The function may be called by the names: e04rhc or nag_opt_handle_set_simplebounds.
After the handle has been initialized (e.g., e04rac has been called),
e04rhc may be used to define the variable bounds of the problem. If the bounds have already been defined, they will be overwritten. Individual bounds may also be set by e04tdc.
This will typically be used for problems, such as:
Linear Programming (LP)
Quadratic Programming (QP)
Nonlinear Programming (NLP)
or linear Semidefinite Programming (SDP)
where and are -dimensional vectors. Note that upper and lower bounds are specified for all the variables. This form allows full generality in specifying various types of constraint. In particular, the th variable may be fixed by setting . If certain bounds are not present, the associated elements of or may be set to special values that are treated as or . See the description of the optional parameter which is common among all solvers in the suite. Its value is denoted as further in this text. Note that the bounds are interpreted based on its value at the time of calling this function and any later alterations to will not affect these constraints.
See Section 4.1 in the E04 Chapter Introduction for more details about the NAG optimization modelling suite.
Candes E and Recht B (2009) Exact matrix completion via convex optimization Foundations of Computation Mathematics (Volume 9) 717–772
1: – void *Input
On entry: the handle to the problem. It needs to be initialized (e.g., by e04rac) and must not be changed between calls to the NAG optimization modelling suite.
2: – IntegerInput
On entry: , the current number of decision variables in the model.
3: – const doubleInput
4: – const doubleInput
On entry: , bl and , bu define lower and upper bounds on the variables, respectively. To fix the th variable, set , where . To specify a nonexistent lower bound (i.e., ), set ; to specify a nonexistent upper bound (i.e., ), set .
, for ;
, for ;
, for .
5: – NagError *Input/Output
The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).
6Error Indicators and Warnings
Dynamic memory allocation failed.
See Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
On entry, argument had an illegal value.
On entry, , , .
On entry, , and .
On entry, , , .
The supplied handle does not define a valid handle to the data structure for the NAG optimization modelling suite. It has not been properly initialized or it has been corrupted.
An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
See Section 7.5 in the Introduction to the NAG Library CL Interface for further information.
Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library CL Interface for further information.
The problem cannot be modified right now, the solver is running.
On entry, , expected .
Constraint: nvar must match the current number of variables of the model in the handle.
8Parallelism and Performance
e04rhc is not threaded in any implementation.
Internal changes have been made to this function as follows:
At Mark 26.1: The limitation to specifying model fixed variables ( for some ) where such input returns NE_BOUND has been lifted.
At Mark 27.1: Previously, it was not possible to modify the bounds once they were set or to edit the model once a solver had been called. These limitations have been removed and the associated error codes were removed.
For details of all known issues which have been reported for the NAG Library please refer to the Known Issues.
There is a vast number of problems which can be reformulated as SDP. This example follows Candes and Recht (2009) to show how a rank minimization problem can be approximated by SDP. In addition, it demonstrates how to work with the monitor mode of e04svc.
The problem can be stated as follows: Let's have respondents answering questions where they express their preferences as a number between and or the question can be left unanswered. The task is to fill in the missing entries, i.e., to guess the unexpressed preferences. This problem falls into the category of matrix completion. The idea is to choose the missing entries to minimize the rank of the matrix as it is commonly believed that only a few factors contribute to an individual's tastes or preferences.
Rank minimization is in general NP-hard but it can be approximated by a heuristic, minimizing the nuclear norm of the matrix. The nuclear norm of a matrix is the sum of its singular values. A rank deficient matrix must have (several) zero singular values. Given the fact that the singular values are always non-negative, a minimization of the nuclear norm has the same effect as norm in compress sensing, i.e., it encourages many singular values to be zero and thus it can be considered as a heuristic for the original rank minimization problem.
Let denote the partially filled in matrix with the valid responses on positions. We are looking for of the same size so that the valid responses are unchanged and the nuclear norm (denoted here as ) is minimal.
This is equivalent to
which is the linear semidefinite problem solved in this example, see Candes and Recht (2009) and the references therein for details.
This example has respondents and answers. The obtained answers are
where denotes missing entries ( is used instead in the data file). The obtained matrix has rank and it is shown below printed to -digit accuracy:
The example also turns on monitor mode of e04svc, there is a time limit introduced for the solver which is being checked at the end of every outer iteration. If the time limit is reached, the function is stopped by setting within the monitor step.
See also e04rac for links to further examples in the suite.