**MIPLearn** is an extensible framework for *Learning-Enhanced Mixed-Integer Optimization*, an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits.
**MIPLearn** is an extensible framework for **Learning-Enhanced Mixed-Integer Optimization**, an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data.
The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see [benchmarks](https://anl-ceeesa.github.io/MIPLearn/problems/) and [references](https://anl-ceeesa.github.io/MIPLearn/about/)).
Features
--------
* **MIPLearn proposes a flexible problem specification format,** which allows users to describe their particular optimization problems to a Learning-Enhanced MIP solver, both from the MIP perspective and from the ML perspective, without making any assumptions on the problem being modeled, the mathematical formulation of the problem, or ML encoding. While the format is very flexible, some constraints are enforced to ensure that it is usable by an actual solver.
* **MIPLearn proposes a flexible problem specification format,** which allows users to describe their particular optimization problems to a Learning-Enhanced MIP solver, both from the MIP perspective and from the ML perspective, without making any assumptions on the problem being modeled, the mathematical formulation of the problem, or ML encoding.
* **MIPLearn provides a reference implementation of a *Learning-Enhanced Solver*,** which can use the above problem specification format to automatically predict, based on previously solved instances, a number of hints to accelerate MIP performance. Currently, the reference solver is able to predict: (i) partial solutions which are likely to work well as MIP starts; (ii) an initial set of lazy constraints to enforce; (iii) affine subspaces where the solution is likely to reside; (iv) variable branching priorities to accelerate the exploration of the branch-and-bound tree. The usage of the solver is very straightforward. The most suitable ML models are automatically selected, trained, cross-validated and applied to the problem with no user intervention.
* **MIPLearn provides a reference implementation of a *Learning-Enhanced Solver*,** which can use the above problem specification format to automatically predict, based on previously solved instances, a number of hints to accelerate MIP performance.
* **MIPLearn provides a set of benchmark problems and random instance generators,** covering applications from different domains, which can be used to quickly evaluate new learning-enhanced MIP techniques in a measurable and reproducible way.
@ -16,35 +21,10 @@ Features
Documentation
-------------
For installation instructions, basic usage and benchmarks results, see the official documentation at:
**MIPLearn** is an extensible framework for *Learning-Enhanced Mixed-Integer Optimization*, an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see [benchmark results](benchmark.md#benchmark-results) and [references](about.md#references) for more details).
**MIPLearn** is an extensible framework for **Learning-Enhanced Mixed-Integer Optimization**, an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data.
The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see [benchmark results](problems.md) and [references](about.md#references) for more details).
@ -30,9 +30,7 @@ Given a simple undirected graph $G=(V,E)$ and weights $w \in \mathbb{R}^V$, the
The class `MaxWeightStableSetGenerator` can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter `fix_graph=True` is provided, one random Erdős-Rényi graph $G_{n,p}$ is generated during the constructor, where $n$ and $p$ are sampled from user-provided probability distributions `n` and `p`. To generate each instance, the generator independently samples each $w_v$ from the user-provided probability distribution `w`. When `fix_graph=False`, a new random graph is generated for each instance, while the remaining parameters are sampled in the same way.
### Benchmark challenges
#### Challenge A
### Challenge A
* Fixed random Erdős-Rényi graph $G_{n,p}$ with $n=200$ and $p=5\%$
* Random vertex weights $w_v \sim U(100, 150)$
@ -96,7 +94,7 @@ By default, all generated prices, weights and capacities are rounded to the near
* Freville, Arnaud, and Gérard Plateau. *An efficient preprocessing procedure for the multidimensional 0–1 knapsack problem.* Discrete applied mathematics 49.1-3 (1994): 189-212.
* Fréville, Arnaud. *The multidimensional 0–1 knapsack problem: An overview.* European Journal of Operational Research 155.1 (2004): 1-21.
<p><strong>MIPLearn</strong> is an extensible framework for <em>Learning-Enhanced Mixed-Integer Optimization</em>, an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see <ahref="benchmark/#benchmark-results">benchmark results</a> and <ahref="about/#references">references</a> for more details).</p>
<p><strong>MIPLearn</strong> is an extensible framework for <strong>Learning-Enhanced Mixed-Integer Optimization</strong>, an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data.</p>
<p>The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see <ahref="problems/">benchmark results</a> and <ahref="about/#references">references</a> for more details).</p>
<p>Given a simple undirected graph $G=(V,E)$ and weights $w \in \mathbb{R}^V$, the problem is to find a stable set $S \subseteq V$ that maximizes $ \sum_{v \in V} w_v$. We recall that a subset $S \subseteq V$ is a <em>stable set</em> if no two vertices of $S$ are adjacent. This is one of Karp's 21 NP-complete problems.</p>
<p>The class <code>MaxWeightStableSetGenerator</code> can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter <code>fix_graph=True</code> is provided, one random Erdős-Rényi graph $G_{n,p}$ is generated during the constructor, where $n$ and $p$ are sampled from user-provided probability distributions <code>n</code> and <code>p</code>. To generate each instance, the generator independently samples each $w_v$ from the user-provided probability distribution <code>w</code>. When <code>fix_graph=False</code>, a new random graph is generated for each instance, while the remaining parameters are sampled in the same way.</p>
@ -230,7 +230,7 @@ from the provided probability distributions <code>K</code> and <code>u</code>.</
<li>Fréville, Arnaud. <em>The multidimensional 0–1 knapsack problem: An overview.</em> European Journal of Operational Research 155.1 (2004): 1-21.</li>
