## Solver Takes Too Long

Solvers can take excessive time for various reasons. To diagnose the reason or enable faster solution, use one or more of the following techniques.

### Enable Iterative Display

Set the `Display`

option to `'iter'`

.
This setting shows the results of the solver iterations.

To enable iterative display at the MATLAB^{®} command line, enter

options = optimoptions('solvername','Display','iter');

Call the solver using the `options`

structure.

For an example of iterative display, see Interpret Result. For more information, see What to Look For in Iterative Display.

### Use Appropriate Tolerances

Solvers can fail to converge if tolerances are too small, especially `OptimalityTolerance`

and `StepTolerance`

.

To change tolerances at the command line, use `optimoptions`

as
described in Set and Change Options.

### Use a Plot Function

You can obtain more visual or detailed information about solver iterations using a plot function. The Options section of your solver's function reference pages lists the plot functions.

To use a plot function at the MATLAB command line, enter

options = optimoptions('solvername','PlotFcn',{@plotfcn1,@plotfcn2,...});

Call the solver using the `options`

structure.

For an example of using a plot function, see Use a Plot Function.

### Use `'lbfgs' HessianApproximation`

Option

For the `fmincon`

and `fminunc`

solvers, if
you have a problem with many variables (hundreds or more), then oftentimes you can
save time and memory by setting the `HessianApproximation`

option
to `'lbfgs'`

. This causes the `fmincon`

`'interior-point'`

algorithm and `fminunc`

`'quasi-newton'`

algorithm to use a low-memory Hessian
approximation. See Solve Nonlinear Problem with Many Variables.

### Enable CheckGradients

If you have supplied derivatives (gradients or Jacobians) to
your solver, the solver can fail to converge if the derivatives are
inaccurate. For more information about using the `CheckGradients`

option,
see Checking Validity of Gradients or Jacobians.

### Use Inf Instead of a Large, Arbitrary Bound

If you use a large, arbitrary bound (upper or lower), a solver
can take excessive time, or even fail to converge. However, if you
set `Inf`

or `-Inf`

as the bound,
the solver can take less time, and might converge better.

Why? An interior-point algorithm can set an initial point to the midpoint of finite bounds. Or an interior-point algorithm can try to find a “central path” midway between finite bounds. Therefore, a large, arbitrary bound can resize those components inappropriately. In contrast, infinite bounds are ignored for these purposes.

Minor point: Some solvers use memory for each constraint, primarily
via a constraint Hessian. Setting a bound to `Inf`

or `-Inf`

means
there is no constraint, so there is less memory in use, because a
constraint Hessian has lower dimension.

### Use an Output Function

You can obtain detailed information about solver iterations using an output function. Solvers call output functions at each iteration. You write output functions using the syntax described in Output Function and Plot Function Syntax.

For an example of using an output function, see Output Functions for Optimization Toolbox.

### Try Different Algorithm Options

Many solvers have options that can change the solution time, but not in easily
predictable ways. Typically, the `Algorithm`

option has a
significant effect on the solution time.

Other options that affect the solution time include:

`fmincon`

`'interior-point'`

algorithm — Try setting the`BarrierParamUpdate`

option to`'predictor-corrector'`

.`'SubproblemAlgorithm'`

option of the`'trust-region'`

or`'trust-region-reflective'`

algorithm — Try setting`'SubproblemAlgorithm'`

to`'factorization'`

instead of the default`'cg'`

.`coneprog`

— For a large sparse problem, try setting the`LinearSolver`

option to`'prodchol'`

,`'schur'`

, or`'normal'`

. For a dense problem, try setting the`LinearSolver`

option to`'augmented'`

.`quadprog`

`'interior-point-convex'`

algorithm or`lsqlin`

`'interior-point'`

algorithm — Try setting the`LinearSolver`

option to`'sparse'`

or`'dense'`

.

### Use a Sparse Solver or a Multiply Function

Large problems can cause MATLAB to run out of memory or time. Here are some suggestions for using less memory:

Use a large-scale algorithm if possible (see Large-Scale vs. Medium-Scale Algorithms). These algorithms include

`trust-region-reflective`

,`interior-point`

, the`fminunc`

`trust-region`

algorithm, the`fsolve`

`trust-region-dogleg`

algorithm, and the`Levenberg-Marquardt`

algorithm. In contrast, the`active-set`

,`quasi-newton`

, and`sqp`

algorithms are not large-scale.**Tip**If you use a large-scale algorithm, then use sparse matrices for your linear constraints.

Use a Jacobian multiply function or Hessian multiply function. For examples, see Jacobian Multiply Function with Linear Least Squares, Quadratic Minimization with Dense, Structured Hessian, and Minimization with Dense Structured Hessian, Linear Equalities.

### Use Parallel Computing

If you have a Parallel Computing Toolbox™ license, your solver might run faster using parallel computing. For more information, see Parallel Computing.