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Instructor: Anatoli Juditsky Bat. IMAG, #156, email: anatoli.juditsky@univ-grenoble-alpes.fr
Lectures take place on Wed 3:30pm--6:30pm in H205
Midterm Exam: Wednesday Nov. 4, 2026
Assignment deadlines: HW1 Monday Nov. 2, 2026 HW2 Monday Jan. 18, 2027
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
When everything is simple: 1-dimensional Convex Optimization
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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