Иллюстрированный самоучитель по Matlab

# Минимизация функции нескольких переменных - часть 3

» [xmin. opt. exflag. out, grad, hessian ]=fminunc(@rb,[-1.2 1].options)

Warning: Gradient must be provided for trust-region method;

using line-search method instead.

> In C:\MATLABR12\toolbox\optim\fminunc.m at line 211

Optimization terminated successfully:

Current search direction is a descent direction, and magnitude of directional derivative in search direction less than 2*options.TolFun

xmin =

1.0000 1.0000

opt =

1.9116e-011

exflag=

out =

iterations: 26

funcCount: 162

stepsize: 1.2992

firstorderopt: 5.0020e-004

algorithm: 'medium-scale: Quasi-Newton line search'

grad=

l.Oe-003 *

-0.5002

-0.1888

hessian =

820.4028 -409.5496

-409.5496 204.7720

firstorderopt - мера оптимальности для первой нормы градиента целевой функции в найденной точке минимума;

»options=optimset('tolX' Gе-6. 'maxFunEvals' .162):

» [xmin. opt]=lsqnonlin(@rb,[-1.2 1].[0 le-6].[0 le-6],options)

Warning: Large-scale method requires at least as many equations as variables:

switching to line-search method instead. Upper and lower bounds will be ignored.

> In C:\MATLABR12\toolbox\optim\private\lsqncommon.m at line 155

In C:\MATLABR12\toolbox\optim\lsqnonlin.m at line 121

Maximum number of function evaluations exceeded Increase

OPTIONS.maxFunEvals

xmin =

0.6120 0.3715

opt =

0.1446

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