Aims and scope

Optimization Methods and Software

publishes refereed papers on the latest developments in the theory and realization of optimization methods, with particular emphasis on the interface between software development and algorithm design.

Topics include:

    Theory, implementation and performance evaluation of algorithms and computer codes for linear, nonlinear, discrete, stochastic optimization and optimal control. This includes in particular conic, semi-definite, mixed integer, network, non-smooth, multi-objective and global optimization by deterministic or nondeterministic algorithms.
    Algorithms and software for complementarity, variational inequalities and equilibrium problems, and also for solving inverse problems, systems of nonlinear equations and the numerical study of parameter dependent operators.
    Various aspects of efficient and user-friendly implementations: e.g. automatic differentiation, massively parallel optimization, distributed computing, on-line algorithms, error sensitivity and validity analysis, problem scaling, stopping criteria and symbolic numeric interfaces.
    Theoretical studies with clear potential for applications and successful applications of specially adapted optimization methods and software to fields like engineering, machine learning, data mining, economics, finance, biology, or medicine. These submissions should not consist solely of the straightforward use of standard optimization techniques.

Special Issue on Derivative-free and Blackbox Optimization
截稿日期: 2024-12-15

Special Issue Editor(s)

Ana Luísa Custódio, Department of Mathematics and NOVA Math, NOVA School of Science and Technology, Portugal
alcustodio@fct.unl.pt

Sébastien Le Digabel, GERAD and Département de Mathématiques et de Génie Industriel, Polytechnique Montréal, Canada
sebastien.le-digabel@polymtl.ca

Giampaolo Liuzzi, Dipartimento di Ingegneria Informatica, Automatica e Gestionale "A. Ruberti","Sapienza" Università di Roma, Italy
liuzzi@diag.uniroma1.it

Margherita Porcelli, Dipartimento di Ingegneria Industriale, Università degli Studi di Firenze, Italy
margherita.porcelli@gmail.com

Francesco Rinaldi, Dipartimento di Matematica "Tullio Levi-Civita", Università di Padova, Italy
rinaldi@math.unipd.it

In the last decades, the increase in computational power led to the development of even more sophisticated and complex models that need to be optimized. Frequently, function evaluation is the result of a time-consuming simulation, often nonsmooth or subject to numerical noise, preventing the use of derivative-based methods. Thus, Derivative-free and Simulation Based Optimization have emerged as important domains in nonlinear optimization, attracting researchers and practitioners to the development and analysis of efficient and robust algorithms, able to tackle these challenging problems.

The present special issue appears on occasion of the 2nd Derivative-free Optimization Symposium, that will be held from 24-28 June 2024 at University of Padova, Italy, and intends to contribute for the advancement of the knowledge in this scientific domain.  With a first edition held in 2022, at University of British Columbia, Canada, this series of meetings of single-track presentations intends to gather the Derivative-free Optimization community, promoting collaborations and the integration of new members.

Main topics include, but are not restricted to:

    Derivative-free Optimization
    Simulation-based Optimization
    Blackbox Optimization
    Software development
    Worst-case complexity analysis
    Multiobjective Derivative-free Optimization
    Global Derivative-free Optimization
    Large-scale Derivative-free Optimization
    Applications