PUBLIC MARKS with tags optimization & python

CVXMOD is a Python-based tool for expressing and solving convex optimization problems. It uses CVXOPT as its solver. It is developed by Jacob Mattingley, as PhD work under Stephen Boyd at Stanford University. CVXMOD is primarily a modeling layer for CVXOPT. While it is possible to use CVXOPT directly, CVXMOD makes it faster and easier to build and solve problems. Advanced users who want to see or manipulate how their problems are being solved should consider using CVXOPT directly. Additional features are being added to CVXMOD beyond just modeling. These are currently experimental. CVXMOD has a similar design philosophy to CVX, a convex optimization modeling language for Matlab®, and uses the principles of disciplined convex programming, as developed by Michael Grant, Stephen Boyd and Yinyu Ye.

CVXOPT is a free software package for convex optimization based on the Python programming language. It can be used with the interactive Python interpreter, on the command line by executing Python scripts, or integrated in other software via Python extension modules. Its main purpose is to make the development of software for convex optimization applications straightforward by building on Python's extensive standard library and on the strengths of Python as a high-level programming language.

A comparison of weave with NumPy, Pyrex, Psyco, Fortran and C++ for solving Laplace's equation. This article was originally written by Prabhu Ramachandran.

The PyPy project aims at producing a flexible and fast Python implementation. The guiding idea is to translate a Python-level description of the Python language itself to lower level languages. Rumors have it that the secret goal is being faster-than-C wh

I made a shameless plug on the comp.lang.python newsgroup. A guy called Paul McGuire gave me some great tips on improving my Huffman (en)coding. I’ve implemented most of his suggestions and the results are big. The improvements plus the ones I’d alrea

This page is devoted to various tips and tricks that help improve the performance of your Python programs. Wherever the information comes from someone else, I've tried to identify the source.

In short: run your existing Python software much faster, with no change in your source.

EvoGrid is a componentized framework based on the Zope3 interfaces / adapters system to build Evolutionary Algorithms (aka Genetic Algorithms) by pluging python components together.

EvoGrid is a component-based python framework to build Evolutionary Computation-based Machine Learning algorithms sometime also known as Genetic Algorithms The EvoGrid design is inspired by the idea of "replicators" introduced by Richard Dawkins in his book The Selfish Gene. EvoGrid's replicators can evolve through both classical undirected darwinian evolution or through "intelligent" lamarckian evolution or by a combination of both. In this respect, EvoGrid can be considered a Memetic Computational framework.