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Chapter Contents
Chapter Introduction
NAG Toolbox

# NAG Toolbox Chapter IntroductionD02 — Ordinary Differential Equations

## Scope of the Chapter

This chapter is concerned with the numerical solution of ordinary differential equations. There are two main types of problem: those in which all boundary conditions are specified at one point (initial value problems), and those in which the boundary conditions are distributed between two or more points (boundary value problems and eigenvalue problems). Functions are available for initial value problems, two-point boundary value problems and Sturm–Liouville eigenvalue problems.

## Background to the Problems

For most of the functions in this chapter a system of ordinary differential equations must be written in the form
 y1 ′ = f1(x,y1,y2, … ,yn), y2 ′ = f2(x,y1,y2, … ,yn), ⋮ yn ′ = fn(x,y1,y2, … ,yn),
$y1′=f1(x,y1,y2,…,yn), y2′=f2(x,y1,y2,…,yn), ⋮ yn′=fn(x,y1,y2,…,yn),$
that is the system must be given in first-order form. The n$n$ dependent variables (also, the solution) y1,y2,,yn${y}_{1},{y}_{2},\dots ,{y}_{n}$ are functions of the independent variable x$x$, and the differential equations give expressions for the first derivatives yi = (dyi)/(dx) ${y}_{i}^{\prime }=\frac{d{y}_{i}}{dx}$ in terms of x$x$ and y1,y2,,yn${y}_{1},{y}_{2},\dots ,{y}_{n}$. For a system of n$n$ first-order equations, n$n$ associated boundary conditions are usually required to define the solution.
A more general system may contain derivatives of higher order, but such systems can almost always be reduced to the first-order form by introducing new variables. For example, suppose we have the third-order equation
 z ′ ′ ′ + zz ′ ′ + k(l − z′2) = 0. $z′′′+zz′′+k(l-z′2)=0.$
We write y1 = z${y}_{1}=z$, y2 = z${y}_{2}={z}^{\prime }$, y3 = z${y}_{3}={z}^{\prime \prime }$, and the third-order equation may then be written as the system of first-order equations
 y1 ′ = y2 y2 ′ = y3 y3 ′ = − y1y3 − k(l − y22).
$y1′=y2 y2′=y3 y3′=-y1y3-k(l-y22).$
For this system n = 3$n=3$ and we require 3$3$ boundary conditions in order to define the solution. These conditions must specify values of the dependent variables at certain points. For example, we have an initial value problem if the conditions are
 y1 = 0.1  at ​x = 0 y2 = 0.1  at ​x = 0 y3 = 0.1  at ​x = 0.
$y1=0.1 at ​x=0 y2=0.1 at ​x=0 y3=0.1 at ​x=0.$
These conditions would enable us to integrate the equations numerically from the point x = 0$x=0$ to some specified end point. We have a boundary value problem if the conditions are
 y1 = 0  at ​x = 0 y2 = 0  at ​x = 0 y2 = 1  at ​x = 10.
$y1=0 at ​x=0 y2=0 at ​x=0 y2=1 at ​x=10.$
These conditions would be sufficient to define a solution in the range 0x10$0\le x\le 10$, but the problem could not be solved by direct integration (see Section [Boundary Value Problems]). More general boundary conditions are permitted in the boundary value case.
It is sometimes advantageous to solve higher-order systems directly. In particular, there is an initial value function to solve a system of second-order ordinary differential equations of the special form
 y1 ′ ′ = f1(x,y1,y2, … ,yn), y2 ′ ′ = f2(x,y1,y2, … ,yn), ⋮ yn ′ ′ = fn(x,y1,y2, … ,yn).
$y1′′=f1(x,y1,y2,…,yn), y2′′=f2(x,y1,y2,…,yn), ⋮ yn′′=fn(x,y1,y2,…,yn).$
For this second-order system initial values of the derivatives of the dependent variables, yi${y}_{\mathit{i}}^{\prime }$, for i = 1,2,,n$\mathit{i}=1,2,\dots ,n$, are required.
There is also a boundary value function that can treat directly a mixed order system of ordinary differential equations.
There is a broader class of initial value problems known as differential algebraic systems which can be treated. Such a system may be defined as
 y′ = f(x,y,z) 0 = g(x,y,z)
$y′ = f(x,y,z) 0 = g(x,y,z)$
where y$y$ and f$f$ are vectors of length n$n$ and g$g$ and z$z$ are vectors of length m$m$. The functions g$g$ represent the algebraic part of the system.
In addition implicit systems can also be solved, that is systems of the form
 A(x,y)y′ = f(x,y) $A(x,y)y′=f(x,y)$
where A$A$ is a matrix of functions; such a definition can also incorporate algebraic equations. Note that general systems of this form may contain higher-order derivatives and that they can usually be transformed to first-order form, as above.

