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jiedxu committed Aug 12, 2019
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# MatrixOptim

[![Build Status](https://travis-ci.org/edxu96/MatrixOptim.svg?branch=master)](https://travis-ci.org/edxu96/MatrixOptim)
Expand All @@ -10,44 +11,59 @@ __矩阵优化__:通过矩阵表示混合整数线性规划,鲁棒(抗差

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.

## 1, What? and Why?
## Introduction

The MILP can be formulated in the following matrixes:
The MILP can always be formulated in the following matrixes:

```
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
```

There are two directions for matrix optimization to develop: make modeling easier and solving faster.

### 1.1, Specialization
In this package, there are formulated algorithm for four kinds of optimization problems, and two decomposition algorithms for faster MILP solving.

In this package, there are formulation algorithm for three kinds of optimization problems, and two decomposition
algorithms for faster MILP solving.
## To Check

- 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

### 1.2, Decomposition
## How to Use

Two decomposition algorithms are:
- Benders Decomposition
- Dantzig-Wolfe Family Decomposition
```
julia> Pkg.clone("https://github.com/edxu96/MatrixOptim.git")
```

## 2, How to Use
Or

```
julia> Pkg.clone("https://github.com/edxu96/MatrixOptim.git")
(v1.1) pkg> add https://github.com/edxu96/MatrixOptim.git
```

Besides, remember to update it regularly after installation:

```
julia> Pkg.update("MatrixOptim")
```

Or

```
(v1.1) pkg> update MatrixOptim
```

## 3, More Info
## More Info

[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)

## 4, Contributers
## Contributers

Edward Xu (<edxu96@outlook.com>) (<https://edxu96.github.io>)
Edward J. Xu (<edxu96@outlook.com>) (<https://edxu96.github.io>)

2 comments on commit 7fc0a37

@jiedxu
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@jiedxu jiedxu commented on 7fc0a37 Aug 15, 2019

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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:

git tag -a v0.1.0 -m "<description of version>" 7fc0a374ca80ce2c5ce9502112f41115c8581003
git push origin v0.1.0

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