- Aide de Scilab
- Optimisation et Simulation
- Optimization base
- optimbase_cget
- optimbase_checkbounds
- optimbase_checkcostfun
- optimbase_checkx0
- optimbase_configure
- optimbase_destroy
- optimbase_function
- optimbase_get
- optimbase_hasbounds
- optimbase_hasconstraints
- optimbase_hasnlcons
- optimbase_histget
- optimbase_histset
- optimbase_incriter
- optimbase_isfeasible
- optimbase_isinbounds
- optimbase_isinnonlincons
- optimbase_log
- optimbase_new
- optimbase_outputcmd
- optimbase_outstruct
- overview
- optimbase_proj2bnds
- optimbase_set
- optimbase_stoplog
- optimbase_terminate
Please note that the recommended version of Scilab is 2025.0.0. This page might be outdated.
See the recommended documentation of this function
optimbase_configure
Configures the current object.
Calling Sequence
opt = optimbase_configure(opt, key, value)
Argument
- opt
The current object of TOPTIM type (tlist).
- key
A string.
- value
The value is assigned in function of
key
.
Description
The optimbase_configure
function allows to set the value contained in
the key
.
The following keys are available :
- -verbose
A 1-by-1 matrix of doubles, positive, integer value, set to 1 to enable verbose logging (default
-verbose
= 0).- -verbosetermination
A 1-by-1 matrix of doubles, positive, integer value, set to 1 to enable verbose termination logging (default
-verbosetermination
= 0).- -x0
The initial guess. A n-by-1 matrix of doubles, where n is the number of variables. There is no default value, i.e. the user must provide
-x0
.- -maxfunevals
The maximum number of function evaluations is a 1-by-1 matrix of doubles, positive, integer value (default
-maxfunevals
= 100). If this criteria is triggered, the status of the optimization is set to-maxfunevals
.- -maxiter
The maximum number of iterations is a 1-by-1 matrix of doubles, positive, integer value (default
-maxiter
= 100). If this criteria is triggered, the status of the optimization is set to-maxiter
.- -tolfunabsolute
The absolute tolerance for the function value is a 1-by-1 matrix of doubles, positive (default
-tolfunabsolute
= 0).- -tolfunrelative
The relative tolerance for the function value is a 1-by-1 matrix of doubles, positive (default
-tolfunrealtive
= %eps).- -tolfunmethod
A 1-by-1 matrix of booleans. Set to %t to enable termination with tolerance on function value (default
-tolfunmethod
= %f). If this criteria is triggered, the status of the optimization is set to "tolf".- -tolxabsolute
The absolute tolerance on x is a 1-by-1 matrix of doubles, positive (default
-tolxabsolute
= 0).- -tolxrelative
The relative tolerance on x is a 1-by-1 matrix of doubles, positive (default
-tolxrealtive
= sqrt(%eps)).- -tolxmethod
A 1-by-1 matrix of booleans. Set to %t to enable the tolerance on x in the termination criteria (default
-tolxmethod
= %t). If this criteria is triggered, the status of the optimization is set to "tolx".- -function
A function or a list, the objective function. This function computes the value of the cost and the non linear constraints, if any. There is no default value, i.e. the user must provide
f
.- -outputcommand
A function or a list. The function is called back for output.
- -numberofvariables
The number of variables to optimize is a 1-by-1 matrix of doubles, positive, integer value (default
-numberofvariables
= 0).- -storehistory
A 1-by-1 matrix of doubles, positive, integer value. Set to %t to enable the history storing (default
-storehistory
= %f).- -boundsmin
The minimum bounds for the parameters. A n-by-1 matrix of doubles where n is the number of variables (default
-boundsmin
= [], i.e. there are no minimim bounds).- -boundsmax
The maximum bounds for the parameters. A n-by-1 matrix of doubles where n is the number of variables (default
-boundsmax
= [], i.e. there are no maximum bounds).- -nbineqconst
The number of inequality constraints is a 1-by-1 matrix of doubles, positive, integer value (default
-nbineqconst
= 0).- -logfile
The name of the log file.
- -withderivatives
A 1-by-1 matrix of booleans. Set to %t if the algorithm uses derivatives (default
-withderivatives
= 0).
Example
opt = optimbase_new(); // Set number of variables opt = optimbase_configure ( opt , "-numberofvariables" , 10) // Set initial guess opt = optimbase_configure(opt, "-x0", [-1.2 1.0]') // Set maximum number of iteration opt = optimbase_configure(opt,"-maxiter",200) opt = optimbase_destroy(opt);
See Also
- optimbase_new — Creates a new optimization object.
- optimbase_cget — Returns the value for the given key.
Report an issue | ||
<< optimbase_checkx0 | Optimization base | optimbase_destroy >> |