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Please note that the recommended version of Scilab is 2025.1.0. This page might be outdated.
However, this page did not exist in the previous stable version.

Scilab Help >> Optimization and Simulation

Optimization and Simulation

• Annealing
  • Utilities
    • accept_func_default — The default Simulated Annealing acceptation function.
    • accept_func_vfsa — The Very Fast Simulated Annealing acceptation function.
    • compute_initial_temp — A SA function which allows to compute the initial temperature of the simulated annealing
    • neigh_func_csa — The classical neighborhood relationship for the simulated annealing
    • neigh_func_default — A SA function which computes a neighbor of a given point
    • neigh_func_fsa — The Fast Simulated Annealing neighborhood relationship
    • neigh_func_vfsa — The Very Fast Simulated Annealing neighborhood relationship
    • temp_law_csa — The classical temperature decrease law
    • temp_law_default — A SA function which computed the temperature of the next temperature stage
    • temp_law_fsa — The Szu and Hartley Fast simulated annealing
    • temp_law_huang — The Huang temperature decrease law for the simulated annealing
    • temp_law_vfsa — This function implements the Very Fast Simulated Annealing from L. Ingber
  • optim_sa — A Simulated Annealing optimization method
• Genetic algorithms
  • Algorithms
    • optim_ga — A flexible genetic algorithm
    • optim_moga — multi-objective genetic algorithm
    • optim_nsga — A multi-objective Niched Sharing Genetic Algorithm
    • optim_nsga2 — A multi-objective Niched Sharing Genetic Algorithm version 2
  • Utilities
    • coding_ga_binary — A function which performs conversion between binary and continuous representation
    • coding_ga_identity — A "no-operation" conversion function
    • crossover_ga_binary — A crossover function for binary code
    • crossover_ga_default — A crossover function for continuous variable functions
    • init_ga_default — A function a initialize a population
    • mutation_ga_binary — A function which performs binary mutation
    • mutation_ga_default — A continuous variable mutation function
    • output_ga_default — A simple output function used for logging purposes
    • pareto_filter — A function which extracts non dominated solution from a set
    • selection_ga_elitist — An 'elitist' selection function
    • selection_ga_random — A function which performs a random selection of individuals
• Neldermead
  • fminsearch — Computes the unconstrained minimum of given function with the Nelder-Mead algorithm.
  • neldermead — Provides direct search optimization algorithms.
  • overview — An overview of the Nelder-Mead toolbox.
  • nmplot — Provides direct search optimization algorithms.
  • optimget — Queries an optimization data structure.
  • optimplotfunccount — Plot the number of function evaluations of an optimization algorithm
  • optimplotfval — Plot the function value of an optimization algorithm
  • optimplotx — Plot the value of the parameters of an optimization algorithm
  • optimset — Configures and returns an optimization data structure.
• Nonlinear Least Squares
  • datafit — Non linear (constrained) parametric fit of measured (weighted) data
  • leastsq — Solves non-linear least squares problems
  • lsqrsolve — minimize the sum of the squares of nonlinear functions, levenberg-marquardt algorithm
• Optimization base
  • optimbase_cget — Returns the value for the given key.
  • optimbase_checkbounds — Checks the bounds.
  • optimbase_checkcostfun — Checks the cost function.
  • optimbase_checkx0 — Checks initial guess.
  • optimbase_configure — Configures the current object.
  • optimbase_destroy — Resets the historyfopt and historyxopt fields of an object.
  • optimbase_function — Calls cost function.
  • optimbase_get — Returns the value for the given key.
  • optimbase_hasbounds — Checks if the bounds are specified.
  • optimbase_hasconstraints — Checks if the constraints are specified.
  • optimbase_hasnlcons — Checks if the non linear constraints are specified.
  • optimbase_histget — Returns the history value.
  • optimbase_histset — Set the history value at given iteration for the given key.
  • optimbase_incriter — Increments the number of iterations.
  • optimbase_isfeasible — Checks if the point satisfies constraints.
  • optimbase_isinbounds — Checks if the given point satisfies bounds constraints.
  • optimbase_isinnonlincons — Checks if the given point satisfies the non-linear constraints.
  • optimbase_log — Prints the given message.
  • optimbase_new — Creates a new optimization object.
  • optimbase_outputcmd — Calls back user's output command.
  • optimbase_outstruct — Returns a data structure with type T_OPTDATA.
  • overview — An overview of the Optimbase toolbox.
  • optimbase_proj2bnds — Returns a projection point.
  • optimbase_set — Set the value for the given key.
  • optimbase_stoplog — Prints the given stopping message.
  • optimbase_terminate — Checks if the algorithm is terminated.
• Simplex
  • optimsimplex_center — Computes the center.
  • optimsimplex_check — Checks the consistency of internal data
  • optimsimplex_compsomefv — Computes the values of the function at vertices points.
  • optimsimplex_computefv — Computes the values of the function at vertices points.
  • optimsimplex_deltafv — Computes the difference of function values.
  • optimsimplex_deltafvmax — Computes the difference of function value between the highest and the lowest vertices.
  • optimsimplex_destroy — Destroys the simplex object.
  • optimsimplex_dirmat — Computes the directions.
  • optimsimplex_fvmean — Computes the mean.
  • optimsimplex_fvstdev — Computes the standard deviation.
  • optimsimplex_fvvariance — Computes the variance.
  • optimsimplex_getall — Returns all the data contained in the simplex object.
  • optimsimplex_getallfv — Returns all the function values contained in the simplex object.
  • optimsimplex_getallx — Returns all the coordinates.
  • optimsimplex_getfv — Returns the function value at given index.
  • optimsimplex_getn — Returns the dimension of the space.
  • optimsimplex_getnbve — Gets the number of vertices of the simplex.
  • optimsimplex_getve — Gets the vertex at the given index.
  • optimsimplex_getx — Gets the coordinates of the vertex at given index.
  • optimsimplex_gradientfv — Returns the simplex gradient of the function.
  • optimsimplex_new — Creates a new simplex object.
  • overview — An overview of the Optimsimplex toolbox.
  • optimsimplex_reflect — Returns the reflected simplex object.
  • optimsimplex_setall — Sets all in the simplex object.
  • optimsimplex_setallfv — Sets all the function values.
  • optimsimplex_setallx — Sets all the coordinates.
  • optimsimplex_setfv — Sets the function value at given index.
  • optimsimplex_setn — Sets the dimension of the space.
  • optimsimplex_setnbve — Sets the number of vertices of the simplex.
  • optimsimplex_setve — Sets the data at given index in the current simplex.
  • optimsimplex_setx — Sets the coordinates at given index into the current simplex.
  • optimsimplex_shrink — Shrinks the simplex.
  • optimsimplex_size — Computes the size.
  • optimsimplex_sort — Sorts the simplex.
  • optimsimplex_xbar — Returns the center of n vertices.
• Semidefinite Programming
  • aplat — Flattens a list.
  • lmisolver — Solve linear matrix inequations.
  • lmitool — Graphical tool for solving linear matrix inequations.
  • pack — Compress a list of block-diagonal symmetric matrices.
  • recons — Inverse function for aplat.
  • semidef — Solve semidefinite problems.
  • unpack — Uncompress a list of block-diagonal symmetric matrices.
• NDcost — generic external for optim computing gradient using finite differences
• fsolve — find a zero of a system of n nonlinear functions
• karmarkar — Solves a linear optimization problem.
• optim — non-linear optimization routine
• qld — linear quadratic programming solver
• qp_solve — linear quadratic programming solver builtin
• qpsolve — linear quadratic programming solver
• readmps — Reads a Linear Program from a MPS file.

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