}$If r > 0$r>0$, r$r$ defines the singularity tolerance used to guard against illconditioned basis matrices. Whenever the basis is refactorized, the diagonal elements of U$U$ are tested as follows. If u_{jj} ≤ r$\left{u}_{jj}\right\le r$ or u_{jj} < r × max_{i} u_{ij}$\left{u}_{jj}\right<r\times {\displaystyle \underset{i}{\mathrm{max}}}\phantom{\rule{0.25em}{0ex}}\left{u}_{ij}\right$, the j$j$th column of the basis is replaced by the corresponding slack variable. If r ≤ 0$r\le 0$, the default value is used.
Minimize DefaultMaximize This option specifies the required direction of the optimization. It applies to both linear and nonlinear terms (if any) in the objective function. Note that if two problems are the same except that one minimizes
f(x)$f\left(x\right)$ and the other maximizes
− f(x)$f\left(x\right)$, their solutions will be the same but the signs of the dual variables
π_{i}${\pi}_{i}$ and the reduced gradients
d_{j}${d}_{j}$ (see
Section [The Main Iteration]) will be reversed.
Monitoring File i$i$Default = − 1$\text{}=1$If
i ≥ 0$i\ge 0$ and
Print Level > 0${\mathbf{Print\; Level}}>0$, monitoring information produced by
nag_mip_iqp_sparse (h02ce) is sent to a file with logical unit number
i$i$. If
i < 0$i<0$ and/or
Print Level = 0${\mathbf{Print\; Level}}=0$, the default value is used and hence no monitoring information is produced.
Optimality Tolerance r$r$Default = max (10^{ − 6},sqrt(ε))$\text{}=\mathrm{max}\phantom{\rule{0.125em}{0ex}}({10}^{6},\sqrt{\epsilon})$If r ≥ ε$r\ge \epsilon $, r$r$ is used to judge the size of the reduced gradients d_{j} = g_{j} − π^{T}a_{j}${d}_{j}={g}_{j}{\pi}^{\mathrm{T}}{a}_{j}$. By definition, the reduced gradients for basic variables are always zero. Optimality is declared if the reduced gradients for any nonbasic variables at their lower or upper bounds satisfy − r × max (1,‖π‖) ≤ d_{j} ≤ r × max (1,‖π‖)$r\times \mathrm{max}\phantom{\rule{0.125em}{0ex}}(1,\Vert \pi \Vert )\le {d}_{j}\le r\times \mathrm{max}\phantom{\rule{0.125em}{0ex}}(1,\Vert \pi \Vert )$, and if d_{j} ≤ r × max (1,‖π‖)$\left{d}_{j}\right\le r\times \mathrm{max}\phantom{\rule{0.125em}{0ex}}(1,\Vert \pi \Vert )$ for any superbasic variables. If r < ε$r<\epsilon $, the default value is used.
Partial Price i$i$Default = 10$\text{}=10$Note that this option does not apply to QP problems.
This option is recommended for large FP or LP problems that have significantly more variables than constraints (i.e., n ≫ m$n\gg m$). It reduces the work required for each pricing operation (i.e., when a nonbasic variable is selected to enter the basis). If i = 1$i=1$, all columns of the constraint matrix (A − I)$(AI)$ are searched. If i > 1$i>1$, A$A$ and − I$I$ are partitioned to give i$i$ roughly equal segments A_{j},K_{j}${A}_{\mathit{j}},{K}_{\mathit{j}}$, for j = 1,2, … ,p$\mathit{j}=1,2,\dots ,p$ (modulo p$p$). If the previous pricing search was successful on A_{j − 1},K_{j − 1}${A}_{j1},{K}_{j1}$, the next search begins on the segments A_{j},K_{j}${A}_{j},{K}_{j}$. If a reduced gradient is found that is larger than some dynamic tolerance, the variable with the largest such reduced gradient (of appropriate sign) is selected to enter the basis. If nothing is found, the search continues on the next segments A_{j + 1},K_{j + 1}${A}_{j+1},{K}_{j+1}$, and so on. If i ≤ 0$i\le 0$, the default value is used.
Pivot Tolerance r$r$Default = ε^{0.67}$\text{}={\epsilon}^{0.67}$If r > 0$r>0$, r$r$ is used to prevent columns entering the basis if they would cause the basis to become almost singular. If r ≤ 0$r\le 0$, the default value is used.
Print Level i$i$Default = 10$\text{}=10$The value of
i$i$ controls the amount of printout produced by
nag_mip_iqp_sparse (h02ce), as indicated below. A detailed description of the printed output is given in
Section [Description of the Printed Output] (summary output at each iteration and the final solution) and
Section [Description of Monitoring Information] (monitoring information at each iteration). Note that the summary output will not exceed
80$80$ characters per line and that the monitoring information will not exceed
120$120$ characters per line. If
i < 0$i<0$, the default value is used. The following printout is sent to the current advisory message unit (as defined by
nag_file_set_unit_advisory (x04ab)):
≥ 0i$\phantom{\ge 0}i$  Output 
≥ 00$\phantom{\ge 0}0$  No output. 
≥ 01$\phantom{\ge 0}1$  The final solution only. 
