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

Software may appear to operate without bias because it strictly uses computer code to reach conclusions. But a team of computer scientists has discovered a way to find out if an algorithm used for ...

In this paper, we consider two different formulations (one is smooth and the other one is nonsmooth) for solving linear matrix inequalities (LMIs), an important class of semidefinite programming (SDP) ...

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

[Kory] has been writing genetic algorithms for a few months now. This in itself isn’t anything unique or exceptional, except for what he’s getting these genetic algorithms to do. [Kory] has been using ...

Column dropping procedures are provided for the Generalized Programming algorithm. Nonbasic columns may not be dropped, however, from the restricted master at every iteration for all problems.

Computer scientists have shown that an important class of artificial intelligence (AI) algorithms could be implemented using chemical reactions. In the long term, they say, such theoretical ...