We propose a steepest descent method for unconstrained multicriteria optimization and a “feasible descent direction” method for the constrained case. In the unconstrained case, the objective functions are assumed to be continuously differentiable.
Homework 1 due Feb. 13 1. Describe the atmospheric optics problem. Show that the reconstruction problem is ill-posed and discuss the parameters that determine the ill-posedness of the problem. Show (analytically or numerically) how the ill-posedness is changing with changing these parameters.. Describe the steepest descent method and show.
Algorithms for unconstrained problems (steepest descent, Newton’s, etc.) and analysis of their convergence Optimality conditions and constraint quali cations for constrained problems Convexity and its role in optimization Algorithms for constrained problems (SQP, barrier and penalty methods, etc.).
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View Homework Help - Homework 3 from MAT 362 at Northern Arizona University. MAT 362 Spring 2007 6. Programming assignment: Steepest descent Name: Instructor: Nndor Sieben a e-mail.
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Homework. Homework 1 (due Feb 6). (Solutions) Homework 2 (due Feb 27) (Solutions) Homework 3 (due March 17) MATLAB example of plotting steepest descent contours Homework 4 (due April 7) Homework 5 (due May 6).
The following exercise demonstrates the use of Quasi-Newton methods, Newton's methods, and a Steepest Descent approach to unconstrained optimization. The following tutorial covers: Newton's method (exact 2nd derivatives) BFGS-Update method (approximate 2nd derivatives).