Parallel computing is the technique of using multiple processors on a single problem. The reason to use parallel computing is to speed computations.
The following Optimization Toolbox™ solvers can automatically distribute the numerical estimation of gradients of objective functions and nonlinear constraint functions to multiple processors:
fmincon
fminunc
fgoalattain
fminimax
fsolve
lsqcurvefit
lsqnonlin
These solvers use parallel gradient estimation under the following conditions:
You have a license for Parallel Computing Toolbox™ software.
The option SpecifyObjectiveGradient
is
set to false
, or, if there is a nonlinear constraint
function, the option SpecifyConstraintGradient
is
set to false
. Since false
is
the default value of these options, you don't have to set them; just
don't set them both to true
.
Parallel computing is enabled with parpool
,
a Parallel Computing Toolbox function.
The option UseParallel
is set to true
.
The default value of this option is false
.
When these conditions hold, the solvers compute estimated gradients in parallel.
Note: Even when running in parallel, a solver occasionally calls the objective and nonlinear constraint functions serially on the host machine. Therefore, ensure that your functions have no assumptions about whether they are evaluated in serial or parallel. |
One solver subroutine can compute in parallel automatically: the subroutine that estimates the gradient of the objective function and constraint functions. This calculation involves computing function values at points near the current location x. Essentially, the calculation is
$$\nabla f(x)\approx \left[\frac{f(x+{\Delta}_{1}{e}_{1})-f(x)}{{\Delta}_{1}},\frac{f(x+{\Delta}_{2}{e}_{2})-f(x)}{{\Delta}_{2}},\dots ,\frac{f(x+{\Delta}_{n}{e}_{n})-f(x)}{{\Delta}_{n}}\right],$$
where
f represents objective or constraint functions
e_{i} are the unit direction vectors
Δ_{i} is the size of a step in the e_{i} direction
To estimate ∇f(x) in parallel, Optimization Toolbox solvers distribute the evaluation of (f(x + Δ_{i}e_{i}) – f(x))/Δ_{i} to extra processors.
You can choose to have gradients estimated by central finite differences instead of the default forward finite differences. The basic central finite difference formula is
$$\nabla f(x)\approx \left[\frac{f(x+{\Delta}_{1}{e}_{1})-f(x-{\Delta}_{1}{e}_{1})}{2{\Delta}_{1}},\dots ,\frac{f(x+{\Delta}_{n}{e}_{n})-f(x-{\Delta}_{n}{e}_{n})}{2{\Delta}_{n}}\right].$$
This takes twice as many function evaluations as forward finite differences, but is usually much more accurate. Central finite differences work in parallel exactly the same as forward finite differences.
Enable central finite differences by using optimoptions
to
set the FiniteDifferenceType
option to 'central'
.
To use forward finite differences, set the FiniteDifferenceType
option
to 'forward'
.
Solvers employ the Parallel Computing Toolbox function parfor
to perform parallel estimation
of gradients. parfor
does not work in parallel
when called from within another parfor
loop.
Therefore, you cannot simultaneously use parallel gradient estimation
and parallel functionality within your objective or constraint functions.
Suppose, for example, your objective function userfcn
calls parfor
,
and you wish to call fmincon
in a loop. Suppose
also that the conditions for parallel gradient evaluation of fmincon
,
as given in Parallel Optimization Functionality, are satisfied. When parfor Runs In Parallel shows three
cases:
The outermost loop is parfor
.
Only that loop runs in parallel.
The outermost parfor
loop is
in fmincon
. Only fmincon
runs
in parallel.
The outermost parfor
loop is
in userfcn
. userfcn
can use parfor
in
parallel.
When parfor Runs In Parallel