Algorithms in Nonsmooth Optimization

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 891

Special Issue Editor


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Guest Editor
Faculty of Mathematics, University of Vienna, 1090 Vienna, Austria
Interests: convex analysis; convex optimization; monotone operators; vector optimization
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Special Issue Information

Dear Colleagues,

In this Special Issue of Algorithms, we welcome contributions presenting and/or analyzing iterative methods for solving nonsmooth optimization problems and real-life applications that can be modeled in this way. A preference is given to splitting proximal point type methods and improvements of existing ones as well as iterative procedures for solving convex and nonconvex optimization problems where the involved functions lack differentiability. However, submissions on other types of iterative methods for solving nonsmooth optimization problems are welcome as well. In particular, extensions to multiobjective and vector optimization of known algorithms from scalar optimization are of special interest. Iterative methods for solving monotone inclusions, variational inequalities and equilibrium problems involving nonsmooth functions as well as continuous versions of proximal point type algorithms by means of dynamical systems and differential inclusions will also be considered. The proposed algorithms are expected to be illustrated by means of computational results; however, theoretical schemes of potential high interest can be accepted for publication as well as those hinting toward possible implementation approaches. Possible applications of the proposed iterative methods can be found in fields such as artificial intelligence, machine learning, location and logistics, game theory, image processing, etc.

Dr. Sorin-Mihai Grad
Guest Editor

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Keywords

  • proximal point method
  • convex optimization problem
  • nonsmooth optimization problem
  • splitting technique
  • image processing
  • machine learning
  • stochastic proximal algorithm
  • location theory
  • game theory

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Published Papers (1 paper)

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Research

20 pages, 6256 KiB  
Article
Spiking Neural Networks Optimized by Improved Cuckoo Search Algorithm: A Model for Financial Time Series Forecasting
by Panke Qin, Yongjie Ding, Ya Li, Bo Ye, Zhenlun Gao, Yaxing Liu, Zhongqi Cai and Haoran Qi
Algorithms 2025, 18(5), 262; https://doi.org/10.3390/a18050262 - 2 May 2025
Viewed by 378
Abstract
Financial Time Series Forecasting (TSF) remains a critical challenge in Artificial Intelligence (AI) due to the inherent complexity of financial data, characterized by strong non-linearity, dynamic non-stationarity, and multi-factor coupling. To address the performance limitations of Spiking Neural Networks (SNNs) caused by hyperparameter [...] Read more.
Financial Time Series Forecasting (TSF) remains a critical challenge in Artificial Intelligence (AI) due to the inherent complexity of financial data, characterized by strong non-linearity, dynamic non-stationarity, and multi-factor coupling. To address the performance limitations of Spiking Neural Networks (SNNs) caused by hyperparameter sensitivity, this study proposes an SNN model optimized by an Improved Cuckoo Search (ICS) algorithm (termed ICS-SNN). The ICS algorithm enhances global search capability through piecewise-mapping-based population initialization and introduces a dynamic discovery probability mechanism that adaptively increases with iteration rounds, thereby balancing exploration and exploitation. Applied to futures market price difference prediction, experimental results demonstrate that ICS-SNN achieves reductions of 13.82% in MAE, 21.27% in MSE, and 15.21% in MAPE, while improving the coefficient of determination (R2) from 0.9790 to 0.9822, compared to the baseline SNN. Furthermore, ICS-SNN significantly outperforms mainstream models such as Long Short-Term Memory (LSTM) and Backpropagation (BP) networks, reducing prediction errors by 10.8% (MAE) and 34.9% (MSE), respectively, without compromising computational efficiency. This work highlights that ICS-SNN provides a biologically plausible and computationally efficient framework for complex financial TSF, bridging the gap between neuromorphic principles and real-world financial analytics. The proposed method not only reduces manual intervention in hyperparameter tuning but also offers a scalable solution for high-frequency trading and multi-modal data fusion in future research. Full article
(This article belongs to the Special Issue Algorithms in Nonsmooth Optimization)
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