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

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

Computers can be used to help solve problems. However, before a problem can be tackled, it must first be understood. Computational thinking helps us to solve problems. Designing, creating and refining ...

The author presents a rapidly convergent algorithm to solve the general portfolio problem of maximizing concave utility functions subject to linear constraints. The algorithm is based on an iterative ...

Efficient Algorithm Design redefines algorithms, tracing the evolution of computer science as a discipline bridging natural science and mathematics. Author Masoud Makrehchi, PhD, with his extensive ...

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 ...

This is a preview. Log in through your library . Abstract The trust region approach has been extended to solving nonlinear constrained optimization. Most of these extensions consider only equality ...

MATLAB implementations of various nonlinear programming algorithms. This repository contains MATLAB implementations of a variety of popular nonlinear programming algorithms, many of which can be found ...