### Initial Value Problems

To solve first-order systems, initial values of the dependent variables yi${y}_{\mathit{i}}$, for i = 1,2,,n$\mathit{i}=1,2,\dots ,n$, must be supplied at a given point, a$a$. Also a point, b$b$, at which the values of the dependent variables are required, must be specified. The numerical solution is then obtained by a step-by-step calculation which approximates values of the variables yi${y}_{\mathit{i}}$, for i = 1,2,,n$\mathit{i}=1,2,\dots ,n$, at finite intervals over the required range [a,b]$\left[a,b\right]$. The functions in this chapter adjust the step length automatically to meet specified accuracy tolerances. Although the accuracy tests used are reliable over each step individually, in general an accuracy requirement cannot be guaranteed over a long range. For many problems there may be no serious accumulation of error, but for unstable systems small perturbations of the solution will often lead to rapid divergence of the calculated values from the true values. A simple check for stability is to carry out trial calculations with different tolerances; if the results differ appreciably the system is probably unstable. Over a short range, the difficulty may possibly be overcome by taking sufficiently small tolerances, but over a long range it may be better to try to reformulate the problem.
A special class of initial value problems are those for which the solutions contain rapidly decaying transient terms. Such problems are called stiff; an alternative way of describing them is to say that certain eigenvalues of the Jacobian matrix ((fi)/(yj)) $\left(\frac{\partial {f}_{i}}{\partial {y}_{j}}\right)$ have large negative real parts when compared to others. These problems require special methods for efficient numerical solution; the methods designed for non-stiff problems when applied to stiff problems tend to be very slow, because they need small step lengths to avoid numerical instability. A full discussion is given in Hall and Watt (1976) and a discussion of the methods for stiff problems is given in Berzins et al. (1988).

### Boundary Value Problems

In general, a system of nonlinear differential equations with boundary conditions at two or more points cannot be guaranteed to have a solution. The solution, if it exists, has to be determined iteratively. A comprehensive treatment of the numerical solution of boundary value problems can be found in Ascher et al. (1988) and Keller (1992). The methods for this chapter are discussed in Ascher et al. (1979), Ascher and Bader (1987) and Gladwell (1987).

#### Collocation methods

In the collocation method, the solution components are approximated by piecewise polynomials on a mesh. The coefficients of the polynomials form the unknowns to be computed. The approximation to the solution must satisfy the boundary conditions and the differential equations at collocation points in each mesh sub-interval. A modified Newton method is used to solve the nonlinear equations. The mesh is refined by trying to equidistribute the estimated error over the whole interval. An initial estimate of the solution across the mesh is required.

#### Shooting methods

In the shooting method, the unknown boundary values at the initial point are estimated to form an initial value problem, and the equations are then integrated to the final point. At the final point the computed solution and the known boundary conditions should be equal. The condition for equality gives a set of nonlinear equations for the estimated values, which can be solved by Newton's method or one of its variants. The iteration cannot be guaranteed to converge, but it is usually successful if
• the system has a solution,
• the system is not seriously unstable or very stiff for step-by-step solution, and
• good initial estimates can be found for the unknown boundary conditions.
It may be necessary to simplify the problem and carry out some preliminary calculations, in order to obtain suitable starting values. A fuller discussion is given in Chapters 16, 17 and 18 of Hall and Watt (1976), Chapter 11 of Gladwell and Sayers (1980) and Chapter 8 of Gladwell (1979a).

#### Finite difference methods

If a boundary value problem seems insoluble by the above methods and a good estimate for the solution of the problem is known at all points of the range then a finite difference method may be used. Finite difference equations are set up on a mesh of points and estimated values for the solution at the grid points are chosen. Using these estimated values as starting values a Newton iteration is used to solve the finite difference equations. The accuracy of the solution is then improved by deferred corrections or the addition of points to the mesh or a combination of both. The method does not suffer from the difficulties associated with the shooting method but good initial estimates of the solution may be required in some cases and the method is unlikely to be successful when the solution varies very rapidly over short ranges. A discussion is given in Chapters 9 and 11 of Gladwell and Sayers (1980) and Chapter 4 of Gladwell (1979a).

