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Lecture
Optimization: Classical Problems
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Related lectures (52)
Approximation Algorithms
Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Optimization Methods: Theory Discussion
Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.
Optimisation in Energy Systems
Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.
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.
Greedy Algorithms & Matroids
Introduces greedy algorithms and matroids, highlighting their efficiency in solving optimization problems.
Simplex Algorithm: Tableau
MOOC: Optimization: principles and algorithms - Linear optimization
Covers the main idea behind the Simplex algorithm and explains the Tableau method for solving linear programming problems.
Linear Programming: Weighted Bipartite Matching
Covers linear programming, weighted bipartite matching, and vertex cover problems in optimization.
Complexity Classes: P and NP
Explores complexity classes P and NP, highlighting solvable and verifiable problems, including NP-complete challenges.
Optimization Problems: Path Finding and Portfolio Allocation
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Covers optimization problems in path finding and portfolio allocation.
Linear Programming: Optimization and Constraints
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Explores linear programming optimization with constraints, Dijkstra's algorithm, and LP formulations for finding feasible solutions.
Linear Programming Duality
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Explores Linear Programming Duality, covering weak duality, strong duality, Lagrange multipliers interpretation, and optimization constraints.
Dynamic Programming: Knapsack
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Explores dynamic programming for the Knapsack problem, discussing strategies, algorithms, NP-hardness, and time complexity analysis.
Integer Optimization: Theory and Applications
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Covers the fundamentals of integer optimization, including integer programming, dynamic programming, and approximation algorithms.
Linear Optimization: Fundamentals
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Covers the basics of linear optimization, including equations, polyhedrons, feasible directions, and optimal solutions.
Optimization Algorithms
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Covers optimization algorithms, convergence properties, and time complexity of sequences and functions.
Solving Linear Programs: SIMPLEX Method
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Explains the SIMPLEX method for solving linear programs and optimizing the solution through basis variable manipulation.
Cutset Formulation: MST Problem
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Explores the cutset formulation for the MST Problem and Gomory Cutting Planes method.
Convex Polyhedra and Linear Programs
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Explores convex polyhedra, linear programs, and their optimization importance.
Branch & Bound: Optimization
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Covers the Branch & Bound algorithm for efficient exploration of feasible solutions and discusses LP relaxation, portfolio optimization, Nonlinear Programming, and various optimization problems.
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