slide8_handout-1

Newtons method converges if at each iteration k the

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

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: hod won’t always converge. ￿ No convergence if for some iterate xn the Jacobian ∇g (xn ) is singular (because this would be equivalent to requiring us to divide by zero) ￿ ￿ Modified Newton’s Method from lecture notes can help Sometimes no convergence from our starting point x0 if x0 is too far from the root ￿ In practice, it’s a good idea to set a maximum number of iterations so your algorithm doesn’t go forever 41 / 45 HW5 Q1 - Gradient Descent and Newton’s Method (a) Use the theorem on steepest gradient descent convergence, and remember to check for convexity to decide if it’s a global or local optimum (b) Perform just the first step of the steepest gradient descent method and Newton’s method. To determine which will converge faster, recall what we know about the convergence of Newton’s method for quadratic problems! (c) To determine when Newton’s method will converge, use the fact that we already know when g (x ) = x α = 0 (at x = 0). Newton’s method con...
View Full Document

This note was uploaded on 12/10/2013 for the course MS&E 211 taught by Professor Yinyuye during the Fall '07 term at Stanford.

Ask a homework question - tutors are online