OMLT: Optimization & Machine Learning Toolkit
- URL: http://arxiv.org/abs/2202.02414v1
- Date: Fri, 4 Feb 2022 22:23:45 GMT
- Title: OMLT: Optimization & Machine Learning Toolkit
- Authors: Francesco Ceccon, Jordan Jalving, Joshua Haddad, Alexander Thebelt,
Calvin Tsay, Carl D. Laird, Ruth Misener
- Abstract summary: The optimization and machine learning toolkit (OMLT) is an open-source software package incorporating neural network and gradient-boosted tree surrogate models.
We discuss the advances in optimization technology that made OMLT possible and show how OMLT seamlessly integrates with the algebraic modeling language Pyomo.
- Score: 54.58348769621782
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The optimization and machine learning toolkit (OMLT) is an open-source
software package incorporating neural network and gradient-boosted tree
surrogate models, which have been trained using machine learning, into larger
optimization problems. We discuss the advances in optimization technology that
made OMLT possible and show how OMLT seamlessly integrates with the algebraic
modeling language Pyomo. We demonstrate how to use OMLT for solving
decision-making problems in both computer science and engineering.
Related papers
- ILILT: Implicit Learning of Inverse Lithography Technologies [5.373749225521622]
We propose an implicit learning ILT: ILILT, which leverages the implicit learning inputs to ground-conditioned ILT solutions, significantly improving efficiency and quality.
arXiv Detail & Related papers (2024-05-06T15:49:46Z) - Supercompiler Code Optimization with Zero-Shot Reinforcement Learning [63.164423329052404]
We present CodeZero, an artificial intelligence agent trained extensively on large data to produce effective optimization strategies instantly for each program in a single trial of the agent.
Our methodology kindles the great potential of artificial intelligence for engineering and paves the way for scaling machine learning techniques in the realm of code optimization.
arXiv Detail & Related papers (2024-04-24T09:20:33Z) - Machine Learning Augmented Branch and Bound for Mixed Integer Linear
Programming [11.293025183996832]
Mixed Linear Programming (MILP) offers a powerful modeling language for a wide range of applications.
In recent years, there has been an explosive development in the use of machine learning algorithms for enhancing all main tasks involved in the branch-and-bound algorithm.
In particular, we give detailed attention to machine learning algorithms that automatically optimize some metric of branch-and-bound efficiency.
arXiv Detail & Related papers (2024-02-08T09:19:26Z) - Machine Learning Insides OptVerse AI Solver: Design Principles and
Applications [74.67495900436728]
We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver.
We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem.
We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
arXiv Detail & Related papers (2024-01-11T15:02:15Z) - VeLO: Training Versatile Learned Optimizers by Scaling Up [67.90237498659397]
We leverage the same scaling approach behind the success of deep learning to learn versatiles.
We train an ingest for deep learning which is itself a small neural network that ingests and outputs parameter updates.
We open source our learned, meta-training code, the associated train test data, and an extensive benchmark suite with baselines at velo-code.io.
arXiv Detail & Related papers (2022-11-17T18:39:07Z) - Large Scale Mask Optimization Via Convolutional Fourier Neural Operator
and Litho-Guided Self Training [54.16367467777526]
We present a Convolutional Neural Operator (CFCF) that can efficiently learn mask tasks.
For the first time, our machine learning-based framework outperforms state-of-the-art numerical mask dataset.
arXiv Detail & Related papers (2022-07-08T16:39:31Z) - How to effectively use machine learning models to predict the solutions
for optimization problems: lessons from loss function [0.0]
This paper aims to predict a good solution for constraint optimization problems using advanced machine learning techniques.
It extends the work of citeabbasi 2020predicting to use machine learning models for predicting the solution of large-scaled optimization models.
arXiv Detail & Related papers (2021-05-14T02:14:00Z) - Learning to Optimize: A Primer and A Benchmark [94.29436694770953]
Learning to optimize (L2O) is an emerging approach that leverages machine learning to develop optimization methods.
This article is poised to be the first comprehensive survey and benchmark of L2O for continuous optimization.
arXiv Detail & Related papers (2021-03-23T20:46:20Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.