Dynamic Programming, Greedy Algorithms @ University of Colorado-Boulder Resources

Repository for algorithm design course projects. Covers divide and conquer, dynamic programming, greedy algorithms, NP-completeness, and advanced data structures. Includes practical applications and optimization problem solving.

Part 2: Greedy and Dynamic: You have two problems to solve, using either greedy or dynamic programming algorithms. Your first job, before writing any code, is to figure out an algorithm that will solve the problem!

Dec 24, 2020 · Greedy Algorithms and Dynamic Programming. Date: December 24, 2020 Conducted by: Utkarsh, Harsh, Hemant. Agenda. Greedy. Thinking greedily Greedy choice property; Optimal substructure; Problem discussion and Applications; Dynamic Programming. Introduction; Solving a Problem; Propertites of DP problems …

Oct 13, 2023 · Dynamic programming: We learned about the concept of overlapping subproblems and optimal substructure. We explored the bottom-up and top-down approaches to dynamic programming and analyzed their time and space complexity. Greedy algorithms: We discussed the concept of greedy choice and its applications in solving optimization problems.

Next section is about the Greedy Algorithms and Dynamic Programming. It will be quite a generous introduction to the concepts and will be followed by some common problems. Greedy Algorithms ¶ Dynamic Programming ¶ Common DP Problems ¶ Bitmask DP ¶ DP on Rooted Trees ¶ DP on Directed Acyclic Graphs ¶ Digit DP ¶ Walk Counting using Matrix ...

Nov 5, 2024 · Dynamic programming starts by solving subproblems and builds up to solving the big problem. Every dynamic-programming algorithm starts with a grid. Here’s a grid for the knapsack problem.

Apr 5, 2021 · In this module, you will learn about dynamic programming as a design principle for algorithms. We will provide a step-by-step approach to formulating a problem as a dynamic program and solving these problems using memoization.

In this assignment, we will explore greedy algorithms for makespan scheduling. We will see how a greedy algorithm can sometimes provide a solution that is guaranteed to be within some constant factor of the best possible solution.

This repository contains Coursera Stanford Algorithm Specialization implementations in Python. Knapsack problem with bottom-up dynamic programming approach.