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# MatrixOptim | ||
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[![Build Status](https://travis-ci.org/edxu96/MatrixOptim.svg?branch=master)](https://travis-ci.org/edxu96/MatrixOptim) | ||
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Every optimization problem can be written in matrix form. For some problems, it may seems trivial, but it's coherent and easy to understand. Secondly, when it comes to algorithms to solve them, it's more explicit in matrix form. Thirdly, the abstraction algorithm for problem modeling helps a lot in understanding. | ||
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## 1, What? and Why? | ||
## Introduction | ||
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The MILP can be formulated in the following matrixes: | ||
The MILP can always be formulated in the following matrixes: | ||
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``` | ||
min vecFf' * vecXx + vecGg' * vecYy | ||
s.t. matAa1 * vecXx + matCc1 * vecYy <= vecBb1 | ||
matAa2 * vecXx + matCc2 * vecYy <= vecBb2 | ||
vecXx in R | ||
vecYy in Z+ | ||
min vec_c' * vec_x + vec_f' * vec_y | ||
s.t. mat_aCap * vec_x + mat_bCap * vec_y <= vec_b | ||
vec_x in R | ||
vec_y in Z | ||
``` | ||
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There are two directions for matrix optimization to develop: make modeling easier and solving faster. | ||
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### 1.1, Specialization | ||
In this package, there are formulated algorithm for four kinds of optimization problems, and two decomposition algorithms for faster MILP solving. | ||
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In this package, there are formulation algorithm for three kinds of optimization problems, and two decomposition | ||
algorithms for faster MILP solving. | ||
## To Check | ||
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- Robust Optimization: [edxu96/RobustOptim](https://github.com/edxu96/RobustOptimization) | ||
- Stochastic Optimization: [edxu96/StochasticOptim](https://github.com/edxu96/StochasticOptim) | ||
- [x] Linear Programming | ||
- [x] Mixed Integer Linear Programming | ||
- [ ] Robust Optimization | ||
- [ ] Stochastic Optimization | ||
- [ ] Benders Decomposition | ||
- [ ] Dantzig-Wolfe Decomposition Family | ||
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### 1.2, Decomposition | ||
## How to Use | ||
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Two decomposition algorithms are: | ||
- Benders Decomposition | ||
- Dantzig-Wolfe Family Decomposition | ||
``` | ||
julia> Pkg.clone("https://github.com/edxu96/MatrixOptim.git") | ||
``` | ||
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## 2, How to Use | ||
Or | ||
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``` | ||
julia> Pkg.clone("https://github.com/edxu96/MatrixOptim.git") | ||
(v1.1) pkg> add https://github.com/edxu96/MatrixOptim.git | ||
``` | ||
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Besides, remember to update it regularly after installation: | ||
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``` | ||
julia> Pkg.update("MatrixOptim") | ||
``` | ||
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Or | ||
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``` | ||
(v1.1) pkg> update MatrixOptim | ||
``` | ||
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## 3, More Info | ||
## More Info | ||
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[edxu96/MatrixOptim/wiki](https://github.com/edxu96/MatrixOptim/wiki/1-Home) | ||
- [edxu96/MatrixOptim/wiki](https://github.com/edxu96/MatrixOptim/wiki/1-Home) | ||
- [中文详解](https://github.com/edxu96/MatrixOptim/wiki/9-zh) | ||
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## 4, Contributers | ||
## Contributers | ||
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Edward Xu (<[email protected]>) (<https://edxu96.github.io>) | ||
Edward J. Xu (<[email protected]>) (<https://edxu96.github.io>) |
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@JuliaRegistrator register()
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Registration pull request updated: JuliaRegistries/General/2622
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if Julia TagBot is installed, or can be done manually through the github interface, or via: