Special Issue "Nonsmooth Optimization in honor of the 60th birthday of Adil M. Bagirov"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (31 October 2019).

Special Issue Editors

Dr. Napsu Karmitsa
E-Mail Website
Guest Editor
Department of Mathematics and Statistics, University of Turku, FI-20014 Turku, Finland
Interests: nonsmooth optimization; large-scale optimization; data mining; molecular modeling
Dr. Sona Taheri
E-Mail Website
Guest Editor
School of Science, Engineering and Information Technology, Federation University Australia, 3350 VIC, Australia
Interests: nonsmooth optimization; data mining; machine learning

Special Issue Information

Dear Colleagues,

Nonsmooth optimization (NSO) refers to the general problem of minimizing (or maximizing) functions that have discontinuous gradients. These types of functions arise in many applied fields, for instance, in image denoising, optimal shape design, computational chemistry and physics, machine learning, and data mining including cluster analysis, classification and regression. In most of these applications NSO approaches lead to a significant reduction in the number of decision variables in comparison with any other approaches, and thus facilitate the design of efficient algorithms for their solution. In addition, various real-world problems can be modeled more realistic as an NSO problem; the robust formulation of a system may lead to solving an NSO problem; and even solving a difficult smooth (continuously differentiable) problem sometimes requires the use of NSO techniques in order to either reduce the problem’s size or simplify its structure. These are some of the main reasons for the increased attraction to nonsmooth analysis and optimization during the past few years. Despite the considerable developments in NSO, the lack of numerically effective methods is still evident and their applications to real-world problems is somewhat limited. The aim of this Special Issue is to collect together the most recent techniques and applications in the area of NSO.

We invite you to submit your original and unpublished research papers to the Special Issue on nonsmooth optimization. We have a special interest in research works focusing on various new NSO algorithms including those applying the special structure of nonsmooth problems (DC, partial smoothness, sparsity, etc.) and the applications of NSO including (but not limited to) image denoising, machine learning, and data mining

Dr. Napsu Karmitsa
Dr. Sona Taheri
Guest Editors

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 1000 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

  • Nonsmooth optimization
  • Non-differentiable programming
  • Subgradient methods
  • Bundle methods
  • Applications of nonsmooth optimization

Published Papers (2 papers)

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Research

Open AccessFeature PaperArticle
Planning the Schedule for the Disposal of the Spent Nuclear Fuel with Interactive Multiobjective Optimization
Algorithms 2019, 12(12), 252; https://doi.org/10.3390/a12120252 - 25 Nov 2019
Abstract
Several countries utilize nuclear power and face the problem of what to do with the spent nuclear fuel. One possibility, which is under the scope in this paper, is to dispose of the fuel assemblies in the disposal facility. Before the assemblies can [...] Read more.
Several countries utilize nuclear power and face the problem of what to do with the spent nuclear fuel. One possibility, which is under the scope in this paper, is to dispose of the fuel assemblies in the disposal facility. Before the assemblies can be disposed of, they must cool down their decay heat power in the interim storage. Next, they are loaded into canisters in the encapsulation facility, and finally, the canisters are placed in the disposal facility. In this paper, we model this process as a nonsmooth multiobjective mixed-integer nonlinear optimization problem with the minimization of nine objectives: the maximum number of assemblies in the storage, maximum storage time, average storage time, total number of canisters, end time of the encapsulation, operation time of the encapsulation facility, the lengths of disposal and central tunnels, and total costs. As a result, we obtain the disposal schedule i.e., amount of canisters disposed of periodically. We introduce the interactive multiobjective optimization method using the two-slope parameterized achievement scalarizing functions which enables us to obtain systematically several different Pareto optimal solutions from the same preference information. Finally, a case study adapting the disposal in Finland is given. The results obtained are analyzed in terms of the objective values and disposal schedules. Full article
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Open AccessFeature PaperArticle
SVM-Based Multiple Instance Classification via DC Optimization
Algorithms 2019, 12(12), 249; https://doi.org/10.3390/a12120249 - 23 Nov 2019
Abstract
A multiple instance learning problem consists of categorizing objects, each represented as a set (bag) of points. Unlike the supervised classification paradigm, where each point of the training set is labeled, the labels are only associated with bags, while the labels of the [...] Read more.
A multiple instance learning problem consists of categorizing objects, each represented as a set (bag) of points. Unlike the supervised classification paradigm, where each point of the training set is labeled, the labels are only associated with bags, while the labels of the points inside the bags are unknown. We focus on the binary classification case, where the objective is to discriminate between positive and negative bags using a separating surface. Adopting a support vector machine setting at the training level, the problem of minimizing the classification-error function can be formulated as a nonconvex nonsmooth unconstrained program. We propose a difference-of-convex (DC) decomposition of the nonconvex function, which we face using an appropriate nonsmooth DC algorithm. Some of the numerical results on benchmark data sets are reported. Full article
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