ANL-CEEESA/MIPLearn
1
0
Fork 0
mirror of https://github.com/ANL-CEEESA/MIPLearn.git synced 2025-12-06 09:28:51 -06:00
Code Issues Packages Projects Releases Wiki Activity

Make package work with persistent solvers; update README.md

Browse Source
This commit is contained in:
Alinson S. Xavier
2020-01-26 11:45:57 -06:00
parent 228d41fb6b
commit 68191837a9
2 changed files with 51 additions and 35 deletions
Download Patch File Download Diff File Expand all files Collapse all files

63
README.md
View File

@@ -5,22 +5,23 @@ MIPLearn
Table of contents
-----------------
* [Features](#features)
* [Installation](#installation)
* [Basic usage](#basic-usage)
* [Using LearningSolver](#using-learningsolver)
* [Selecting the internal MIP solver](#selecting-the-internal-mip-solver)
* [Describing problem instances](#describing-problem-instances)
* [Obtaining heuristic solutions](#obtaining-heuristic-solutions)
* [Saving and loading solver state](#saving-and-loading-solver-state)
* [Solving training instances in parallel](#solving-training-instances-in-parallel)
* [Benchmarking](#benchmarking)
* [Using BenchmarkRunner](#using-benchmarkrunner)
* [Saving and loading benchmark results](#saving-and-loading-benchmark-results)
* [Current Limitations](#current-limitations)
* [References](#references)
* [Authors](#authors)
* [License](#license)
* [Features](#features)
* [Installation](#installation)
* [Basic Usage](#basic-usage)
* [Using LearningSolver](#using-learningsolver)
* [Describing problem instances](#describing-problem-instances)
* [Obtaining heuristic solutions](#obtaining-heuristic-solutions)
* [Saving and loading solver state](#saving-and-loading-solver-state)
* [Solving training instances in parallel](#solving-training-instances-in-parallel)
* [Benchmarking](#benchmarking)
* [Using BenchmarkRunner](#using-benchmarkrunner)
* [Saving and loading benchmark results](#saving-and-loading-benchmark-results)
* [Customization](#customization)
* [Selecting the internal MIP solver](#selecting-the-internal-mip-solver)
* [Current Limitations](#current-limitations)
* [References](#references)
* [Authors](#authors)
* [License](#license)
Features
--------
@@ -60,17 +61,6 @@ for instance in all_instances:
During the first call to `solver.solve(instance)`, the solver will process the instance from scratch, since no historical information is available, but it will already start gathering information. By calling `solver.fit()`, we instruct the solver to train all the internal Machine Learning models based on the information gathered so far. As this operation can be expensive, it may be performed after a larger batch of instances has been solved, instead of after every solve. After the first call to `solver.fit()`, subsequent calls to `solver.solve(instance)` will automatically use the trained Machine Learning models to accelerate the solution process.
### Selecting the internal MIP solver
By default, `LearningSolver` uses Gurobi as its internal MIP solver. Alternative solvers can be specified through the `parent_solver`a argument, as follows. To select CPLEX, for example:
```python
from miplearn import LearningSolver
import pyomo.environ as pe
cplex = pe.SolverFactory("cplex")
solver = LearningSolver(parent_solver=cplex)
```
### Describing problem instances
Instances to be solved by `LearningSolver` must derive from the abstract class `miplearn.Instance`. The following three abstract methods must be implemented:
@@ -198,6 +188,25 @@ benchmark.load_fit("training_data.bin")
benchmark.parallel_solve(test_instances)
```
Customization
-------------
### Selecting the internal MIP solver
By default, `LearningSolver` uses [Gurobi](https://www.gurobi.com/) as its internal MIP solver. Alternative solvers can be specified through the `internal_solver_factory` constructor argument. This argument should provide a function (with no arguments) that constructs, configures and returns the desired solver. To select CPLEX, for example:
```python
from miplearn import LearningSolver
import pyomo.environ as pe
def cplex_factory():
cplex = pe.SolverFactory("cplex_persistent")
cplex.options["threads"] = 4
return cplex
solver = LearningSolver(internal_solver_factory=cplex_factory)
```
Current Limitations
-------------------