Formulate linear and integer programming problems for solving commonly encountered optimization problems. Understand how approximation algorithms compute solutions that are guaranteed to be within ...

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

Computers don’t simply "understand" code in the way humans do. They rely on a highly sophisticated series of steps to ...

In programming, algorithms play an invaluable role in problem solving, so it is important to note that algorithms have a larger impact in our world than simply getting millions of crawling links ...

Advanced study in models of computation, programming languages and algorithms with a specific focus on concurrent programming. The course includes models of computation, programming language paradigms ...

Introduction to theory of algorithms and basics of Python programming. Algorithmic thinking: Do you know how to multiply integers? Basic toolkit for the design and analysis of algorithms, and an ...

The algorithm presented here overcomes all of these shortcomings. Most significantly, it exhibits only a linear growth in the solution times based on the number of connections between nodes.

Dynamic programming (DP) algorithms have become indispensable in computational biology, addressing problems that range from sequence alignment and phylogenetic inference to RNA secondary structure ...

Many sequential decision problems can be formulated as Markov decision processes (MDPs) where the optimal value function (or cost-to-go function) can be shown to satisfy a monotone structure in some ...