Finding search directions in quasi-Newton methods for minimizing a quadratic function subject to uncertainty

• Published in: Computational optimization and applications Vol. 91; no. 1; pp. 145 - 171
• Main Authors: Peng, Shen, Canessa, Gianpiero, Ek, David, Forsgren, Anders
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2025; Springer Nature B.V
• Subjects: Arithmetic; Chance constrained model; Constraints; Convex and Discrete Geometry; Error analysis; Management Science; Mathematics; Mathematics and Statistics; Methods; Operations Research; Operations Research/Decision Theory; Optimization; Quadratic equations; Quadratic programming; Quasi Newton methods; Quasi-Newton method; Searching; Statistics; Stochastic quasi-Newton method; Subspaces; Stochastic quasi-Newton method; Quasi-Newton method; Quadratic programming; Chance constrained model

We investigate quasi-Newton methods for minimizing a strongly convex quadratic function which is subject to errors in the evaluation of the gradients. In particular, we focus on computing search directions for quasi-Newton methods that all give identical behavior in exact arithmetic, generating mini...