### Chebyshev Collocation for Linear Differential Equations

The collocation method gives a different approach to the solution of ordinary differential equations. It can be applied to problems of either initial value or boundary value type. Suppose the approximate solution is represented in polynomial form, say as a series of Chebyshev polynomials. The coefficients may be determined by matching the series to the boundary conditions, and making it satisfy the differential equation at a number of selected points in the range. The calculation is straightforward for linear differential equations (nonlinear equations may also be solved by an iterative technique based on linearization). The result is a set of Chebyshev coefficients, from which the solution may be evaluated at any point using nag_fit_1dcheb_eval2 (e02ak). A fuller discussion is given in Chapter 24 of Gladwell (1979a) and Chapter 11 of Gladwell and Sayers (1980).
This method can be useful for obtaining approximations to standard mathematical functions. For example, suppose we require values of the Bessel function J(1/3)(x)${J}_{\frac{1}{3}}\left(x\right)$ over the range (0,5)$\left(0,5\right)$, for use in another calculation. We solve the Bessel differential equation by collocation and obtain the Chebyshev coefficients of the solution, which we can use to construct a function for J(1/3)(x)${J}_{\frac{1}{3}}\left(x\right)$. (Note that functions for many common standard functions are already available in Chapter S.)

### Eigenvalue Problems

Sturm–Liouville problems of the form
 (p(x)y′)′ + q (x,λ) y = 0 $( p(x) y′ ) ′ + q (x,λ) y=0$
with appropriate boundary conditions given at two points, can be solved by a Scaled Prüfer method. In this method the differential equation is transformed to another which can be solved for a specified eigenvalue by a shooting method. A discussion is given in Chapter 11 of Gladwell and Sayers (1980) and a complete description is given in Pryce (1986). Some more general eigenvalue problems can be solved by the methods described in Section [Boundary Value Problems].

## Recommendations on Choice and Use of Available Functions

There are no functions which deal directly with complex equations. These may however be transformed to larger systems of real equations of the required form. Split each equation into its real and imaginary parts and solve for the real and imaginary parts of each component of the solution. Whilst this process doubles the size of the system and may not always be appropriate it does make available for use the full range of functions provided presently.

### Initial Value Problems

For stiff systems, that is those which usually contain rapidly decaying transient components, the Backward Differentiation Formula (BDF) variable-order, variable-step codes should be used. The appropriate function in this case is nag_ode_ivp_bdf_zero_simple (d02ej). For more complex tasks where the system residual is difficult to evaluate in direct communication, or is coupled with algebraic equations, there are a collection of functions in sub-chapter D02M–N. These functions can treat implicit differential algebraic systems, they also contain additional methods (beyond BDF techniques) which may be appropriate in some circumstances.
If you are not sure how to classify a problem, you are advised to perform some preliminary calculations with nag_ode_ivp_rkts_range (d02pe), which can indicate whether the system is stiff. We also advise performing some trial calculations with nag_ode_ivp_rkts_range (d02pe) (RK), nag_ode_ivp_adams_zero_simple (d02cj) (Adams) and nag_ode_ivp_bdf_zero_simple (d02ej) (BDF) so as to determine which type of function is best applied to the problem. The conclusions should be based on the computer time used and the number of evaluations of the derivative function fi${f}_{i}$. See Gladwell (1979b) for more details.
For second-order systems of the special form described in Section [Background to the Problems] the Runge–Kutta–Nystrom (RKN) function nag_ode_ivp_2nd_rkn (d02la) should be used.

#### Runge–Kutta functions

The basic RK function is nag_ode_ivp_rkts_onestep (d02pf) which takes one integration step at a time. An alternative is nag_ode_ivp_rkts_range (d02pe), which provides output at user-specified points. The initialization of either nag_ode_ivp_rkts_range (d02pe) or nag_ode_ivp_rkts_onestep (d02pf) and the setting of optional inputs, including choice of method, is made by a call to the setup function nag_ode_ivp_rkts_setup (d02pq). Optional output information about the integration and about error assessment, if selected, can be obtained by calls to the diagnostic functions nag_ode_ivp_rkts_diag (d02pt) and nag_ode_ivp_rkts_errass (d02pu) respectively. nag_ode_ivp_rkts_interp (d02ps) may be used to interpolate on information produced by nag_ode_ivp_rkts_onestep (d02pf) to give solution and derivative values between the integration points. nag_ode_ivp_rkts_reset_tend (d02pr) may be used to reset the end of the integration range whilst integrating using nag_ode_ivp_rkts_onestep (d02pf).
There is a simple driving function nag_ode_ivp_rk_zero_simple (d02bj), which integrates a system over a range and, optionally, computes intermediate output and/or determines the position where a specified function of the solution is zero.
For well-behaved systems with no difficulties such as stiffness or singularities, the Merson form of the RK method, as used by nag_ode_ivp_rkm_val_simple (d02bg), works well for low accuracy calculations. nag_ode_ivp_rkm_zero_simple (d02bh) also uses the Merson form and can additionally find a root of a supplied equation involving solution components.

