Zobrazeno 1 - 10
of 13 690
pro vyhledávání: '"A. Frangioni"'
Although nearly 20 years have passed since its conception, the feasibility pump algorithm remains a widely used heuristic to find feasible primal solutions to mixed-integer linear problems. Many extensions of the initial algorithm have been proposed.
Externí odkaz:
http://arxiv.org/abs/2411.03535
Publikováno v:
In: Pardalos, P.M., Prokopyev, O.A. (eds) Encyclopedia of Optimization. Springer, Cham. (2023)
The field of algorithmic optimization has significantly advanced with the development of methods for the automatic configuration of algorithmic parameters. This article delves into the Algorithm Configuration Problem, focused on optimizing parametriz
Externí odkaz:
http://arxiv.org/abs/2403.00898
We discuss the issue of finding a good mathematical programming solver configuration for a particular instance of a given problem, and we propose a two-phase approach to solve it. In the first phase we learn the relationships between the instance, th
Externí odkaz:
http://arxiv.org/abs/2401.05041
We propose a methodology, based on machine learning and optimization, for selecting a solver configuration for a given instance. First, we employ a set of solved instances and configurations in order to learn a performance function of the solver. Sec
Externí odkaz:
http://arxiv.org/abs/2401.04237
In recent years, the integration of Machine Learning (ML) models with Operation Research (OR) tools has gained popularity across diverse applications, including cancer treatment, algorithmic configuration, and chemical process optimization. In this d
Externí odkaz:
http://arxiv.org/abs/2307.07457
Autor:
Cacciola, Matteo, Frangioni, Antonio, Asgharian, Masoud, Ghaffari, Alireza, Nia, Vahid Partovi
Deep learning models are dominating almost all artificial intelligence tasks such as vision, text, and speech processing. Stochastic Gradient Descent (SGD) is the main tool for training such models, where the computations are usually performed in sin
Externí odkaz:
http://arxiv.org/abs/2301.01651
Our study is motivated by the solution of Mixed-Integer Non-Linear Programming (MINLP) problems with separable non-convex functions via the Sequential Convex MINLP technique, an iterative method whose main characteristic is that of solving, for bound
Externí odkaz:
http://arxiv.org/abs/2211.15450
Autor:
Bacci, Tiziano1 (AUTHOR) tiziano.bacci@iasi.cnr.it, Frangioni, Antonio2 (AUTHOR) frangio@di.unipi.it, Gentile, Claudio1 (AUTHOR) gentile@iasi.cnr.it, Tavlaridis-Gyparakis, Kostas2 (AUTHOR) kostas.tavlaridis@gmail.com
Publikováno v:
Operations Research. Sep/Oct2024, Vol. 72 Issue 5, p2153-2167. 15p.
Publikováno v:
Il Foro Italiano, 1991 Jan 01. 114, 309/310-321/322.
Externí odkaz:
https://www.jstor.org/stable/23186374
Publikováno v:
Il Foro Italiano, 1898 Jan 01. 23, 153/154-157/158.
Externí odkaz:
https://www.jstor.org/stable/23102208