Lectures related to Longest Common Subsequence: Dynamic Programming Algorithm

Delves into dynamic programming with a focus on Matrix Chain Multiplication and the longest common subsequence problem.

Covers a dynamic programming algorithm for a financial adviser to maximize the probability of impressing her clients.

Explores the Tower of Hanoi algorithm, recursion, and dynamic programming in solving problems efficiently.

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

Explores group membership and view synchrony in distributed systems, ensuring accurate knowledge and coordinated views.

Explores dynamic programming strategies for finding shortest paths in networks with various algorithms and complexities.

Covers counting distinct elements using hash functions and the median trick.

Covers search algorithms, focusing on abductive reasoning and heuristic search strategies.

Explores the complexity of calculating binomial coefficients using dynamic programming.

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

Explores dynamic programming with Bellman-Ford, Dijkstra, greedy strategies, and activity scheduling problems.

Explores dynamic programming for the Knapsack Problem, optimizing weight accumulation and profit subsets efficiently.

Covers optimization algorithms, stable matching, and Big-O notation for algorithm efficiency.

Explores reliable broadcast in distributed systems, presenting specifications, algorithms, and properties for different types of broadcast.

Covers Markov Decision Processes, their structure, and their role in reinforcement learning.

Introduces an improved algorithm for three-color parity games, focusing on progress measures, acceleration, and practical speed-up.

Covers the median search algorithm and the Hoare algorithm for finding the smallest element in a list.

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