There is a simple driving function nag_ode_ivp_adams_zero_simple (d02cj), which integrates a system over a range and, optionally, computes intermediate output and/or determines the position where a specified function of the solution is zero.

#### BDF functions

General functions for explicit and implicit ordinary differential equations with a wide range of options for integrator choice and special forms of numerical linear algebra are provided in sub-chapter D02M–N. A separate document describing the use of this sub-chapter is given immediately before the functions of the sub-chapter.
There are three utility functions available for use with sub-chapter D02M–N functions. nag_ode_ivp_stiff_nat_interp (d02xj) and nag_ode_ivp_stiff_c1_interp (d02xk) can be used to interpolate to a solution at a given point using the natural and C1${C}^{1}$ interpolants respectively. nag_ode_ivp_stiff_errest (d02za) can be used to return the weighted norm of the local error estimate calculated by sub-chapter D02M–N functions.
There is a simple driving function nag_ode_ivp_bdf_zero_simple (d02ej), which integrates a system over a range and, optionally, computes intermediate output and/or determines the position where a specified function of the solution is zero. It has a specification similar to the Adams function nag_ode_ivp_adams_zero_simple (d02cj) except that to solve the equations arising in the BDF method an approximation to the Jacobian ((fi)/(yj)) $\left(\frac{\partial {f}_{i}}{\partial {y}_{j}}\right)$ is required. This approximation can be calculated internally but you may supply an analytic expression. In most cases supplying a correct analytic expression will reduce the amount of computer time used.

#### Runge–Kutta–Nystrom functions

The Runge–Kutta–Nystrom function nag_ode_ivp_2nd_rkn (d02la) uses either a low- or high-order method (chosen by you). The choice of method and error control and the setting of optional inputs is made by a call to the setup function nag_ode_ivp_2nd_rkn_setup (d02lx). Optional output information about the integration can be obtained by a call to the diagnostic function nag_ode_ivp_2nd_rkn_diag (d02ly). When the low-order method has been employed nag_ode_ivp_2nd_rkn_interp (d02lz) may be used to interpolate on information produced by nag_ode_ivp_2nd_rkn (d02la) to give the solution and derivative values between the integration points.

### Boundary Value Problems

In general, for a nonlinear system of mixed order with separated boundary conditions, the collocation method (nag_ode_bvp_coll_nlin (d02tk) and its associated functions) can be used. Problems of a more general nature can often be transformed into a suitable form for treatment by nag_ode_bvp_coll_nlin (d02tk), for example nonseparated boundary conditions or problems with unknown parameters (see Section [Further Comments] in (d02tv) for details).
For simple boundary value problems with assigned boundary values you may prefer to use a code based on the shooting method or finite difference method for which there are functions with simple calling sequences (nag_ode_bvp_shoot_bval (d02ha) and nag_ode_bvp_fd_nonlin_fixedbc (d02ga)).
For difficult boundary value problems, where you need to exercise some control over the calculation, and where the collocation method proves unsuccessful, you may wish to try the alternative methods of shooting (nag_ode_bvp_shoot_genpar_algeq (d02sa)) or finite differences (nag_ode_bvp_fd_nonlin_gen (d02ra)).
Note that it is not possible to make a fully automatic boundary value function, and you should be prepared to experiment with different starting values or a different function if the problem is at all difficult.

#### Collocation methods

The collocation function nag_ode_bvp_coll_nlin (d02tk) solves a nonlinear system of mixed order boundary value problems with separated boundary conditions. The initial mesh and accuracy requirements must be specified by a call to the setup function nag_ode_bvp_coll_nlin_setup (d02tv). Optional output information about the final mesh and estimated maximum error can be obtained by a call to the diagnostic function nag_ode_bvp_coll_nlin_diag (d02tz). The solution anywhere on the mesh can be computed by a call to the interpolation function nag_ode_bvp_coll_nlin_interp (d02ty). If nag_ode_bvp_coll_nlin (d02tk) is being used to solve a sequence of related problems then the continuation function nag_ode_bvp_coll_nlin_contin (d02tx) should also be used.

