Optimal Control: Unconstrained and Constrained Problems

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Linear Optimization: Finding Initial BFS

Explains the process of finding an initial Basic Feasible Solution for linear optimization problems using the Simplex Algorithm.

Optimization Techniques: Convexity in Machine Learning

Covers optimization techniques in machine learning, focusing on convexity and its implications for efficient problem-solving.

Relations Between Events

Explores relations between events, disjunctive constraints, and modeling with binary variables in optimization problems.

Equivalent formulation

Covers the concept of equivalent formulation in constrained optimization and explores the tangent cone.

Polynomial Optimization: SOS and SDP

Explores Sum of Squares polynomials and Semidefinite Programming in Polynomial Optimization, enabling the approximation of non-convex polynomials with convex SDP.

Agency Theory: Incentives and Contracts

Explores incentive compatibility, optimal contracts, and agency costs in aligning principal-agent interests.

Support Vector Machines: SVM Basics

Covers the basics of Support Vector Machines, focusing on hard-margin and soft-margin formulations.

Energy System Modelling: Overview and Optimization Problems

Covers energy system modelling, optimization, scenarios, predictions, complexities, and controversies in energy models.

Two-phase Simplex Algorithm: Introduction and Duality

Introduces the two-phase simplex algorithm and explores duality in linear programming.

Lagrangian Duality: Convex Optimization

Explores Lagrangian duality in convex optimization, transforming problems into min-max formulations and discussing the significance of dual solutions.