Lectures related to Dynamic Programming: Integer Programming

Linear Programming: Optimization and Constraints

Explores linear programming optimization with constraints, Dijkstra's algorithm, and LP formulations for finding feasible solutions.

Cutset Formulation: MST Problem

Explores the cutset formulation for the MST Problem and Gomory Cutting Planes method.

Integer Program Formulation

Covers the process of formulating integer programs and improving solutions.

Convex Polyhedra and Linear Programs

Explores convex polyhedra, linear programs, and their optimization importance.

Solving Parity Games in Practice

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

Transition to Smart Cities: Complexity and Interdependencies

Delves into the complexity and interdependencies of transitioning to smart cities, emphasizing the importance of a holistic approach.

Algorithmic Complexity: Definition and Examples

Explores algorithm correctness, worst-case complexity analysis, and efficiency comparison based on input size.

Integer Optimization: Theory and Applications

Covers the fundamentals of integer optimization, including integer programming, dynamic programming, and approximation algorithms.

Approximation Algorithms

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

Simplex Algorithm: Tableau

Covers the main idea behind the Simplex algorithm and explains the Tableau method for solving linear programming problems.

Linear Programming: Weighted Bipartite Matching

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

Implementation Research: Concepts and Scope

Explores Implementation Research, addressing challenges in implementing health interventions and developing effective strategies for infectious diseases of poverty.

Convex Hulls: Complexity and Vertices

Explores the complexity of convex hulls and the concept of vertices within them.

Linear Systems: Convergence and Methods

Explores linear systems, convergence, and solving methods with a focus on CPU time and memory requirements.

Optimization: Classical Problems

Covers classical optimization problems, brute force algorithms, and integer linear optimization.

Optimal Decision Making: Sensitivity Analysis

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

Optimization Problems: Path Finding and Portfolio Allocation

Covers optimization problems in path finding and portfolio allocation.

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Graph Coloring: Theory and Applications

Covers the theory and applications of graph coloring, focusing on disassortative stochastic block models and planted coloring.

Optimization Methods: Theory Discussion

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