#### Shooting methods

nag_ode_bvp_shoot_bval (d02ha) may be used for simple boundary value problems, where the unknown parameters are the missing boundary conditions. More general boundary value problems may be handled by using nag_ode_bvp_shoot_genpar (d02hb). This function allows for a generalized parameter structure, and is fairly complicated. The older function nag_ode_bvp_shoot_genpar_intern (d02ag) has been retained for use when an interior matching-point is essential; otherwise the newer function nag_ode_bvp_shoot_genpar (d02hb) should be preferred.
For particularly complicated problems where, for example, the parameters must be constrained or the range of integration must be split to enable the shooting method to succeed, the recommended function is nag_ode_bvp_shoot_genpar_algeq (d02sa), which extends the facilities provided by nag_ode_bvp_shoot_genpar (d02hb). If you are an experienced user nag_ode_bvp_shoot_genpar_algeq (d02sa) permits you much more control over the calculation than does nag_ode_bvp_shoot_genpar (d02hb); in particular you are permitted precise control of solution output and intermediate monitoring information.

#### Finite difference methods

nag_ode_bvp_fd_nonlin_fixedbc (d02ga) may be used for simple boundary value problems with assigned boundary values. The calling sequence of nag_ode_bvp_fd_nonlin_fixedbc (d02ga) is very similar to that for nag_ode_bvp_shoot_bval (d02ha) discussed above.
You may find that convergence is difficult to achieve using nag_ode_bvp_fd_nonlin_fixedbc (d02ga) since only specifying the unknown boundary values and the position of the finite difference mesh is permitted. In such cases you may use nag_ode_bvp_fd_nonlin_gen (d02ra), which permits specification of an initial estimate for the solution at all mesh points and allows the calculation to be influenced in other ways too. nag_ode_bvp_fd_nonlin_gen (d02ra) is designed to solve a general nonlinear two-point boundary value problem with nonlinear boundary conditions.
A function, nag_ode_bvp_fd_lin_gen (d02gb), is also supplied specifically for the general linear two-point boundary value problem written in a standard ‘textbook’ form.
You are advised to use interpolation functions from Chapter E01 to obtain solution values at points not on the final mesh.

#### Chebyshev integration method

The Chebyshev integration method is an implementation of the Chebyshev collocation method (see Section [Chebyshev Collocation Method]) which is fully described and compared against other implementations in Muite (2010). nag_ode_bvp_ps_lin_solve (d02ue) solves a linear constant coefficient boundary value problem using the Chebyshev integration formulation on a Chebyshev Gauss–Lobatto grid and solving in the coefficient space. The required Chebyshev Gauss–Lobatto grid points on a given arbitrary interval [a,b]$\left[a,b\right]$ can first be generated using nag_ode_bvp_ps_lin_cgl_grid (d02uc). Then nag_ode_bvp_ps_lin_coeffs (d02ua) obtains the Chebyshev coefficients for the right-hand side (of system) function discretized on the obtained Chebyshev Gauss–Lobatto grid. nag_ode_bvp_ps_lin_solve (d02ue) then solves the problem in Chebyshev coefficient space using the integration formulation. Finally nag_ode_bvp_ps_lin_cgl_vals (d02ub) evaluates the solution (or one of its lower order derivatives) from the set of Chebyshev coefficients returned by nag_ode_bvp_ps_lin_solve (d02ue) on the Chebyshev Gauss–Lobatto grid on [a,b]$\left[a,b\right]$. The set of functions can be used to solve up to fourth order boundary value problems.

### Chebyshev Collocation Method

nag_ode_bvp_coll_nth_comp (d02tg) may be used to obtain the approximate solution of a system of differential equations in the form of a Chebyshev series. The function treats linear differential equations directly, and makes no distinction between initial value and boundary value problems. This function is appropriate for problems where it is known that the solution is smooth and well-behaved over the range, so that each component can be represented by a single polynomial. Singular problems can be solved using nag_ode_bvp_coll_nth_comp (d02tg) as long as their polynomial-like solutions are required.
nag_ode_bvp_coll_nth_comp (d02tg) permits the differential equations to be specified in higher order form; that is without conversion to a first-order system. This type of specification leads to a complicated calling sequence. If you are an inexperienced user two simpler functions are supplied. nag_ode_bvp_coll_nth (d02ja) solves a single regular linear differential equation of any order whereas nag_ode_bvp_coll_sys (d02jb) solves a system of regular linear first-order differential equations.

### Eigenvalue Problems

Two functions, nag_ode_sl2_reg_finite (d02ka) and nag_ode_sl2_breaks_vals (d02kd), may be used to find the eigenvalues of second-order Sturm–Liouville problems. nag_ode_sl2_reg_finite (d02ka) is designed to solve simple problems with regular boundary conditions. nag_ode_sl2_reg_finite (d02ka) calls nag_ode_sl2_breaks_vals (d02kd), which is designed to solve more difficult problems, for example with singular boundary conditions or on infinite ranges or with discontinuous coefficients.
If the eigenfunctions of the Sturm–Liouville problem are also required, nag_ode_sl2_breaks_funs (d02ke) should be used. (nag_ode_sl2_breaks_funs (d02ke) solves the same types of problem as nag_ode_sl2_breaks_vals (d02kd).)

