[MINIMUM,FVAL,EXITFLAG,OUTPUT] = fminkalman(FUN,PARS,[OPTIONS],[CONSTRAINTS], ...) unscented Kalman filter optimizer
Unconstrained optimization using the unscented Kalman filter.
The Kalman filter is actually a feedback approach to minimize the estimation
error in terms of sum of square. This approach can be generalized to general
nonlinear optimization. This function shows a way using the extended Kalman
filter to solve some unconstrained nonlinear optimization problems.
The objective function has syntax: criteria = objective(p)
Calling:
fminkalman(fun, pars) asks to minimize the 'fun' objective function with starting
parameters 'pars' (vector)
fminkalman(fun, pars, options) same as above, with customized options (optimset)
fminkalman(fun, pars, options, fixed)
is used to fix some of the parameters. The 'fixed' vector is then 0 for
free parameters, and 1 otherwise.
fminkalman(fun, pars, options, lb, ub)
is used to set the minimal and maximal parameter bounds, as vectors.
fminkalman(fun, pars, options, constraints)
where constraints is a structure (see below).
fminkalman(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
fminkalman(..., args, ...)
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;
[x,fval] = fminkalman(banana,[-1.2, 1])
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 simulated annealing,
compliant with optimset. Default options may be obtained with
o=fminkalman('defaults')
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:
Kalman, R. E. "A New Approach to Linear Filtering and Prediction Problems,
Transactions of the ASME - J. of Basic Engineering Vol. 82: pp. 35 (1960)
http://en.wikipedia.org/wiki/Kalman_filter
Contrib:
By Yi Cao at Cranfield University, 08 January 2008 [ukfopt, ukf]
Version: Aug. 22, 2017
See also: fminsearch, optimset
(c) E.Farhi, ILL. License: EUPL.