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Mathematical Optimization (Programming)

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Related Topics

Mathematical Modeling & AbstractionMulti-Criteria Decision Making (MCDM) & Analysis (MCDA)

Mathematical Analysis (& Analytical Mathematics)

Formal Verification & Analysis Programming Languages

Formal Methods & Formal Verification (FV)

Formal Verifications & Constraint Solvers (Proof Assistants)

Systematic & Combinatorial Search (Classical Search)

Scientific ComputingJulia

Problem Solving & Search-Based Methods

Learning Resources

https://www.nicolasboumal.net/book/IntroOptimManifolds_Boumal_2023.pdf An introduction to optimization on smooth manifolds Nicolas Boumal

https://enpocourses.github.io/enpo811203/optimization-intro/ neos, apmonitor, pyomo,jump,

JuMP.jl

NEOS 如果你想尝试不同的求解器,包括一些商业的求解器,你可以使用NEOS-Server。他们提供了不同格式输入,并且还提供api调用。就是要求不能滥用。

APMonitor和Gekko

GalacticOptim.jl

pyomo

OpenMDAO

Other Resources

Intro

🔗 https://en.wikipedia.org/wiki/Mathematical_optimization

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. It is generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries.

In the more general approach, an optimization problem consists of maximizing or minimizing a real function by systematically choosing input values from within an allowed set and computing the value of the function. The generalization of optimization theory and techniques to other formulations constitutes a large area of applied mathematics.

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