## Decision Trees

### Tree 1: Initial Value Problems

 Is the problem first order? _yes Is the problem known to be stiff? _yes Use functions described in the D02M–N sub-chapter Introduction, or their simple driver . | no| | Backward Differentiation Formula: Use functions described in the D02M–N sub-chapter Introduction, or their simple driver nag_ode_ivp_bdf_zero_simple (d02ej). Adams method with driver function: nag_ode_ivp_adams_zero_simple (d02cj) Adams method with comprehensive suite: nag_ode_ivp_adams_roots (d02qf), nag_ode_ivp_adams_roots_revcom (d02qg), nag_ode_ivp_adams_setup (d02qw), nag_ode_ivp_adams_diag (d02qx), nag_ode_ivp_adams_rootdiag (d02qy) and nag_ode_ivp_adams_interp (d02qz) Runge–Kutta method with driver function: nag_ode_ivp_rk_zero_simple (d02bj) Runge–Kutta method with comprehensive suite: nag_ode_ivp_rkts_range (d02pe), nag_ode_ivp_rkts_onestep (d02pf), nag_ode_ivp_rkts_setup (d02pq), nag_ode_ivp_rkts_reset_tend (d02pr), nag_ode_ivp_rkts_interp (d02ps), nag_ode_ivp_rkts_diag (d02pt) and nag_ode_ivp_rkts_errass (d02pu) no| Is the problem of the form: y ′ ′ = f(x,y)${y}^{\prime \prime }=f\left(x,y\right)$ _yes Use the D02L functions no| Convert to first order problem: is the problem known to be stiff? _yes Use functions described in the D02M–N sub-chapter Introduction, or their simple driver nag_ode_ivp_bdf_zero_simple (d02ej). no| Backward Differentiation Formula: use functions described in the D02M–N sub-chapter Introduction, or their simple driver nag_ode_ivp_bdf_zero_simple (d02ej). Adams method with driver function: nag_ode_ivp_adams_zero_simple (d02cj) Adams method with comprehensive suite: nag_ode_ivp_adams_roots (d02qf), nag_ode_ivp_adams_roots_revcom (d02qg), nag_ode_ivp_adams_setup (d02qw), nag_ode_ivp_adams_diag (d02qx), nag_ode_ivp_adams_rootdiag (d02qy) and nag_ode_ivp_adams_interp (d02qz) Runge–Kutta method with driver function: nag_ode_ivp_rk_zero_simple (d02bj) Runge–Kutta method with comprehensive suite: nag_ode_ivp_rkts_range (d02pe), nag_ode_ivp_rkts_onestep (d02pf), nag_ode_ivp_rkts_setup (d02pq), nag_ode_ivp_rkts_reset_tend (d02pr), nag_ode_ivp_rkts_interp (d02ps), nag_ode_ivp_rkts_diag (d02pt) and nag_ode_ivp_rkts_errass (d02pu)

### Tree 2: Boundary Value Problems

 Is the problem simply of the form y' = f(x,y)${y}^{\text{'}}=f\left(x,y\right)$? _yes Are only boundary values to be determined? _yes Shooting method: nag_ode_bvp_shoot_bval (d02ha) Finite differences: nag_ode_bvp_fd_nonlin_fixedbc (d02ga) Collocation: nag_ode_bvp_coll_nlin (d02tk) | no| | Shooting method: nag_ode_bvp_shoot_genpar (d02hb) Finite differences: nag_ode_bvp_fd_lin_gen (d02gb) Collocation, piecewise polynomials: nag_ode_bvp_coll_nlin (d02tk) Collocation, Chebyshev polynomials: nag_ode_bvp_coll_nth (d02ja), nag_ode_bvp_coll_sys (d02jb) or nag_ode_bvp_ps_lin_solve (d02ue) no| Shooting method: nag_ode_bvp_shoot_genpar_algeq (d02sa) Finite differences: nag_ode_bvp_fd_nonlin_gen (d02ra) Collocation, piecewise polynomials: nag_ode_bvp_coll_nlin (d02tk) Collocation, Chebyshev polynomials: nag_ode_bvp_coll_nth (d02ja), nag_ode_bvp_coll_sys (d02jb) or nag_ode_bvp_ps_lin_solve (d02ue)

