Convex optimization
arXiv:2106.01946
Abstract
This textbook is based on lectures given by the authors at MIPT (Moscow), HSE (Moscow), FEFU (Vladivostok), V.I. Vernadsky KFU (Simferopol), ASU (Republic of Adygea), and the University of Grenoble-Alpes (Grenoble, France). First of all, the authors focused on the program of a two-semester course of lectures on convex optimization, which is given to students of MIPT. The first chapter of this book contains the materials of the first semester ("Fundamentals of convex analysis and optimization"), the second and third chapters contain the materials of the second semester ("Numerical methods of convex optimization"). The textbook has a number of features. First, in contrast to the classic manuals, this book does not provide proofs of all the theorems mentioned. This allowed, on one side, to describe more themes, but on the other side, made the presentation less self-sufficient. The second important point is that part of the material is advanced and is published in the Russian educational literature, apparently for the first time. Third, the accents that are given do not always coincide with the generally accepted accents in the textbooks that are now popular. First of all, we talk about a sufficiently advanced presentation of conic optimization, including robust optimization, as a vivid demonstration of the capabilities of modern convex analysis.
364 pages, in Russian
References in corpus (6)
- ADADELTA: An Adaptive Learning Rate Method
- New upper bounds for kissing numbers from semidefinite programming
- Near-Optimal Algorithms for Minimax Optimization
- Contracting Proximal Methods for Smooth Convex Optimization
- Improved Algorithms for Convex-Concave Minimax Optimization
- Near-Optimal Hyperfast Second-Order Method for convex optimization and its Sliding