- Scilabヘルプ
 - Optimization and 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 2026.0.0. This page might be outdated.
See the recommended documentation of this function
optimbase_configure
Configures the current object.
Syntax
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 minimum 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.
 
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