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Xpress was originally developed by Dash Optimization, and was acquired by FICO in 2008. [3] Its initial authors were Bob Daniel and Robert Ashford. The first version of Xpress could only solve LPs; support for MIPs was added in 1986. Being released in 1983, Xpress was the first commercial LP and MIP solver running on PCs. [4]
Given a system transforming a set of inputs to output values, described by a mathematical function f, optimization refers to the generation and selection of the best solution from some set of available alternatives, [1] by systematically choosing input values from within an allowed set, computing the value of the function, and recording the best value found during the process.
AMPL Optimization LLC was founded by the inventors of AMPL, Robert Fourer, David Gay, and Brian Kernighan. The new company took over the development and support of the AMPL modeling language from Lucent Technologies, Inc. 2005 AMPL Modeling Language Google group opened [11] 2008 Kestrel: An AMPL Interface to the NEOS Server introduced 2012
nl is a file format for presenting and archiving mathematical programming problems. [1] Initially, this format has been invented for connecting solvers to AMPL. [2] It has also been adopted by other systems such as COIN-OR (as one of the input formats), FortSP (for interacting with external solvers), and Coopr (as one of its output formats).
AMPL earnings call for the period ending September 30, 2024.
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).
Amplitude Inc. was founded in 2014. [3] The company's first product, Amplitude Analytics, was listed as the #22 software product in 2021 by popular review site G2 Crowd . [ 4 ] The addition of their Amplitude Recommend, and Amplitude Experiment products enabled what the company calls its Digital Optimization System.
IPOPT is designed to exploit 1st derivative and 2nd derivative information if provided (usually via automatic differentiation routines in modeling environments such as AMPL). If no Hessians are provided, IPOPT will approximate them using a quasi-Newton methods , specifically a BFGS update .