≥ 05$\phantom{\ge 0}5$  One line of summary output for each iteration (no printout of the final solution). 
≥ 10$\text{}\ge 10$  The final solution and one line of summary output for each iteration. 
The following printout is sent to the logical unit number defined by the
Monitoring File:
≥ 0i$\phantom{\ge 0}i$  Output 
≥ 00$\phantom{\ge 0}0$  No output. 
≥ 01$\phantom{\ge 0}1$  The final solution only. 
≥ 05$\phantom{\ge 0}5$  One long line of output for each iteration (no printout of the final solution). 
≥ 10$\text{}\ge 10$  The final solution and one long line of output for each iteration. 
≥ 20$\text{}\ge 20$  The final solution, one long line of output for each iteration, matrix statistics (initial status of rows and columns, number of elements, density, biggest and smallest elements, etc.), details of the scale factors resulting from the scaling procedure (if Scale Option = 1${\mathbf{Scale\; Option}}=1$ or 2$2$), basis factorization statistics and details of the initial basis resulting from the crash procedure (if start = 'C'${\mathbf{start}}=\text{'C'}$; see Section [Parameters]). 
If
Print Level > 0${\mathbf{Print\; Level}}>0$ and the unit number defined by
Monitoring File is the same as that defined by
nag_file_set_unit_advisory (x04ab), then the summary output is suppressed.
Rank Tolerance r$r$Default = 100ε$\text{}=100\epsilon $Scale Option i$i$Default = 2$\text{}=2$This option enables you to scale the variables and constraints using an iterative procedure due to Fourer (see
Hock and Schittkowski (1981)), which attempts to compute row scales
r_{i}${r}_{i}$ and column scales
c_{j}${c}_{j}$ such that the scaled matrix coefficients
a_{ij} = a_{ij} × (c_{j} / r_{i})${\stackrel{}{a}}_{ij}={a}_{ij}\times ({c}_{j}/{r}_{i})$ are as close as possible to unity. This may improve the overall efficiency of the function on some problems. (The lower and upper bounds on the variables and slacks for the scaled problem are redefined as
l_{j} = l_{j} / c_{j}${\stackrel{}{l}}_{j}={l}_{j}/{c}_{j}$ and
u_{j} = u_{j} / c_{j}${\stackrel{}{u}}_{j}={u}_{j}/{c}_{j}$ respectively, where
c_{j} ≡ r_{j − n}${c}_{j}\equiv {r}_{jn}$ if
j > n$j>n$.)
If i = 0$i=0$, no scaling is performed. If i = 1$i=1$, all rows and columns of the constraint matrix A$A$ are scaled. If i = 2$i=2$, an additional scaling is performed that may be helpful when the solution x$x$ is large; it takes into account columns of (A − I)$(AI)$ that are fixed or have positive lower bounds or negative upper bounds. If i < 0$i<0$ or i > 2$i>2$, the default value is used.
Scale Tolerance r$r$Default = 0.9$\text{}=0.9$Note that this option does not apply when
Scale Option = 0${\mathbf{Scale\; Option}}=0$.
If 0 < r < 1$0<r<1$, r$r$ is used to control the number of scaling passes to be made through the constraint matrix A$A$. At least 3$3$ (and at most 10$10$) passes will be made. More precisely, let a_{p}${a}_{p}$ denote the largest column ratio (i.e.,
('biggest'element)/('smallest'element)
$\frac{\text{'biggest'}\text{element}}{\text{'smallest'}\text{element}}$ in some sense) after the p$p$th scaling pass through A$A$. The scaling procedure is terminated if a_{p} ≥ a_{p − 1} × r${a}_{p}\ge {a}_{p1}\times r$ for some p ≥ 3$p\ge 3$. Thus, increasing the value of r$r$ from 0.9$0.9$ to 0.99$0.99$ (say) will probably increase the number of passes through A$A$. If r ≤ 0$r\le 0$ or r ≥ 1$r\ge 1$, the default value is used.
Superbasics Limit i$i$Default = min (n_{H} + 1,n)$\text{}=\mathrm{min}\phantom{\rule{0.125em}{0ex}}({n}_{H}+1,n)$Note that this option does not apply to FP or LP problems.
The value of i$i$ specifies ‘how nonlinear’ you expect the QP problem to be. If i ≤ 0$i\le 0$, the default value is used.
Description of Monitoring Information
This section describes the intermediate printout and final printout which constitutes the monitoring information produced by
nag_mip_iqp_sparse (h02ce). (See also the description of the optional parameters
Monitoring File and
Print Level in
Section [Description of the Optional s].) You can control the level of printed output.
When
Print Level = 5${\mathbf{Print\; Level}}=5$ or
≥ 10$\text{}\ge 10$ and
Monitoring File ≥ 0${\mathbf{Monitoring\; File}}\ge 0$, the following line of intermediate printout (
< 120$\text{}<120$ characters) is produced at every iteration on the unit number specified by
Monitoring File. Unless stated otherwise, the values of the quantities printed are those in effect
on completion of the given iteration.
Itn 
is the iteration count.

