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The American Bureau of Shipping (ABS) is an American maritime classification society established in 1862. [1] Its stated mission is to promote the security of life, property, and the natural environment, primarily through the development and verification of standards for the design, construction and operational maintenance of marine and offshore assets.
ABSS may refer to: Alamance-Burlington School System, a school district; Administration of the Property of the Holy See, or Amministrazione dei Beni della Santa Sede in Italian; Agent-based social simulation, in social science; Automated Boxing Scoring System, an electronic computer-based scoring system for amateur boxing
Stephen P. Boyd is an American professor and control theorist. He is the Samsung Professor of Engineering, Professor in Electrical Engineering, and professor by courtesy in Computer Science and Management Science & Engineering at Stanford University.
The SciPy scientific library, for instance, uses HiGHS as its LP solver [13] from release 1.6.0 [14] and the HiGHS MIP solver for discrete optimization from release 1.9.0. [15] As well as offering an interface to HiGHS, the JuMP modelling language for Julia [ 16 ] also describes the specific use of HiGHS in its user documentation. [ 17 ]
Mathematical optimization (alternatively spelled optimisation) or mathematical programming is the selection of a best element, with regard to some criteria, from some set of available alternatives. [ 1 ] [ 2 ] It is generally divided into two subfields: discrete optimization and continuous optimization .
Covariance matrix adaptation evolution strategy (CMA-ES) is a particular kind of strategy for numerical optimization. Evolution strategies (ES) are stochastic, derivative-free methods for numerical optimization of non-linear or non-convex continuous optimization problems.
In computer programming, profile-guided optimization (PGO, sometimes pronounced as pogo [1]), also known as profile-directed feedback (PDF) [2] or feedback-directed optimization (FDO), [3] is the compiler optimization technique of using prior analyses of software artifacts or behaviors ("profiling") to improve the expected runtime performance of the program.