Nonsmooth Optimization and Its Applications

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 789

Special Issue Editor


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Guest Editor
Institute of Innovation, Science and Sustainability, Federation University Australia, Ballarat, VIC 3350, Australia
Interests: nonsmooth optimization and its various applications
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Special Issue Information

Dear Colleagues,

We would like to invite submissions of manuscripts dedicated to recent advancements in the field of nonsmooth optimization and its applications.

Nonsmooth optimization refers to a class of optimization problems where the objective function and/or constraints are not differentiable. It provides powerful tools with which to solve problems that traditional smooth methods cannot tackle effectively. In real-life applications, nonsmoothness often emerges from the inherent nature of systems or models that exhibit abrupt changes, discontinuities, or piecewise behavior. These problems arise in a variety of fields, such as machine learning, image and signal processing, economics and finance, control systems, and others. In some cases, nonsmooth terms are intentionally introduced into models for computational efficiency in order to approximate the otherwise complex smooth behavior of systems.

This Special Issue aims to bring together the latest research on the theoretical principles, algorithmic developments, and practical applications of nonsmooth optimization.

Dr. Nargiz Sultanova
Guest Editor

Manuscript Submission Information

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Keywords

  • nonsmooth optimization
  • non-differentiable optimization
  • subgradient methods
  • signal processing
  • image processing
  • bundle methods
  • machine learning
  • applications of nonsmooth optimization

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Published Papers (1 paper)

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Research

16 pages, 649 KiB  
Article
Adapted B-Spline Quasi-Interpolation for Approximating Piecewise Smooth Functions
by David Levin and Nira Gruberger
Algorithms 2025, 18(6), 335; https://doi.org/10.3390/a18060335 - 3 Jun 2025
Viewed by 153
Abstract
We address the challenge of efficiently approximating piecewise smooth functions, particularly those with jump discontinuities. Given function values on a uniform grid over a domain Ω in Rd, we present a novel B-spline-based approximation framework, using new adaptable quasi-interpolation operators. This [...] Read more.
We address the challenge of efficiently approximating piecewise smooth functions, particularly those with jump discontinuities. Given function values on a uniform grid over a domain Ω in Rd, we present a novel B-spline-based approximation framework, using new adaptable quasi-interpolation operators. This approach integrates discontinuity detection techniques, allowing the quasi-interpolation operator to selectively use points from only one side of a discontinuity in both one- and two-dimensional cases. Among a range of candidate operators, the most suitable quasi-interpolation scheme is chosen to ensure high approximation accuracy and efficiency, while effectively suppressing spurious oscillations in the vicinity of discontinuities. Full article
(This article belongs to the Special Issue Nonsmooth Optimization and Its Applications)
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