pp 
is the partial price indicator. The variable selected by the last pricing operation came from the ppth partition of A$A$ and − I$I$. Note that pp is reset to zero whenever the basis is refactorized.

dj 
is the value of the reduced gradient (or reduced cost) for the variable selected by the pricing operation at the start of the current iteration.

+S 
is the variable selected by the pricing operation to be added to the superbasic set.

S 
is the variable chosen to leave the superbasic set.

B 
is the variable removed from the basis (if any) to become nonbasic.

B 
is the variable chosen to leave the set of basics (if any) in a special basic ↔ $\leftrightarrow $ superbasic swap. The entry under S has become basic if this entry is nonzero, and nonbasic otherwise. The swap is done to ensure that there are no superbasic slacks.

Step 
is the value of the step length α$\alpha $ taken along the computed search direction p$p$. The variables x$x$ have been changed to x + αp$x+\alpha p$. If a variable is made superbasic during the current iteration (i.e., +S is positive), Step will be the step to the nearest bound. During the optimality phase, the step can be greater than unity only if the reduced Hessian is not positive definite.

Pivot 
is the r$r$th element of a vector y$y$ satisfying By = a_{q}$By={a}_{q}$ whenever a_{q}${a}_{q}$ (the q$q$th column of the constraint matrix (A − I)$(AI)$) replaces the r$r$th column of the basis matrix B$B$. Wherever possible, Step is chosen so as to avoid extremely small values of Pivot (since they may cause the basis to be nearly singular). In extreme cases, it may be necessary to increase the value of the optional parameter Pivot Tolerance (default value = ε^{0.67}$\text{default value}={\epsilon}^{0.67}$, where ε$\epsilon $ is the machine precision) to exclude very small elements of y$y$ from consideration during the computation of Step.

Ninf 
is the number of violated constraints (infeasibilities). This will be zero during the optimality phase.

Sinf/Objective 
is the value of the current objective function. If x$x$ is not feasible, Sinf gives a weighted sum of the magnitudes of constraint violations. If x$x$ is feasible, Objective is the value of the objective function. The output line for the final iteration of the feasibility phase (i.e., the first iteration for which Ninf is zero) will give the value of the true objective at the first feasible point. During the optimality phase, the value of the objective function will be nonincreasing. During the feasibility phase, the number of constraint infeasibilities will not increase until either a feasible point is found, or the optimality of the multipliers implies that no feasible point exists. Once optimal multipliers are obtained, the number of infeasibilities can increase, but the sum of infeasibilities will either remain constant or be reduced until the minimum sum of infeasibilities is found.

