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Bookmark or Share- Line search methods - Cornell University Computational Optimization ...Learn how to use line search methods to find local minima of nonlinear functions using gradients. Explore the basic algorithm, exact and inexact methods, convergence conditions, and applications in machine learning and other fields.
- Lecture 5: CS395T Numerical Optimization for Graphics and AI — Line SearchThe following theorem, due to Zoutendijk, has far-reaching consequences. It shows, for example, that the steepest descent method is globally convergent. For other algorithms it describes how far p k can deviate from the steepest descent direction and still give rise to a globally convergent iteration. Various line search
- 3.2 Convergence of Line Search Methods — Notes for Numerical ...The following theorem, due to Zoutendijk, quantifies the effort of properly chosen step lengths \(\alpha_k\), and shows that the steepest descent method is globally convergent. For other algorithms, it describes how far \(p_k\) can deviate from the steepest descent direction and still produce a globally convergent iteration.
- Extension of Zoutendijk method for solving constrained multiobjective ...Zoutendijk method solves a constrained single objective optimization problem by generating an improving feasible direction via a linear programming subproblem or a simple quadratic programming problem with linear constraints.
- Methods of Feasible Directions: A Review | SpringerLinkThis paper reviews several typical methods of feasible directions and their variants: Zoutendijk’s MFD, Topkis — Veinott’s MFD, Pironneau — Polak’s MFD, Wiest — Polak’s MFD, Cawood — Kostreva’s MFD, Panier — Tits’ FSQP and a modified MFD.
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