Search Results for "mixed integer linear programming"
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Showing 5 open source projects for "mixed integer linear programming"
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Convex.jl
A Julia package for disciplined convex programming
Convex.jl is a Julia package for Disciplined Convex Programming (DCP). Convex.jl makes it easy to describe optimization problems in a natural, mathematical syntax, and to solve those problems using a variety of different (commercial and open-source) solvers. Convex.jl works by transforming the problem—which possibly has nonsmooth, nonlinear constructions like the nuclear norm, the log determinant, and so forth—into a linear optimization problem subject to conic constraints. This... -
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InferOpt.jl
Combinatorial optimization layers for machine learning pipelines
InferOpt.jl is a toolbox for using combinatorial optimization algorithms within machine learning pipelines. It allows you to create differentiable layers from optimization oracles that do not have meaningful derivatives. Typical examples include mixed integer linear programs or graph algorithms. -
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Data Envelopment Analysis using Stata
Develop a user written Data Envelopment Analysis package in Stata.
The goal of this project is to develop a Data Envelopment Analysis(DEA) program using Stata programming language. This is the replacement of "deastata" project that we maintained for the purpose of version management. We call the program package "DEAS" which stands for Data Envelopment Analysis using Stata. DEAS covers the basic models of DEA and extensions including CCR, BCC, SBM, Super-efficiency Model, Allocative Model(Profit, Revenue, Cost), (Global) Malmquist Productivity Index Model, Imprecise DEA, FDH, Additive Model, Virtual Price Model, linear programming(lp), mixed integer linear programming(MILP), and more... -
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A java decision supporting application for the urban planning domain. It is designed as a desktop frontend to a Mixed Integer Programming engine. Actually it uses the GNU Linear Programming Kit (GLPK).
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Bat2015
Bachelor of Science (Informatik)
The toolkit glpk supports methods for mixed integer linear programming (MILP). These methods solve Capital Budgeting Problems (CBP). Unfortunately, glpk does not support any multithreading and there is no feature to distribute problems via network connections. Today, this is a pitiable sight, because modern computer systems are coupled by networks and support multi threading.
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