L 
is the number of nonzeros in the basis factor L$L$. Immediately after a basis factorization B = LU$B=LU$, this is lenL, the number of subdiagonal elements in the columns of a lower triangular matrix. Further nonzeros are added to L when various columns of B$B$ are later replaced. (Thus, L increases monotonically.)

U 
is the number of nonzeros in the basis factor U$U$. Immediately after a basis factorization, this is lenU, the number of diagonal and superdiagonal elements in the rows of an upper triangular matrix. As columns of B$B$ are replaced, the matrix U$U$ is maintained explicitly (in sparse form). The value of U may fluctuate up or down; in general, it will tend to increase.

Ncp 
is the number of compressions required to recover workspace in the data structure for U$U$. This includes the number of compressions needed during the previous basis factorization. Normally, Ncp should increase very slowly. If it does not, increase lenz by at least L + U$\mathtt{L}+\mathtt{U}$ and rerun nag_mip_iqp_sparse (h02ce) (possibly using start = 'W'${\mathbf{start}}=\text{'W'}$; see Section [Parameters]).

Norm rg 
is ‖d_{S}‖$\Vert {d}_{S}\Vert $, the Euclidean norm of the reduced gradient (see Section [The Main Iteration]). During the optimality phase, this norm will be approximately zero after a unit step. For FP and LP problems, Norm rg is not printed.

Ns 
is the current number of superbasic variables. For FP and LP problems, Ns is not printed.

Cond Hz 
is a lower bound on the condition number of the reduced Hessian (see Section [Definition of the Working Set and Search Direction]). The larger this number, the more difficult the problem. For FP and LP problems, Cond Hz is not printed.

When
Print Level ≥ 20${\mathbf{Print\; Level}}\ge 20$ and
Monitoring File ≥ 0${\mathbf{Monitoring\; File}}\ge 0$, the following lines of intermediate printout (
< 120$\text{}<120$ characters) are produced on the unit number specified by
Monitoring File whenever the matrix
B$B$ or
BS = (BS)${B}_{S}={\left(\begin{array}{cc}B& S\end{array}\right)}^{\mathrm{T}}$ is factorized. Gaussian elimination is used to compute an
LU$LU$ factorization of
B$B$ or
B_{S}${B}_{S}$, where
PLP^{T}$PL{P}^{\mathrm{T}}$ is a lower triangular matrix and
PUQ$PUQ$ is an upper triangular matrix for some permutation matrices
P$P$ and
Q$Q$. The factorization is stabilized in the manner described under the
LU Factor Tolerance (
default value = 100.0$\text{default value}=100.0$; see
Section [Description of the Optional s]).
Factorize 
is the factorization count.

Demand 
is a code giving the reason for the present factorization as follows:
Code 
Meaning 
010$\phantom{01}0$ 
First LU$LU$ factorization. 
011$\phantom{01}1$ 
Number of updates reached the value of the optional parameter Factorization Frequency (default value = 100$\text{default value}=100$). 
012$\phantom{01}2$ 
Excessive nonzeros in updated factors. 
017$\phantom{01}7$ 
Not enough storage to update factors. 
010$\phantom{0}10$ 
Row residuals too large (see the description for the optional parameter Check Frequency). 
011$\phantom{0}11$ 
Illconditioning has caused inconsistent results. 

Iteration 
is the iteration count.

Nonlinear 
is the number of nonlinear variables in B$B$ (not printed if B_{S}${B}_{S}$ is factorized).

Linear 
is the number of linear variables in B$B$ (not printed if B_{S}${B}_{S}$ is factorized).

Slacks 
is the number of slack variables in B$B$ (not printed if B_{S}${B}_{S}$ is factorized).

Elems 
is the number of nonzeros in B$B$ (not printed if B_{S}${B}_{S}$ is factorized).

