Special Issue "Stochastic Algorithms and Their Applications"

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Analysis of Algorithms and Complexity Theory".

Deadline for manuscript submissions: 30 April 2022.

Special Issue Editor

Prof. Stephanie Allassonniere
E-Mail Website
Guest Editor
Applied Mathematics and Statistics, School of medicine, Paris Descartes University
Interests: Statistical modeling, stochastic algorithm convergence study, statistics with Riemanian geometry, medical data analysis, decision support systems

Special Issue Information

Dear Colleagues,

Stochastic algorithms are at the core of machine learning and artificial intelligence. Stochastic gradient descent and expectation–maximization algorithms among others offer incredible tools to calibrate statistical models or deep networks. Their studies are of particular interest to ensure garanties on their results, improve their convergence speed and optimize their use in machine learning problems.

The research fields of these algorithms are extremely diverse, ranging from computer vision (CV) to natural language processing (NLP), and targeting emerging applications, such as transport (for example, for autonomous vehicles) or medical data analysis (for example, to propose decision support systems).

We invite you to submit high quality papers for this Special Issue on “Stochastic Algorithms and their Applications”, with subjects covering a whole range of topics, from theory to applications. Both original contributions and review articles will be considered. Submitted articles may focus on any machine learning problem involving this (non-exhaustive) list of topics of interest:

  • Stochastic algorithms convergence, acceleration, optimization, etc.
  • Adaptation of state-of-the-art stochastic algorithms for new CV, NLP and high dimension data problems
  • Application of these stochastic algorithms to environment, transport or medical data and questions

Prof. Stephanie Allassonniere
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Stochastic algorithms convergence, acceleration, optimization, etc.
  • Adaptation of state-of-the-art stochastic algorithms for new CV, NLP and high dimension data problems
  • Application of these stochastic algorithms to environment, transport or medical data and questions

Published Papers (2 papers)

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Research

Article
Approximately Optimal Control of Nonlinear Dynamic Stochastic Problems with Learning: The OPTCON Algorithm
Algorithms 2021, 14(6), 181; https://doi.org/10.3390/a14060181 - 08 Jun 2021
Viewed by 781
Abstract
OPTCON is an algorithm for the optimal control of nonlinear stochastic systems which is particularly applicable to economic models. It delivers approximate numerical solutions to optimum control (dynamic optimization) problems with a quadratic objective function for nonlinear economic models with additive and multiplicative [...] Read more.
OPTCON is an algorithm for the optimal control of nonlinear stochastic systems which is particularly applicable to economic models. It delivers approximate numerical solutions to optimum control (dynamic optimization) problems with a quadratic objective function for nonlinear economic models with additive and multiplicative (parameter) uncertainties. The algorithm was first programmed in C# and then in MATLAB. It allows for deterministic and stochastic control, the latter with open loop (OPTCON1), passive learning (open-loop feedback, OPTCON2), and active learning (closed-loop, dual, or adaptive control, OPTCON3) information patterns. The mathematical aspects of the algorithm with open-loop feedback and closed-loop information patterns are presented in more detail in this paper. Full article
(This article belongs to the Special Issue Stochastic Algorithms and Their Applications)
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Article
An Effective Decomposition-Based Stochastic Algorithm for Solving the Permutation Flow-Shop Scheduling Problem
Algorithms 2021, 14(4), 112; https://doi.org/10.3390/a14040112 - 30 Mar 2021
Cited by 1 | Viewed by 737
Abstract
This paper presents an effective stochastic algorithm that embeds a large neighborhood decomposition technique into a variable neighborhood search for solving the permutation flow-shop scheduling problem. The algorithm first constructs a permutation as a seed using a recursive application of the extended two-machine [...] Read more.
This paper presents an effective stochastic algorithm that embeds a large neighborhood decomposition technique into a variable neighborhood search for solving the permutation flow-shop scheduling problem. The algorithm first constructs a permutation as a seed using a recursive application of the extended two-machine problem. In this method, the jobs are recursively decomposed into two separate groups, and, for each group, an optimal permutation is calculated based on the extended two-machine problem. Then the overall permutation, which is obtained by integrating the sub-solutions, is improved through the application of a variable neighborhood search technique. The same as the first technique, this one is also based on the decomposition paradigm and can find an optimal arrangement for a subset of jobs. In the employed large neighborhood search, the concept of the critical path has been used to help the decomposition process avoid unfruitful computation and arrange only promising contiguous parts of the permutation. In this fashion, the algorithm leaves those parts of the permutation which already have high-quality arrangements and concentrates on modifying other parts. The results of computational experiments on the benchmark instances indicate the procedure works effectively, demonstrating that solutions, in a very short distance of the best-known solutions, are calculated within seconds on a typical personal computer. In terms of the required running time to reach a high-quality solution, the procedure outperforms some well-known metaheuristic algorithms in the literature. Full article
(This article belongs to the Special Issue Stochastic Algorithms and Their Applications)
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