Optimization
Handle optimization solver properties.
Syntax
model.sol(sname).create(fname,"Optimization")
model.sol(sname).feature(fname).set(pname,value)
model.sol(sname).feature(fname).create(aname,SolverAttribute)
Description
Operation feature. Use this feature to solve PDE-constrained optimization problems. The computed solution object contains the PDE solution evaluated for the optimal set of design variables. When the gradient-evaluation method is analytic, it also returns the adjoint solution.
The Optimization Module includes SNOPT and several other optimization solvers. See the Optimization Module Manual for details.
To add a stationary solver, substitute SolverAttribute above with StationaryAttrib. For a time-dependent solver, replace SolverAttribute with TimeAttrib.
Choosing solver is done with the following property
Table 6-51: Property/Value Pair to select optimization solver
Property
Value
Default
Description
optsolver
bobyqa | cobyla | coordsearch | lm | mma | montecarlo | neldermead | snopt
snopt
Optimization solver
The following table includes general optimization solver properties, which a common to all optimization solvers:
Table 6-52: General Optimization Properties
Property
Value
Default
Description
control
String
user
Name of the controlling study step or "user" if the feature is controlled manually.
keeplog
on | off
off
Keep warnings in stored log.
nsolvemax
positive integer
1000
Maximum number of objective evaluations.
opttol
real scalar
0.001
Tolerance.
plot
on | off
off
Whether to plot while solving.
plotgroup
String
default
Name of plot group for plot while solving.
probes
vector of strings
 
Probes to use if probesel=manual.
probesel
all | none | manual
all
The probes to compute.
When the optimization solver is set to SNOPT (snopt), the following properties are accepted:
Table 6-53: Valid Properties for optsolver snopt
Property
Value
Default
Description
clist
String array
 
Provide values for constants as input parameters using a string array; for the corresponding constant names, use cname.
cname
String array
 
Provide names of constants as input parameters using a string array; for the corresponding constant values, use clist.
feastol
real scalar
1e-6
Linear constraint tolerance.
funcprec
real scalar
3.8e-11
Function precision.
gradientmma
analytic | forward | adjoint | forward_numeric
analytic
Gradient/Jacobian evaluation method for MMA.
gradientsnopt
analytic | numeric
analytic
Gradient/Jacobian evaluation method for SNOPT.
hessupd
integer
10
Hessian updates.
linesearch
derivative | nonderivative
derivative
Use a derivative (gradient) or nonderivative (gradient free) linesearch strategy.
linestol
real scalar
0.9
Linesearch tolerance (a value between 0 and 1). A lower value gives a more accurate search.
majfeastol
real scalar
1e-6
Nonlinear constraint tolerance.
manualhessupd
on | off
off
Whether to use property hessupd.
manualstepcond
on | off
off
Whether to use manual step condition.
message
String
 
The log message from the last solution process.
objcontrib
all | manual
all
Whether to use all objective contributions present or specify manually.
optobj
String
 
Objective function that is minimized when objcontrib=manual.
pdistrib
on | off
off
Distribute parametric sweep.
qpsolver
cholesky | cg | qn
cholesky
QP subproblem algorithm.
stepcond
String
empty
Manual step condition.
The property gradientsnopt is used to control if the gradient should be computed analytically (by solving the adjoint problem) or numerically. If the number of design variables is large, numerical computation of the gradient can be very time consuming. Analytic gradient is only supported when the underlying PDE-problem is stationary.
If manualstepcond is set to on, the expression in the property stepcond is evaluated when new values for the design variables have been computed. If the expression becomes negative, the new values are discarded and the optimization solver reduces the step length in the current line search.
When the optimization solver is set to Levenberg-Marquardt (lm), the following properties are accepted:
Table 6-54: Valid Properties for optsolver lm
Property
Value
Default
Description
gradientlm
numeric
numeric
Gradient/Jacobian evaluation method.
gradorder
first | second
first
Approximation order of the gradient.
lmfact
real scalar
1e-3
Initial Levenberg-Marquardt factor.
When the optimization solver is set to MMA, the following properties are accepted:
Table 6-55: Valid Properties for optsolver MMA
Property
Value
Default
Description
mmamaxiter
positive integer
1
Maximum outer iterations.
mmamaxiteractive
on | off
off
Enable maximum outer iterations.
mmainnmax
positive integer
10
Maximum inner iterations per outer iteration.
mmagepsfactor
real scalar
0.1
Internal tolerance factor.
mmacfactor
real scalar
1000
Constraint penalty factor.
mmaghinit
real scalar
0.5
Initial asymptote factor (ghinit).
mmaghdecr
real scalar
0.7
Decreasing asymptote factor (ghdecr).
mmaghincr
real scalar
1.2
Increasing asymptote factor (ghincr).
mmaasymin
real scalar
0.01
Minimum asymptote level (asymin).
mmaasymax
real scalar
10
Maximum asymptote level (asymax).
mmaalbeta
real scalar
0.1
Bounds asymptote factor (albefa).
mmaxxmove
real scalar
0.5
Bounds control factor (xxmove).
mmaraai
real scalar
0.00001
Approximation increment (raai).
mmaraamin
real scalar
0.000001
Lower approximation bound (raamin).
mmalsq
on | off
on
Automatically transform least-squares objectives to constraints for efficiency.
mmaminmax
on | off
on
Automatically transform minimax and maximin problems to constraints for efficiency.
gcmma
on | off
on
Use the globally convergent MMA algorithm.
For a description of the optimization properties, see Advanced Solver Properties in the Optimization Module User’s Guide.
Table 6-56: Removed femoptim Properties
Property
Reason
Callblevel
Handled by attribute features
Solprop
Handled by stationary or time
Solcomp
Handled by variables
Report
Handled by variables
Out
Solution should be exported