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) .

Goal programming is a very powerful technique for solving multiple objective optimisation problems. It has been successfully applied to numerous diverse real life problems. In this paper a Taboo ...

Probabilistic programming has emerged as a powerful paradigm that integrates uncertainty directly into computational models. By embedding probabilistic constructs into conventional programming ...

In a companion paper (Godfrey and Powell 2002) we introduced an adaptive dynamic programming algorithm for stochastic dynamic resource allocation problems, which arise in the context of logistics and ...

Probabilistic Programming and Inference Algorithms Publication Trend The graph below shows the total number of publications each year in Probabilistic Programming and Inference Algorithms.

View on Coursera Course Decription This course introduces number-theory based cryptography, basics of quantum algorithms and advanced data-structures. Learning Outcomes Understand how basic ...

Quantum computing is in the cloud, and you don’t need a degree in advanced physics to run your own programs. For the first time, anyone with a web browser will soon be able to log in and run basic ...