Lectures related to Greedy Algorithms & Matroids

Covers the concept of matroids, focusing on matroid intersection and the properties of subsets of a ground set.

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

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

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

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

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

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

Covers classical and quantum computational complexity concepts and implications.

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

Covers optimization algorithms, stable matching, and Big-O notation for algorithm efficiency.

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

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

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

Explores algorithmic challenges, time complexity, optimization, recursion, and probability calculations.

Explores algorithmic complexity, visualization of functions, and algorithm efficiency analysis using Python.

Provides an overview of essential search and sort algorithms in computer science.

Focuses on solving 2019 Algorithms Midterm Exam problems and analyzing time complexities.

Explores worst-case complexity, mathematical induction, and algorithms like binary search and insertion sort.

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