Lectures related to Simplex Algorithm

Explores the Stein algorithm for polynomial identity testing and the minimization of a cut problem.

Explores Prim's algorithm for minimum spanning trees and introduces the Traveling Salesman Problem.

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Focuses on fixed points in graph theory and their implications in algorithms and analysis.

Explores practical aspects of solving parity games, including winning strategies, algorithms, complexity, determinism, and heuristic approaches.

Covers graph algorithms, modeling relationships between objects, and traversal techniques like BFS and DFS.

Covers search algorithms, focusing on abductive reasoning and heuristic search strategies.

Explores Breadth-First Search and Dijkstra's algorithm for finding shortest paths in graphs.

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Covers the KKT conditions for optimization with constraints, essential for solving constrained optimization problems efficiently.

Explores the transition from sparse to dense graphs and their real-world applications.

Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.

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

Covers differentiable functions, extreme points, and the Lagrange multiplier method for optimization.

Covers the properties and operations of convex functions.

Explores finding the equation of the tangent line to a function's graph at a point.

Covers the analysis of functions, composition, and mathematical induction.

Covers random variables, variances, and covariance, as well as the probability in random graphs.

Covers graphical models for probability distributions and factor graphs representation.

Covers linear programming, weighted bipartite matching, and vertex cover problems in optimization.