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 ...

The success of parallel computing in solving real-life computationally intensive problems relies on their efficient mapping and execution on large-scale multiprocessor architectures. Many important ...

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 ...

Q-Learning: A Paradigm In Dynamic Programming. In DP, there are a host of algorithms to obtain MDPs. The popular amongst them that finds more implementation in applications is Q-learning. This method ...