From 622d132ba2c3e5cf5e0be1a5f654e328a2080222 Mon Sep 17 00:00:00 2001 From: Alinson S Xavier Date: Thu, 14 Jan 2021 18:35:17 -0600 Subject: [PATCH] Update package description --- README.md | 8 +++----- docs/index.md | 8 +++----- 2 files changed, 6 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index f2439a7..75d23fc 100644 --- a/README.md +++ b/README.md @@ -11,12 +11,10 @@

-**MIPLearn** is an extensible framework for solving discrete optimization problems using a combination of Mixed-Integer Linear Programming (MIP) and Machine Learning (ML). +**MIPLearn** is an extensible framework for solving discrete optimization problems using a combination of Mixed-Integer Linear Programming (MIP) and Machine Learning (ML). The framework uses ML methods to automatically identify patterns in previously solved instances of the problem, then uses these patterns to accelerate the performance of conventional state-of-the-art MIP solvers (such as CPLEX, Gurobi or XPRESS). -The framework uses ML methods to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, then uses these patterns to accelerate the performance of conventional state-of-the-art MIP solvers (such as CPLEX, Gurobi or XPRESS). - -* Unlike pure ML methods, **MIPLearn** is able not only find high-quality solutions to discrete optimization problems, but it can also prove that the solutions are optimal and feasible. -* Unlike conventional MIP solvers, **MIPLearn** can take full advantage of very specific observations that happen to be true in a particular family of instances (such as the observation that a particular constraint is typically redundant, or that a particular variable typically assumes a certain value). +Unlike pure ML methods, MIPLearn is not only able to find high-quality solutions to discrete optimization problems, but it can also prove the optimality and feasibility of these solutions. +Unlike conventional MIP solvers, MIPLearn can take full advantage of very specific observations that happen to be true in a particular family of instances (such as the observation that a particular constraint is typically redundant, or that a particular variable typically assumes a certain value). For certain classes of problems, this approach has been shown to provide significant performance benefits (see [benchmarks](https://anl-ceeesa.github.io/MIPLearn/0.1/problems/) and [references](https://anl-ceeesa.github.io/MIPLearn/0.1/about/)). diff --git a/docs/index.md b/docs/index.md index ebd3f9e..c8b504d 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,11 +1,9 @@ # MIPLearn -**MIPLearn** is an extensible framework for solving discrete optimization problems using a combination of Mixed-Integer Linear Programming (MIP) and Machine Learning (ML). +**MIPLearn** is an extensible framework for solving discrete optimization problems using a combination of Mixed-Integer Linear Programming (MIP) and Machine Learning (ML). The framework uses ML methods to automatically identify patterns in previously solved instances of the problem, then uses these patterns to accelerate the performance of conventional state-of-the-art MIP solvers (such as CPLEX, Gurobi or XPRESS). -The framework uses ML methods to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, then uses these patterns to accelerate the performance of conventional state-of-the-art MIP solvers (such as CPLEX, Gurobi or XPRESS). - -* Unlike pure ML methods, **MIPLearn** is able not only find high-quality solutions to discrete optimization problems, but it can also prove that the solutions are optimal and feasible. -* Unlike conventional MIP solvers, **MIPLearn** can take full advantage of very specific observations that happen to be true in a particular family of instances (such as the observation that a particular constraint is typically redundant, or that a particular variable typically assumes a certain value). +Unlike pure ML methods, MIPLearn is not only able to find high-quality solutions to discrete optimization problems, but it can also prove the optimality and feasibility of these solutions. +Unlike conventional MIP solvers, MIPLearn can take full advantage of very specific observations that happen to be true in a particular family of instances (such as the observation that a particular constraint is typically redundant, or that a particular variable typically assumes a certain value). For certain classes of problems, this approach has been shown to provide significant performance benefits (see [benchmarks](problems.md) and [references](about.md)).