This repository contains seminars resources for the course "Optimization methods" for the 3-rd year students of Department of Control and Applied Mathematics. Every seminar presents brief review of necessary part of theory covered in lectures and examples of standard tasks for considered topic.
The main tool in development of efficient optimization methods is numerical linear algebra. To refresh your knowledge, you can use the crash course (ru, en).
Almost all numerical tests in this repository are performed with liboptpy library, where you can find easy to use implementations of different optimization methods. Also we use CVXPY 1.0 for comparison purpose.
The minimum list of questions (ru) on the topics in Fall term.
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Preliminary
The minimum list of questions (ru) on topics of Spring term.
- Nesterov's method and ODE (ru, en)
- Sequential quadratic programming
- Theory of optimal methods and lower complexity bounds
- Mirror descent
- Optimization methods on Riemanien manifolds
- Structured optimization with sparsity conditions
- Submodular optimization
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Y.E. Nesterov. Introductory lectures on convex optimization: A basic course
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Lecture notes on Modern Convex Optimization by A. Nemirovski
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Introductory Lectures on Stochastic Optimization by J. Duchi
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Practical optimization by P. E. Gill, W. Murray, M. H. Wright
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Advanced Optimization and Randomized Methods by A. Smola and S. Sra at CMU
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Convex Optimization and Approximation by M. Hardt at UC Berkeley
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