optim
Non-linear optimization toolkit.
Select category:
Minimization
Data fitting
Optimization statistics
Zero finding
Compatibility
Numerical derivatives
Pivoting
Tests
Examples
[x,v,nev,.
[x0,v,nev] = nelder_mead_min (f,args,ctl) - Nelder-Mead minimization
[x,v,nev,h,args] = d2_min(f,d2f,args,ctl,code) - Newton-like minimization
Using X0 as a starting point find a minimum of the scalar function F.
alias for fminbnd
[a,fx,nev] = line_min (f, dx, args, narg, h, nev_max) - Minimize f() along dx
Multidimensional minimization (direction-set method).
Find the minimum of a funtion of several variables.
ADSMAX Alternating directions method for direct search optimization.
MDSMAX Multidirectional search method for direct search optimization.
NMSMAX Nelder-Mead simplex method for direct search optimization.
bfgsmin: bfgs or limited memory bfgs minimization of function
samin: simulated annealing minimization of a function.
battery.
Find the minimum of a funtion of several variables.
NonLinear Conjugate Gradient method to minimize function F.
de_min: global optimisation using differential evolution
Frontend for constrained nonlinear minimization of a scalar objective function.
USAGE [alpha,c,rms] = expfit( deg, x1, h, y )
Return the coefficients of a polynomial P(X) of degree N that minimizes `sumsq (p(x(i)) - y(i))', to best fit the data in the least squares sense.
function [f,p,cvg,iter,corp,covp,covr,stdresid,Z,r2]= leasqr(x,y,pin,F,{stol,niter,wt,dp,dFdp,options})
Frontend for nonlinear minimization of residuals returned by a model function.
Frontend for nonlinear fitting of values, computed by a model function, to observed values.
general linear regression
Frontend for computation of statistics for a residual-based minimization.
Frontend for computation of statistics for fitting of values, computed by a model function, to observed values.
A variant of `fzero'.
[x,v,flag,out,df,d2f] = fminunc_compat (f,x,opt,.
opt = optimset_compat (.
Solve a linear problem.
numerical partial derivatives (Jacobian) df/dp for use with leasqr --------INPUT VARIABLES--------- x=vec or matrix of indep var(used as arg to func) x=[x0 x1 .
function prt = dcdp (f, p, dp, func[, bounds])
function jac = dfpdp (p, func[, hook])
function jac = dfxpdp (x, p, func[, hook])
c = cdiff (func,wrt,N,dfunc,stack,dx) - Code for num.
Calculate derivate of function F.
numgradient(f, {args}, minarg)
numhessian(f, {args}, minarg)
Calculate the jacobian of a function using the complex step method.
Return the Taylor coefficients and numerical differentiation of a function F for the first N-1 coefficients or derivatives using the fft.
[lb, idx, ridx, mv] = cpiv_bard (v, m[, incl])
m = gjp (m, k[, l])
[x,v,niter] = feval (optim_func, "testfunc","dtestf", xinit);
[xlev,vlev,nlev] = feval(optim_func, "ff", "dff", xinit) ;
[xlev,vlev,nlev] = feval (optim_func, "ff", "dff", xinit, "extra", extra) ; [xlev,vlev,nlev] = feval \ (optim_func, "ff", "dff", list (xinit, obsmat, obses));
Plain run, just to make sure ###################################### Minimum wrt 'x' is y0 [xlev,vlev,nlev] = feval (optim_func, "ff", "dff", {x0,y0,1}); ctl.
test_d2_min_1
Not implemented.
test_d2_min_2
Not implemented.
test_d2_min_3
Not implemented.
Use vanilla nelder_mead_min
Test using volume #################################################
ex = poly_2_ex (l, f) - Extremum of a 1-var deg-2 polynomial
Plain run, just to make sure ###################################### Minimum wrt 'x' is y0
Problems for testing optimizers.
initial values
Rosenbrock function - used to create example obj.
dimensionality
Package: optim
