Optimization: Classical Problems

Related lectures (29)

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Covers optimization problems in path finding and portfolio allocation.

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

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.

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

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

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

Explores Linear Programming Duality, covering weak duality, strong duality, Lagrange multipliers interpretation, and optimization constraints.

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

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