Optimization Algorithms

Related lectures (26)

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

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

Explores limits, convergence, and boundedness of functions and sequences.

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

Covers classical and quantum computational complexity concepts and implications.

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

Explores the continuity of functions in R² and R³, emphasizing accumulation points and limits.

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

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

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