## Functionality Index

 Differentiation of a function discretized on Chebyshev Gauss–Lobatto points nag_ode_bvp_ps_lin_cgl_deriv (d02ud)
 Linear constant coefficient boundary value problem,
 Chebyshev spectral integration method,
 Chebyshev coefficients generator for a function discretized on Chebyshev Gauss–Lobatto grid nag_ode_bvp_ps_lin_coeffs (d02ua)
 Chebyshev coefficients to function values on Chebyshev Gauss–Lobatto grid nag_ode_bvp_ps_lin_cgl_vals (d02ub)
 Chebyshev Gauss–Lobatto grid generator nag_ode_bvp_ps_lin_cgl_grid (d02uc)
 Chebyshev integration solver for linear constant coefficient boundary value problem nag_ode_bvp_ps_lin_solve (d02ue)
 Clenshaw–Curtis quadrature weights generator at Chebyshev Gauss–Lobatto points nag_ode_bvp_ps_lin_quad_weights (d02uy)
 Evaluation on uniform grid of function by Barycentric Lagrange interpolation nag_ode_bvp_ps_lin_grid_vals (d02uw)
 value of Kth Chebyshev polynomial nag_ode_bvp_ps_lin_cheb_eval (d02uz)
 Second-order Sturm–Liouville problems,
 regular/singular system, finite/infinite range,
 eigenvalue and eigenfunction nag_ode_sl2_breaks_funs (d02ke)
 eigenvalue only nag_ode_sl2_breaks_vals (d02kd)
 regular system, finite range, user-specified break-points,
 eigenvalue only nag_ode_sl2_reg_finite (d02ka)
 System of first-order ordinary differential equations, initial value problems,
 C1-interpolant nag_ode_ivp_stiff_c1_interp (d02xk)
 comprehensive integrator functions for stiff systems,
 continuation to call nag_ode_dae_dassl_gen (d02ne) nag_ode_dae_dassl_cont (d02mc)
 explicit ordinary differential equations,
 banded Jacobian nag_ode_ivp_stiff_exp_bandjac (d02nc)
 full Jacobian nag_ode_ivp_stiff_exp_fulljac (d02nb)
 sparse Jacobian nag_ode_ivp_stiff_exp_sparjac (d02nd)
 explicit ordinary differential equations (reverse communication):
 full Jacobian nag_ode_ivp_stiff_exp_revcom (d02nm)
 implicit ordinary differential equations coupled with algebraic equations,
 banded Jacobian nag_ode_ivp_stiff_imp_bandjac (d02nh)
 banded Jacobian selector for DASSL integrator nag_ode_dae_dassl_linalg (d02np)
 DASSL integrator nag_ode_dae_dassl_gen (d02ne)
 full Jacobian nag_ode_ivp_stiff_imp_fulljac (d02ng)
 sparse Jacobian nag_ode_ivp_stiff_imp_sparjac (d02nj)
 implicit ordinary differential equations coupled with algebraic equations (reverse communication) nag_ode_ivp_stiff_imp_revcom (d02nn)
 comprehensive integrator functions using Adams' method with root-finding option,
 diagnostic function nag_ode_ivp_adams_diag (d02qx)
 diagnostic function for root-finding nag_ode_ivp_adams_rootdiag (d02qy)
 direct communication nag_ode_ivp_adams_roots (d02qf)
 reverse communication nag_ode_ivp_adams_roots_revcom (d02qg)
 setup function nag_ode_ivp_adams_setup (d02qw)
 comprehensive integrator functions using Runge–Kutta methods,
 diagnostic function nag_ode_ivp_rkts_diag (d02pt)
 diagnostic function for global error assessment nag_ode_ivp_rkts_errass (d02pu)
 interpolant nag_ode_ivp_rkts_interp (d02ps)
 over a range with intermediate output nag_ode_ivp_rkts_range (d02pe)
 over a step nag_ode_ivp_rkts_onestep (d02pf)
 reset end of range nag_ode_ivp_rkts_reset_tend (d02pr)
 setup function nag_ode_ivp_rkts_setup (d02pq)
 compute weighted norm of local error estimate nag_ode_ivp_stiff_errest (d02za)
 enquiry function for use with sparse Jacobian nag_ode_ivp_stiff_sparjac_enq (d02nr)
 integrator diagnostic function nag_ode_ivp_stiff_integ_diag (d02ny)
 integrator setup for backward differentiation formulae method for SPRINT integrator nag_ode_ivp_stiff_bdf (d02nv)
 integrator setup for Blend method for SPRINT integrator nag_ode_ivp_stiff_blend (d02nw)
 integrator setup for DASSL nag_ode_dae_dassl_setup (d02mw)
 integrator setup for DASSL method for SPRINT integrator nag_ode_ivp_stiff_dassl (d02mv)
 linear algebra diagnostic function for sparse Jacobians nag_ode_ivp_stiff_sparjac_diag (d02nx)
