Lecture

Integer Optimization: Theory and Applications

Related lectures (25)

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

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

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

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

Covers worst-case complexity, algorithms, and proofs including mathematical induction and recursion.

Explores the knapsack problem and the traveling salesman problem with a focus on optimization algorithms.

Explores the integrality gap concept in set cover and multiplicative weights algorithms.

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

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

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

Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.

Introduces algorithms as problem-solving procedures, covering complexity, correctness, and implementation in various languages.

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

Introduces greedy algorithms and matroids, highlighting their efficiency in solving optimization problems.

Covers coin rendering and the limitations of the greedy algorithm in finding optimal solutions.

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

Introduces an improved algorithm for three-color parity games, focusing on progress measures, acceleration, and practical speed-up.

Delves into the theory of computation, covering decidability, complexity, P vs. NP, and reductions.

Covers classical and quantum computational complexity concepts and implications.

Examines NP problems, graph coloring, path optimization, and computational complexity distinctions in P and NP classes.