Variance Reduction: Strategies and Applications

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Optimization Techniques: Stochastic Gradient Descent and Beyond

Discusses optimization techniques in machine learning, focusing on stochastic gradient descent and its applications in constrained and non-convex problems.

Primal-dual Optimization: Algorithms and Convergence

Explores primal-dual optimization algorithms for convex-concave minimax problems, discussing convergence properties and applications.

Optimization Methods

Covers the Newton's local method in Python using NumPy for optimization.

Suspension Optimisation: Performance Analysis and Optimization

Explores the design and optimization of car suspensions for improved performance.

Approximation Algorithms

Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.

Simplex Algorithm: Basics

Introduces the Simplex algorithm for solving flow problems and handling negative cost cycles.

Optimal Weak Transport Barycenters: Computational Aspects

By Elsa Cazelles explores the computation of optimal weak transport barycenters and their practical applications.

Modeling Prisoner's Dilemma: Naive vs. Optimal

Explores the modeling of the '100 prisoners' problem and compares naive and optimal approaches.

PCA: Derivation and Optimization

Covers the derivation of PCA projection, error minimization, and eigenvector optimization.

Taylor Expansions: First Order

Explores Taylor expansions of first order in optimization on manifolds.