Lectures related to The Backpack Problem

Solving Parity Games in Practice

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

Complexity & Induction: Algorithms & Proofs

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

Constraint Satisfaction: Formulation and Algorithms

Covers the formulation of constraint satisfaction problems and systematic algorithms for solving them efficiently.

Complex Systems: Critical Phenomena

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

Knapsack Problem: Optimization and Traveling Salesman

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

Elements of computational complexity

Covers classical and quantum computational complexity concepts and implications.

Linear Programming: Optimization and Constraints

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

Promise Constraint Satisfaction and Width

Covers Promise Constraint Satisfaction Problems complexity, width, graph coloring, polymorphisms, and algorithms.

Optimisation in Energy Systems

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

Complexity Classes: P and NP

Explores complexity classes P and NP, highlighting solvable and verifiable problems, including NP-complete challenges.

Dynamic Programming: Knapsack

Explores dynamic programming for the Knapsack problem, discussing strategies, algorithms, NP-hardness, and time complexity analysis.

Discrete Optimization: The Curse of Dimensionality

Delves into the curse of dimensionality in discrete optimization, highlighting the challenges of exponential computational time growth with problem size.

Optimization: Classical Problems

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

Quasi-newton optimization

Covers gradient line search methods and optimization techniques with an emphasis on Wolfe conditions and positive definiteness.

Minimum Spanning Trees: Prim's Algorithm

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

Algorithmes: introduction

Covers the basics of algorithms, problem-solving, and efficient resolution methods.

Optimization Algorithms

Covers optimization algorithms, convergence properties, and time complexity of sequences and functions.

Dynamic Programming: Rod Cutting and Matrix Chain Multiplication

Covers dynamic programming techniques for solving the rod cutting and matrix chain multiplication problems.

Integer Optimization: Theory and Applications

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

Traveling Salesman Problem: Introduction and Approximation Methods

Introduces the Traveling Salesman Problem and explores approximation methods using Markov chains.