Density 
is the percentage nonzero density of B$B$ (not printed if B_{S}${B}_{S}$ is factorized). More precisely, Density = 100 × Elems / (Nonlinear + Linear + Slacks)^{2}$\mathtt{Density}=100\times \mathtt{Elems}/{(\mathtt{Nonlinear}+\mathtt{Linear}+\mathtt{Slacks})}^{2}$.

Compressns 
is the number of times the data structure holding the partially factorized matrix needed to be compressed, in order to recover unused workspace. Ideally, it should be zero. If it is more than 3$3$ or 4$4$, increase leniz and lenz and rerun nag_mip_iqp_sparse (h02ce) (possibly using start = 'W'${\mathbf{start}}=\text{'W'}$; see Section [Parameters]).

Merit 
is the average Markowitz merit count for the elements chosen to be the diagonals of PUQ$PUQ$. Each merit count is defined to be (c − 1)(r − 1)$(c1)(r1)$, where c$c$ and r$r$ are the number of nonzeros in the column and row containing the element at the time it is selected to be the next diagonal. Merit is the average of m such quantities. It gives an indication of how much work was required to preserve sparsity during the factorization.

lenL 
is the number of nonzeros in L$L$.

lenU 
is the number of nonzeros in U$U$.

Increase 
is the percentage increase in the number of nonzeros in L$L$ and U$U$ relative to the number of nonzeros in B$B$. More precisely,
Increase = 100 × (lenL + lenU − Elems) / Elems$\mathtt{Increase}=100\times (\mathtt{lenL}+\mathtt{lenU}\mathtt{Elems})/\mathtt{Elems}$.

m 
is the number of rows in the problem. Note that m = Ut + Lt + bp$\mathtt{m}=\mathtt{Ut}+\mathtt{Lt}+\mathtt{bp}$.

Ut 
is the number of triangular rows of B$B$ at the top of U$U$.

d1 
is the number of columns remaining when the density of the basis matrix being factorized reached 0.3$0.3$.

Lmax 
is the maximum subdiagonal element in the columns of L$L$ (not printed if B_{S}${B}_{S}$ is factorized). This will not exceed the value of the LU Factor Tolerance.

Bmax 
is the maximum nonzero element in B$B$ (not printed if B_{S}${B}_{S}$ is factorized).

BSmax 
is the maximum nonzero element in B_{S}${B}_{S}$ (not printed if B$B$ is factorized).

Umax 
is the maximum nonzero element in U$U$, excluding elements of B$B$ that remain in U$U$ unchanged. (For example, if a slack variable is in the basis, the corresponding row of B$B$ will become a row of U$U$ without modification. Elements in such rows will not contribute to Umax. If the basis is strictly triangular, none of the elements of B$B$ will contribute, and Umax will be zero.) Ideally, Umax should not be significantly larger than Bmax. If it is several orders of magnitude larger, it may be advisable to reset the LU Factor Tolerance to a value near 1.0$1.0$. Umax is not printed if B_{S}${B}_{S}$ is factorized.

Umin 
is the magnitude of the smallest diagonal element of PUQ$PUQ$ (not printed if B_{S}${B}_{S}$ is factorized).

Growth 
is the value of the ratio Umax / Bmax$\mathtt{Umax}/\mathtt{Bmax}$, which should not be too large. Providing Lmax is not large (say < 10.0$\text{}<10.0$), the ratio max (Bmax,Umax) / Umin$\mathrm{max}\phantom{\rule{0.125em}{0ex}}(\mathtt{Bmax},\mathtt{Umax})/\mathtt{Umin}$ is an estimate of the condition number of B$B$. If this number is extremely large, the basis is nearly singular and some numerical difficulties could occur in subsequent computations. (However, an effort is made to avoid near singularity by using slacks to replace columns of B$B$ that would have made Umin extremely small, and the modified basis is refactorized.) Growth is not printed if B_{S}${B}_{S}$ is factorized.

Lt 
is the number of triangular columns of B$B$ at the beginning of L$L$.

bp 
is the size of the ‘bump’ or block to be factorized nontrivially after the triangular rows and columns have been removed.

d2 
is the number of columns remaining when the density of the basis matrix being factorized reached 0.6$0.6$.

