TEACHING LIST

Teaching assistant at UMons (2018-2020) for:

• I-MARO-020 : Linear Algebra I

• I-MARO-022 : Linear Algebra II

• I-MARO-023 : Analysis II

• I-MARO-004 : Numerical Analysis

• I-MARO-005: Probability

• I-MARO-007: Statistics

• I-MARO-024: Systems of differential equations and integral transforms

• I-MARO-035: Linear programming 

Feedbacks from the students:

Each year at UMons, students are asked to give evaluations for the teachers and the teaching assistants. They give a global score on a 1 to 5 scale from "Very insufficient" to "Excellent" and support it with comments. You can find here my evaluations from the students at UMons during the following periods; 2020[pdf], 2019[pdf] and 2018[pdf]. 

Teaching assistant at Skoltech for:

• MA030136 : Convex Optimization and Applications (2021, 2022) 

• MA060468 : Tensor Decompositions and Tensor Networks in AI (2022, 2023)

TEACHING NOTES

In this section, you can find teaching notes dedicated to various topics. Please email me if you catch errors.

Please be indulgent, this section is under construction and will be updated from time to time. 

Linear Algebra - Matrix Theory:

• General Introduction

• Matrix derivative (Petersen Cookbook)

Algebraic structures for optimization:

• Vector spaces, variables and gradients vectors

Continuous Optimization:

• General Introduction: what is non-linear optimization ?

• Smooth convex optimization

• Non-smooth convex optimization

• Proximal Algorithms:

  • Proximal Algorithms (Boyd)

  • Proximal gradient method (L. Vandenberghe)

  • Accelerated proximal gradient methods (L. Vandenberghe)

  • Proximal Mapping (L. Vandenberghe)

• Dual Methods:

  • Conjugate functions (L. Vandenberghe)

  • Alternating Direction Method of Multipliers (ADMM)

  • ADMM for Matrix and Tensor Decompositions 

  • Proof of convergence for ADMM (To come soon)

  • Dual proximal gradient method (L. Vandenberghe)

• Primal-Dual Methods:

  • A gentle introduction to Primal-Dual Algorithms

  • RandProx: Primal-Dual Method with Randomized Proximal Updates

  • Primal-dual subgradients methods for convex optimization

• Higher-Order Methods:

  • Inexact accelerated high-order proximal-point methods

• The Majorization-Minimization framework

• Some basics of smooth non-convex optimization

• Optimization in relative scale

• An introduction to Complex Differentials and Complex Differentiability

Lecture series on Lyapunov functions for optimization:

• Introduction to continuous-time dynamics for optimization

• Introduction to convergence analysis of dynamics via Lyapunov functions

• Lyapunov analysis for accelerated methods in optimization 

ISP - Skoltech (2023):

• Introduction to stochastic optimization for large-scale problems:

  • Part 1

  • Part 2

  • Part 3 (proofs gathering, to come)

ISP - Skoltech (2024):

• Introduction to higher-order optimization methods:

  • Part 1 -  Starter

  • Part 2 -  Main Dish

  • Side dishes:

    • Introduction to SLAM Problem (by Prof. Ferrer)

    • Introduction to Automatic Differentiation (by D. Merkulov)

    • Gauss-Newton's Method (by Dr. Ahmadi)

Graduate School SOCN (given by Panos Patrinos from KULeuven)

• Main directory for lecture notes