 linear algebra setup for banded Jacobians nag_ode_ivp_stiff_bandjac_setup (d02nt)
 linear algebra setup for full Jacobians nag_ode_ivp_stiff_fulljac_setup (d02ns)
 linear algebra setup for sparse Jacobians nag_ode_ivp_stiff_sparjac_setup (d02nu)
 natural interpolant nag_ode_ivp_stiff_interp (d02mz)
 natural interpolant (for use by MONITR subfunction) nag_ode_ivp_stiff_nat_interp (d02xj)
 setup function for continuation calls to integrator nag_ode_ivp_stiff_contin (d02nz)
 simple driver functions,
 Runge–Kutta–Merson method,
 until a function of the solution is zero nag_ode_ivp_rkm_zero_simple (d02bh)
 until a specified component attains a given value nag_ode_ivp_rkm_val_simple (d02bg)
 Runge–Kutta method,
 until (optionally) a function of the solution is zero, with optional intermediate output nag_ode_ivp_rk_zero_simple (d02bj)
 variable-order variable-step Adams' method,
 until (optionally) a function of the solution is zero, with optional intermediate output nag_ode_ivp_adams_zero_simple (d02cj)
 variable-order variable-step backward differentiation formulae method for stiff systems,
 until (optionally) a function of the solution is zero, with optional intermediate output nag_ode_ivp_bdf_zero_simple (d02ej)
 System of ordinary differential equations, boundary value problems,
 collocation and least squares,
 single nth-order linear equation nag_ode_bvp_coll_nth (d02ja)
 system of first-order linear equations nag_ode_bvp_coll_sys (d02jb)
 system of nth-order linear equations nag_ode_bvp_coll_nth_comp (d02tg)
 comprehensive functions using a collocation technique,
 continuation function nag_ode_bvp_coll_nlin_contin (d02tx)
 diagnostic function nag_ode_bvp_coll_nlin_diag (d02tz)
 general nonlinear problem solver nag_ode_bvp_coll_nlin (d02tk)
 interpolation function nag_ode_bvp_coll_nlin_interp (d02ty)
 setup function nag_ode_bvp_coll_nlin_setup (d02tv)
 finite difference technique with deferred correction,
 general linear problem nag_ode_bvp_fd_lin_gen (d02gb)
 general nonlinear problem, with continuation facility nag_ode_bvp_fd_nonlin_gen (d02ra)
 simple nonlinear problem nag_ode_bvp_fd_nonlin_fixedbc (d02ga)
 shooting and matching technique,
 boundary values to be determined nag_ode_bvp_shoot_bval (d02ha)
 general parameters to be determined nag_ode_bvp_shoot_genpar (d02hb)
 general parameters to be determined, allowing interior matching-point nag_ode_bvp_shoot_genpar_intern (d02ag)
 general parameters to be determined, subject to extra algebraic equations nag_ode_bvp_shoot_genpar_algeq (d02sa)
 System of second-order ordinary differential equations,
 Runge–Kutta–Nystrom method,
 diagnostic function nag_ode_ivp_2nd_rkn_diag (d02ly)
 integrator nag_ode_ivp_2nd_rkn (d02la)
 interpolating solutions nag_ode_ivp_2nd_rkn_interp (d02lz)
 setup function nag_ode_ivp_2nd_rkn_setup (d02lx)

## References

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Gladwell I (1979a) The development of the boundary value codes in the ordinary differential equations chapter of the NAG Library Codes for Boundary Value Problems in Ordinary Differential Equations. Lecture Notes in Computer Science (eds B Childs, M Scott, J W Daniel, E Denman and P Nelson) 76 Springer–Verlag
Gladwell I (1979b) Initial value routines in the NAG Library ACM Trans. Math. Software 5 386–400
Gladwell I (1987) The NAG Library boundary value codes Numerical Analysis Report 134 Manchester University
Gladwell I and Sayers D K (ed.) (1980) Computational Techniques for Ordinary Differential Equations Academic Press
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Muite B K (2010) A numerical comparison of Chebyshev methods for solving fourth-order semilinear initial boundary value problems Journal of Computational and Applied Mathematics 234(2) 317–342
Pryce J D (1986) Error estimation for phase-function shooting methods for Sturm–Liouville problems IMA J. Numer. Anal. 6 103–123

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