- Scilab Help
- 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 This page might be outdated.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.
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.
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