chap06_8up

# Chap06_8up

This preview shows page 1. Sign up to view the full content.

This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Multi-Dimensional Optimization Scientiﬁc Computing 1.000 −0.667 0.444 −0.296 0.198 −0.132 0.088 −0.059 0.039 −0.026 33 / 74 Unconstrained Optimization Nonlinear Least Squares Constrained Optimization f (xk ) 15.000 6.667 2.963 1.317 0.585 0.260 0.116 0.051 0.023 0.010 f (xk ) 5.000 −3.333 2.222 −1.481 0.988 −0.658 0.439 −0.293 0.195 −0.130 Scientiﬁc Computing Michael T. Heath Optimization Problems One-Dimensional Optimization Multi-Dimensional Optimization Example, continued 5.000 3.333 2.222 1.481 0.988 0.658 0.439 0.293 0.195 0.130 34 / 74 Unconstrained Optimization Nonlinear Least Squares Constrained Optimization Newton’s Method Broader view can be obtained by local quadratic approximation, which is equivalent to Newton’s method In multidimensional optimization, we seek zero of gradient, so Newton iteration has form − xk+1 = xk − Hf 1 (xk ) f (xk ) where Hf (x) is Hessian matrix of second partial derivatives of f , {Hf (x)}ij = ∂ 2 f (x) ∂xi ∂xj < interactive example > Michael T. Heath Optimization Problems One-Dimensional Optimization Multi-Dimensional Optimization Scientiﬁc Computing 35 / 74 Unconstrained Optimization Nonlinear Least Squares Constrained Optimization Michael T. Heath Newton’s Method, continued Use Newton’s method to minimize f (x) = 0.5x2 + 2.5x2 1 2 Hf (xk )sk = − f (xk ) Gradient and Hessian are given by for Newton step sk , then take as next iterate f (x) = xk+1 = xk + sk As usual, Newton’s method is unreliable unless started close enough to solution to converge < interactive example > Scientiﬁc Computing 37 / 74 Unconstrained Optimization Nonlinear Least Squares Constrained Optimization 5 , we have 1 10 05 Michael T. Heath f (x0 ) = Scientiﬁc Computing 38 / 74 Unconstrained Optimization Nonlinear Least Squares Constrained Optimization Newton’s Method, continued If objective function f has continuous second partial derivatives, then Hessian matrix Hf is symmetric, and near minimum it is positive deﬁnite In principle, line search parameter is unnecessary with Newton’s method, since quadratic model determines length, as well as direction, of step to next approximate solution Thus, linear system for step to next iterate can be solved in only about half of work required for LU factorization Far from minimum, Hf (xk ) may not be positive deﬁnite, so Newton step sk may not be descent direction for function, i.e., we may not have When started far from solution, however, it may still be advisable to perform line search along direction of Newton step sk to make method more robust (damped Newton) f (xk )T sk < 0 Once iterates are near solution, then αk = 1 should sufﬁce for subsequent iterations Scientiﬁc Computing and Hf (x) = Optimization Problems One-Dimensional Optimization Multi-Dimensional Optimization Newton’s Method, continued Michael T. Heath x1 5x2 5 5 10 −5 Linear system for Newton step is s= , so 05 0 −5 5 −5 0 x1 = x0 + s0 = + = , which is exact solution 1 −1 0 for this problem, as expected for quadratic function Taking x0 = Convergence rate of Newton’s method for minimization is normally quadratic...
View Full Document

## This note was uploaded on 10/16/2011 for the course MECHANICAL 581 taught by Professor Wasfy during the Fall '11 term at IUPUI.

Ask a homework question - tutors are online