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Instructor: Anatoli Juditsky Bat. IMAG, #156, email: anatoli.juditsky@univ-grenoble-alpes.fr
Lectures take place on Tue 11:15am-12:45pm and Wed 5pm-6:30pm in H205
Assignment deadlines: HW1 Tuesday Nov. 4, 2025 HW2 Monday Jan. 19, 2026
Midterm: Nov. 4, 2025
Part I Optimization theory
Part I Optimization theory
Preliminaries. Complexity of Nonlinear Optimization
Convex sets. Theory of Linear Programming
Convex functions. Convex Programming and Lagrange Duality
From Linear to Conic Programming
Conic Quadratic and Semidefinite Programs
Part II Optimization methods
Part II Optimization methods
Gradient Descent and Newton Method. Around Newton Method
Complexity of Convex Programming. Ellipsoid Method
Methods for high-dimensional convex problems Mirror Descent algorithm
Polynomial-time Interior-Point Methods
Algorithms of Stochastic Optimization
Fundamentals: affine spaces, differentiable functions, symmetric matrices
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