When
Print Level ≥ 20${\mathbf{Print\; Level}}\ge 20$ and
Monitoring File ≥ 0${\mathbf{Monitoring\; File}}\ge 0$, the following lines of intermediate printout (
< 80$\text{}<80$ characters) are produced on the unit number specified by
Monitoring File whenever
start = 'C'${\mathbf{start}}=\text{'C'}$ (see
Section [Parameters]). They refer to the number of columns selected by the crash procedure during each of several passes through
A$A$, whilst searching for a triangular basis matrix.
Slacks 
is the number of slacks selected initially.

Free cols 
is the number of free columns in the basis.

Preferred 
is the number of ‘preferred’ columns in the basis (i.e., istate(j) = 3${\mathbf{istate}}\left(j\right)=3$ for some j ≤ n$j\le n$).

Unit 
is the number of unit columns in the basis.

Double 
is the number of double columns in the basis.

Triangle 
is the number of triangular columns in the basis.

Pad 
is the number of slacks used to pad the basis.

When
Print Level ≥ 20${\mathbf{Print\; Level}}\ge 20$ and
Monitoring File ≥ 0${\mathbf{Monitoring\; File}}\ge 0$, the following lines of intermediate printout (
< 80$\text{}<80$ characters) are produced on the unit number specified by
Monitoring File. They refer to the elements of the
names array (see
Section [Parameters]).
Name 
gives the name for the problem (blank if none).

Objective 
gives the name of the free row for the problem (blank if none).

RHS 
gives the name of the constraint righthand side for the problem (blank if none).

Ranges 
gives the name of the ranges for the problem (blank if none).

Bounds 
gives the name of the bounds for the problem (blank if none).

When
Print Level = 1${\mathbf{Print\; Level}}=1$ or
≥ 10$\text{}\ge 10$ and
Monitoring File ≥ 0${\mathbf{Monitoring\; File}}\ge 0$, the following lines of final printout (
< 120$\text{}<120$ characters) are produced on the unit number specified by
Monitoring File.
Let a_{j}${a}_{\mathit{j}}$ denote the j$\mathit{j}$th column of A$A$, for j = 1,2, … ,n$\mathit{j}=1,2,\dots ,n$. The following describes the printout for each column (or variable). A full stop (.) is printed for any numerical value that is zero.
Number 
is the column number j$j$. (This is used internally to refer to x_{j}${x}_{j}$ in the intermediate output.)

Column 
gives the name of x_{j}${x}_{j}$.

State 
gives the state of the variable (LL if nonbasic on its lower bound, UL if nonbasic on its upper bound, EQ if nonbasic and fixed, FR if nonbasic and strictly between its bounds, BS if basic and SBS if superbasic).
A key is sometimes printed before State to give some additional information about the state of x_{j}${x}_{j}$. Note that unless the optional parameter Scale Option = 0${\mathbf{Scale\; Option}}=0$ ( default value = 2$\text{default value}=2$) is specified, the tests for assigning a key are applied to the variables of the scaled problem.
A 
Alternative optimum possible. The variable is nonbasic, but its reduced gradient is essentially zero. This means that if the variable were allowed to start moving away from its bound, there would be no change to the objective function. The values of the other free variables might change, giving a genuine alternative solution. However, if there are any degenerate variables (labelled D), the actual change might prove to be zero, since one of them could encounter a bound immediately. In either case the values of the Lagrangemultipliers might also change.

D 
Degenerate. The variable is basic or superbasic, but it is equal to (or very close to) one of its bounds.

I 
Infeasible. The variable is basic or superbasic and is currently violating one of its bounds by more than the value of the optional parameter Feasibility Tolerance (default value = max (10^{ − 6},sqrt(ε))$\text{default value}=\mathrm{max}\phantom{\rule{0.125em}{0ex}}({10}^{6},\sqrt{\epsilon})$, where ε$\epsilon $ is the machine precision).

N 
Not precisely optimal. The variable is nonbasic or superbasic. If the value of the reduced gradient for the variable exceeds the value of the optional parameter Optimality Tolerance (default value = max (10^{ − 6},sqrt(ε))$\text{default value}=\mathrm{max}\phantom{\rule{0.125em}{0ex}}({10}^{6},\sqrt{\epsilon})$), the solution would not be declared optimal because the reduced gradient for the variable would not be considered negligible.


