This course continues our data structures and algorithms specialization by focussing on the use of linear and integer programming formulations for solving algorithmic problems that seek optimal ...

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 ...

Ever wondered how computers multiply huge numbers with hundreds or even thousands of digits? The process may seem simple, but ...

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 ...

1 Describe key models of computation and associated programming language paradigms based on them. 2 Evaluate the advantages and disadvantages of various programming languages for different ...

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 ...