Search Results for "basic programming software"

...Both libraries work in arbitrary dimensions and signatures of metric. Additionally, there are some 2D/3D-specific routines including a visualisation to PostScript files through Asymptote (http://asymptote.sourcefourge.net) software. The source is written in literate programming NoWeb.

EasyCSP is an open-source Java library for Constraint Satisfaction Programming. EasyCSP offers search algorithms for both CSPs and CSOPs. EasyCSP supports CSPs to be formalized using objects or int expressions. EasyCSP on GitHub: http://github.com/cordisvictor/easycsp-lib

The key difference with current graph deep learning libraries, such as PyTorch Geometric (PyG) and Deep Graph Library (DGL), is that, while PyG and DGL support basic graph deep learning operations, DIG provides a unified testbed for higher level, research-oriented graph deep learning tasks, such as graph generation, self-supervised learning, explainability, 3D graphs, and graph out-of-distribution. If you are working or plan to work on research in graph deep learning, DIG enables you to...

The toolkit glpk supports methods for mixed integer linear programming (MILP). These methods solve Capital Budgeting Problems (CBP). Unfortunately, glpk does not support any multithreading and there is no feature to distribute problems via network connections. Today, this is a pitiable sight, because modern computer systems are coupled by networks and support multi threading. We create a distributed system with Apache thrift and the C-API of glpk. Now, it is possible to use as many cores in...

Make AsciiDoc part of your literate programming tool set. With eWEB you can weave and tangle literate programs written as AsciiDoc documents, using embedded WEB code snippets.

This is module for basic computation with floating point numbers. Numbers can have very wide mantissa for good precision, for realize "arbitrary-precision arithmetic". Module was adapt for BCB and MSVC compilers. Russian comments.

DBNL is a cross-platform library that offers a variety of implementations of Bayesian networks and machine learning algorithms. It is a flexible library that covers all aspects of Bayesian netwoks from representation to reasoning and learning. It allows you to create simple static networks as well as complex temporal models with changing structure. It can handle highly non-linear dependencies between multivariate random variables. The particle based inference can answer arbitrary...