Nonlinear Opt.: Basic concepts 4: Unterschied zwischen den Versionen

Version vom 1. Juli 2013, 15:55 Uhr

Nonlinear Optimaziation: Basic Concepts

Theory

A nonlinear problem also so called NLP, is similar to a linear program but it is created by the objective function, general constraints and variable bounds. The significant difference from a NLP to a LP is that NLP includes minimum one nonlinear function.

First we define what an optimal solution is. The function which is supposed to be minimized or maximized is (${\displaystyle )f(x),forx\epsilon \mathbb {R} ^{n}(}$) called the objective function. x* is an local minimum, if: (Fehler beim Parsen (http://mathoid.testme.wmflabs.org Serverantwort ist ungültiges JSON.): )[x^*\epsilon\,\mathbb{R}^n,\varepsilon > 0 ,\: f(x^*)\leq f(x)\, for\,all\,x\, \epsilon A (x^*; \varepsilon)]( ) And the global minimum, if: (Fehler beim Parsen (http://mathoid.testme.wmflabs.org Serverantwort ist ungültiges JSON.): ) \[\inline \: f(x^*)\leq f(x)\, for\, all\, x\, \epsilon \, \mathbb{R}^n\] ( )

The problem can be stated simply as: \[x\,\epsilon\, X,\, max f(x)\,to\, maximize\, some\, variable\, such\, as\, product\, throughput\] \[\inline x\,\epsilon\, X,\, min f(x)\,to\, minimize\, a\, cost\, function\, where\,\] \[\inline f: R^n \rightarrow R\] \[\inline x\, \epsilon\, R^n\] subject to: \[\inline h_i(x) = 0, \, i\,\epsilon \, I\, = 1, ..., p\]

\[\inline g_j(x)\,\leq \,0,\,j\,\epsilon \,J\,= 1,...,m\]

Example:

The following set of NLP are genaral subroutines:

NLPCG Conjugate Gradient Method

NLPDD Double Dogleg Method

NLPNMS Nelder-Mead Simplex Method

NLPNRA Newton-Raphson Method

NLPNRR Newton-Raphson Ridge Method

NLPQN (Dual) Quasi-Newton Method

NLPQUA Quadratic Optimization Method

NLPTR Trust-Region Method

The following subroutines are provided for solving nonlinear least-squares problems:

NLPLM Levenberg-Marquardt Least-Squares Method

NLPHQN Hybrid Quasi-Newton Least-Squares Methods

Example 1: Simple NLP maximization

Fehler beim Parsen (http://mathoid.testme.wmflabs.org Serverantwort ist ungültiges JSON.): f(x)= 3x-x^3 \Rightarrow max

First derivative:

Fehler beim Parsen (http://mathoid.testme.wmflabs.org Serverantwort ist ungültiges JSON.): f'(x)= 3-3x^2 \Rightarrow 3-3x^2=0 \Rightarrow x_1 = +1, \, x_2= -1

Second derivative:

Fehler beim Parsen (http://mathoid.testme.wmflabs.org Serverantwort ist ungültiges JSON.): f''(x)= -6 \Rightarrow -6< 0 \Rightarrow max (local maximum)

Example 2:

NLP Gradient Method

First we need to find the gradient:

Fehler beim Parsen (http://mathoid.testme.wmflabs.org Serverantwort ist ungültiges JSON.): f(x,y,z)= 6x^2 +2y^2+2z^2 \rightarrow \triangledown_f (6x^2 +2y+2z)=\left \{ 6x,\, 2y,\, 2z \right \}

Gradient has to equal 0:

Fehler beim Parsen (http://mathoid.testme.wmflabs.org Serverantwort ist ungültiges JSON.): \triangledown f(x)=0 \left \{ 6x,\, 2y,\, 2z \right \}= 0

Now we get the Hesse-Matrix by the derivative once again:

Fehler beim Parsen (http://mathoid.testme.wmflabs.org Serverantwort ist ungültiges JSON.): \triangledown^2_f = (6, 2, 2)

Solve for example with Rule of SARRUS:

Fehler beim Parsen (http://mathoid.testme.wmflabs.org Serverantwort ist ungültiges JSON.): a_1_,_2=2,

a_3=6 a_1_,_2_,_3>0 \Rightarrow global minimum at (0,0,0)</math>

Surces: http://ciser.cornell.edu/sasdoc/saspdf/iml/chap11.pdf www.wikipedia.org/nonlinear_programming www.sce.carleton.ca/faculty/chinneck/po/Chapter%2016.pdf