Nonconvex Optimization
Global optimization, branch-and-bound, and landscape analysis.
Nonconvex Optimization. Global optimization, branch-and-bound, and landscape analysis.
Foundations and canonical references
The standard treatments of nonconvex optimization approach the subject from complementary angles. Nocedal, Numerical Optimization (2006) is the anchor reference for the subject and lays out the core definitions, theorems, and worked examples that practitioners return to.
Recent technical contributions
A handful of recent papers carry the methodological frontier of nonconvex optimization forward. Non-convex optimization for machine learning (Jain et al., 2017) is a primary reference for this area and develops new techniques or results that downstream work builds on.
Open methodological questions for nonconvex optimization include sharpening the bridges between foundational theory and computational practice, extending classical results to broader or more structured settings, and integrating the techniques surveyed above with adjacent mathematical disciplines. The references listed in this page are the entry points that current work builds on.
Prerequisites
Sources
- textbook · primary · 2006Numerical Optimizationnocedal-2006, wright-2006
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