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IPOPT, short for "Interior Point OPTimizer, pronounced I-P-Opt", is a software library for large scale nonlinear optimization of continuous systems.. It is written in C++ (after migrating from Fortran and C) and is released under the EPL (formerly CPL).
It includes the Zero Redundancy Optimizer (ZeRO) for training models with 1 trillion or more parameters. [4] Features include mixed precision training, single-GPU, multi-GPU, and multi-node training as well as custom model parallelism. The DeepSpeed source code is licensed under MIT License and available on GitHub. [5]
HiGHS has an interior point method implementation for solving LP problems, based on techniques described by Schork and Gondzio (2020). [10] It is notable for solving the Newton system iteratively by a preconditioned conjugate gradient method, rather than directly, via an LDL* decomposition. The interior point solver's performance relative to ...
Given a transformation between input and output values, described by a mathematical function, optimization deals with generating and selecting the best solution from some set of available alternatives, by systematically choosing input values from within an allowed set, computing the output of the function and recording the best output values found during the process.
An object code optimizer, sometimes also known as a post pass optimizer or, for small sections of code, peephole optimizer, forms part of a software compiler. It takes the output from the source language compile step - the object code or binary file - and tries to replace identifiable sections of the code with replacement code that is more ...
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Launched in 2001, ionCube PHP Accelerator (PHPA) was the first freely available PHP accelerator to compete with the commercial Zend Cache product. Created before ionCube Ltd. was founded and at a time when the performance of PHP was regarded as lackluster when compared to other popular web programming languages, [citation needed] PHPA showed that PHP can compete with other languages ...
The IBM ILOG CPLEX Optimizer solves integer programming problems, very large [3] linear programming problems using either primal or dual variants of the simplex method or the barrier interior point method, convex and non-convex quadratic programming problems, and convex quadratically constrained problems (solved via second-order cone programming, or SOCP).