Greedy Strategy and Dynamic Programming

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Related lectures (29)

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Problem Solving Strategies: General Overview

Presents methods for problem-solving, emphasizing 'Divide and Conquer', recursion, and dynamic programming.

Cutset Formulation: MST Problem

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

Algorithm Design: Divide and Conquer

Covers recursion, dynamic programming, and algorithm design using divide and conquer strategies.

Approximation Algorithms

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

Branch and Bound: Heuristic Maximization

Explains the Branch and Bound algorithm for heuristic maximization problems using LP relaxations and pruning techniques.

Recursive Algorithms: Divide and Conquer

Explores the concept of divide and conquer in recursive algorithms, exemplified by the Towers of Hanoi.

Dynamic Programming: Binomial Coefficients

Explores dynamic programming through binomial coefficients calculation, emphasizing efficiency and memoization in problem-solving.

Problem-solving Strategies 2: Recursion

Explores problem-solving strategies like recursion and divide and conquer methods, with examples such as the Towers of Hanoi.

Designing Algorithms: Recursion and Dynamic Programming

Explores designing algorithms with recursion and dynamic programming, covering concepts like the Towers of Hanoi and efficient solutions.

Branch & Bound: Optimization

Covers the Branch & Bound algorithm for efficient exploration of feasible solutions and discusses LP relaxation, portfolio optimization, Nonlinear Programming, and various optimization problems.