Difference between revisions of "Line search methods"

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=Introduction=
 
=Introduction=
An algorithm is a line search method if it seeks the minimum of a defined nonlinear function by selecting a reasonable direction vector that, when computed iteratively with a reasonable step size, will provide a function value closer to the absolute minimum of the function. Varying these will change the "tightness" of the optimization. For example, given the function <math>f(x)</math>, an initial <math>x_k</math> is chosen, and the value of <math>f( x_k )</math> is calculated. To find a lower value of <math>f(x)</math>, the value of <math>x_k+1</math> is increased  
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An algorithm is a line search method if it seeks the minimum of a defined nonlinear function by selecting a reasonable direction vector that, when computed iteratively with a reasonable step size, will provide a function value closer to the absolute minimum of the function. Varying these will change the "tightness" of the optimization. For example, given the function <math>f(x)</math>, an initial <math>x_k</math> is chosen, and the value of <math>f(x_k)</math> is calculated. To find a lower value of <math>f(x)</math>, the value of <math>x_{k+1}</math> is increased  
  
  

Revision as of 08:48, 24 May 2015

Author names: Elizabeth Conger
Steward: Dajun Yue and Fengqi You

Contents

Introduction

An algorithm is a line search method if it seeks the minimum of a defined nonlinear function by selecting a reasonable direction vector that, when computed iteratively with a reasonable step size, will provide a function value closer to the absolute minimum of the function. Varying these will change the "tightness" of the optimization. For example, given the function f(x), an initial x_k is chosen, and the value of f(x_k) is calculated. To find a lower value of f(x), the value of x_{k+1} is increased


Step Length

Step Direction

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Steepest Descent Method

Newton Method

Quasi-Newton Method

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Solution to 48 States Traveling Salesman Problem

Conclusion

\begin{bmatrix} G(x,y) & 0 & -A(x)^T \\ 0 & Y & W \\ A(x) & -I & 0 \end{bmatrix} \begin{bmatrix} \Delta x \\ \Delta s \\ \Delta y \end{bmatrix} = \begin{bmatrix} -\nabla f(x) + A(x)^T y \\ \mu e - W Y e \\ -g(x) + s \end{bmatrix}


References

1. Sun, W. & Yuan, Y-X. (2006) Optimization Theory and Methods: Nonlinear Programming (Springer US) p 688.

2. Anonymous (2014) Line Search. (Wikipedia). http://en.wikipedia.org/wiki/Line_search.

3. Nocedal, J. & Wright, S. (2006) Numerical Optimization (Springer-Verlag New York, New York) 2 Ed p 664.