Activity 
is the value of x_{j}${x}_{j}$ at the final iterate.

Obj Gradient 
is the value of g_{j}${g}_{j}$ at the final iterate. For FP problems, g_{j}${g}_{j}$ is set to zero.

Lower Bound 
is the lower bound specified for the variable. None indicates that bl(j) ≤ − bigbnd${\mathbf{bl}}\left(j\right)\le \mathit{bigbnd}$.

Upper Bound 
is the upper bound specified for the variable. None indicates that bu(j) ≥ bigbnd${\mathbf{bu}}\left(j\right)\ge \mathit{bigbnd}$.

Reduced Gradnt 
is the value of d_{j}${d}_{j}$ at the final iterate (see Section [The Main Iteration]). For FP problems, d_{j}${d}_{j}$ is set to zero.

m + j 
is the value of m + j$m+j$.

Let v_{i}${v}_{\mathit{i}}$ denote the i$\mathit{i}$th row of A$A$, for i = 1,2, … ,m$\mathit{i}=1,2,\dots ,m$. The following describes the printout for each row (or constraint). A full stop (.) is printed for any numerical value that is zero.
Number 
is the value of n + i$n+i$. (This is used internally to refer to s_{i}${s}_{i}$ in the intermediate output.)

Row 
gives the name of ν_{i}${\nu}_{i}$.

State 
gives the state of the variable (LL if active on its lower bound, UL if active on its upper bound, EQ if active and fixed, BS if inactive when s_{i}${s}_{i}$ is basic and SBS if inactive when s_{i}${s}_{i}$ is superbasic).
A key is sometimes printed before State to give some additional information about the state of s_{i}${s}_{i}$. Note that unless the optional parameter Scale Option = 0${\mathbf{Scale\; Option}}=0$ ( default value = 2$\text{default value}=2$) is specified, the tests for assigning a key are applied to the variables of the scaled problem.
A 
Alternative optimum possible. The variable is nonbasic, but its reduced gradient is essentially zero. This means that if the variable were allowed to start moving away from its bound, there would be no change to the objective function. The values of the other free variables might change, giving a genuine alternative solution. However, if there are any degenerate variables (labelled D), the actual change might prove to be zero, since one of them could encounter a bound immediately. In either case the values of the Lagrangemultipliers might also change.

D 
Degenerate. The variable is basic or superbasic, but it is equal to (or very close to) one of its bounds.

I 
Infeasible. The variable is basic or superbasic and is currently violating one of its bounds by more than the value of the optional parameter Feasibility Tolerance (default value = max (10^{ − 6},sqrt(ε))$\text{default value}=\mathrm{max}\phantom{\rule{0.125em}{0ex}}({10}^{6},\sqrt{\epsilon})$, where ε$\epsilon $ is the machine precision).

N 
Not precisely optimal. The variable is nonbasic or superbasic. If the value of the reduced gradient for the variable exceeds the value of the optional parameter Optimality Tolerance (default value = max (10^{ − 6},sqrt(ε))$\text{default value}=\mathrm{max}\phantom{\rule{0.125em}{0ex}}({10}^{6},\sqrt{\epsilon})$), the solution would not be declared optimal because the reduced gradient for the variable would not be considered negligible.


Activity 
is the value of v_{i}${v}_{i}$ at the final iterate.

Slack Activity 
is the value by which v_{i}${v}_{i}$ differs from its nearest bound. (For the free row (if any), it is set to Activity.)

Lower Bound 
is the lower bound specified for the variable. None indicates that bl(j) ≤ − bigbnd${\mathbf{bl}}\left(j\right)\le \mathit{bigbnd}$.

Upper Bound 
is the upper bound specified for the variable. None indicates that bu(j) ≥ bigbnd${\mathbf{bu}}\left(j\right)\ge \mathit{bigbnd}$.

i 
gives the index i$i$ of v_{i}${v}_{i}$.

Numerical values are output with a fixed number of digits; they are not guaranteed to be accurate to this precision.
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© The Numerical Algorithms Group Ltd, Oxford, UK. 2009–2013