TL;DR
This paper introduces quasiconvex programming, a generalized optimization framework for minimizing the maximum of quasiconvex functions, and surveys algorithms and applications across various fields.
Contribution
It defines quasiconvex programming and reviews algorithms and applications, extending linear programming techniques to a broader class of problems.
Findings
Algorithms for quasiconvex programming are effective in various applications.
Quasiconvex programming generalizes linear programming methods.
Applications include meshing, scientific computation, and robust statistics.
Abstract
We define quasiconvex programming, a form of generalized linear programming in which one seeks the point minimizing the pointwise maximum of a collection of quasiconvex functions. We survey algorithms for solving quasiconvex programs either numerically or via generalizations of the dual simplex method from linear programming, and describe varied applications of this geometric optimization technique in meshing, scientific computation, information visualization, automated algorithm analysis, and robust statistics.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsOptimization and Variational Analysis · Advanced Optimization Algorithms Research · Optimization and Search Problems