Dynamic Programming is a paradigm of algorithm design in which an optimization problem is solved by a combination of achieving sub-problem solutions and appearing to the "principle of optimality".

Learn how to use algorithmic paradigms and techniques in your work, such as divide and conquer, dynamic programming, greedy, backtracking, branch and bound, recursion, iteration, sorting ...

Dynamic Programming is mainly used to solve optimization and counting problems (i.e. a problem that wants you to "minimize this" or "maximize that" or "count the ways to do that"). Dynamic Programming ...

Create divide and conquer, dynamic programming, and greedy algorithms. Understand intractable problems, P vs NP and the use of integer programming solvers to tackle some of these problems. ... We will ...

A new parallel algorithm that solves a dynamic programming paradigm is proposed. It has the time complexity of O(n) and uses (n-1)n/2 processors. An MPI implementation is used to test the algorithm.

Specialization: Data Science Foundations: Data Structures and Algorithms Instructor: Sriram Sankaranarayanan, Assistant Professor Prior knowledge needed: We highly recommended successfully completing ...

Dynamic programming guided exploration for sampling-based motion planning algorithms Abstract: Several sampling-based algorithms have been recently proposed that ensure asymptotic optimality. The ...

This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP ...

Dynamic programming algorithms are a good place to start understanding what's really going on inside computational biology software. The heart of many well-known programs is a dynamic programming ...