Simplex Method: Basics and Applications

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Two-phase Simplex Algorithm: Introduction and Duality

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

Nonlinear Programming: Part I

Covers the fundamentals of Nonlinear Programming and its applications in Optimal Control, exploring techniques, examples, optimality definitions, and necessary conditions.

SVM for Non-separable Datasets

Explains SVM for non-separable datasets, introducing slack variables and optimizing the margin for classification.

Complex Systems: Critical Phenomena

Explores critical phenomena in complex systems, including stochastic objects, percolation, and combinatorial optimization.

Optimal Decision Making: Sensitivity Analysis

Covers sensitivity analysis in linear programming, focusing on optimal solutions and their sensitivities to changes.

Agency Theory: Incentives and Contracts

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

Hedging for LPs

Covers the concept of hedging for Linear Programs and the simplex method, focusing on minimizing costs and finding optimal solutions.

The Simplex Algorithm: Efficiency and Degeneracy

Covers the Simplex Algorithm, focusing on efficiency and degeneracy in linear optimization problems.

Max-Margin Classifiers

Explores maximizing margins for better classification using support vector machines and the importance of choosing the right parameter.

Primal-dual Optimization: Fundamentals

Explores primal-dual optimization, minimax problems, and gradient descent-ascent methods for optimization algorithms.