Home > Libraries > Optimizers > fmingradrand.m

## DESCRIPTION

``` [MINIMUM,FVAL,EXITFLAG,OUTPUT] = FMINGRADRAND(FUN,PARS,[OPTIONS],[CONSTRAINTS], ...) random gradient optimizer

This minimization method uses a gradient method with random directions.
Namely, it first determine a random direction in the optimization space
and then uses a Newton method. This is repeated iteratively until success.
This method is both fast and less sensitive to local minima traps.
The objective function has syntax: criteria = objective(p)

Calling:
parameters 'pars' (vector)
fmingradrand(fun, pars, options) same as above, with customized options (optimset)
is used to fix some of the parameters. The 'fixed' vector is then 0 for
free parameters, and 1 otherwise.
is used to set the minimal and maximal parameter bounds, as vectors.
where constraints is a structure (see below).
fmingradrand(problem) where problem is a structure with fields
problem.objective:   function to minimize
problem.x0:          starting parameter values
problem.options:     optimizer options (see below)
problem.constraints: optimization constraints
sends additional arguments to the objective function
criteria = FUN(pars, args, ...)

Example:
banana = @(x)100*(x(2)-x(1)^2)^2+(1-x(1))^2;

Input:
FUN is the function to minimize (handle or string): criteria = FUN(PARS)
It needs to return a single value or vector.

PARS is a vector with initial guess parameters. You must input an
initial guess. PARS can also be given as a single-level structure.

OPTIONS is a structure with settings for the optimizer,
compliant with optimset. Default options may be obtained with
options.MinFunEvals sets the minimum number of function evaluations to reach
An empty OPTIONS sets the default configuration.

CONSTRAINTS may be specified as a structure
constraints.min=   vector of minimal values for parameters
constraints.max=   vector of maximal values for parameters
constraints.fixed= vector having 0 where parameters are free, 1 otherwise
constraints.step=  vector of maximal parameter changes per iteration
constraints.eval=  expression making use of 'p', 'constraints', and 'options'
and returning modified 'p'
or function handle p=@constraints.eval(p)
An empty CONSTRAINTS sets no constraints.

Additional arguments are sent to the objective function.

Output:
MINIMUM is the solution which generated the smallest encountered
value when input into FUN.
FVAL is the value of the FUN function evaluated at MINIMUM.
EXITFLAG return state of the optimizer
OUTPUT additional information returned as a structure.

Reference: Computer Methods in Applied Mechanics & Engg, Vol  19, (1979) 99
Contrib: Sheela V. Belur(sbelur@csc.com) 1998 [ossrs]

Version: Nov. 26, 2018