Lectures related to Linear Algebra Complexity

Explores constraints, efficiency, and complexity in linear algebra, emphasizing convexity and worst-case complexity in algorithm analysis.

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

Covers Dijkstra's algorithm and its application to the all-pairs shortest path problem.

Covers algorithmic complexity and travel time analysis, focusing on measuring the time taken by algorithms and evaluating their performance.

Explores the Extra-Gradient method for Primal-dual optimization, covering nonconvex-concave problems, convergence rates, and practical performance.

Explores dynamic programming through Pascal's Triangle and Floyd's Algorithm.

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

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

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

Introduces the concept of shortest path, discussing weighted paths, Hamiltonian paths, and path optimization algorithms.

Covers the time complexity of algorithms and includes a quiz.

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

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

Explores methods and algorithms for integer factorization, including testing for B smoothness and computing small primes.

Explores dynamic programming for efficient problem-solving, illustrated with binomial coefficients and Pascal's triangle.

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

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

Covers complexity classes, effect on computer time, tractable problems, class NP, and NP-complete problems.

Covers complexity classes, tractable problems, the class NP, NP-complete problems, and summarizes the concept of tractable problems.

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