Applications – Science and Engineering

Generalized Inexact Proximal Algorithms: Habit’s/ Routine’s Formation with Resistance to Change, following Worthwhile Changes

  • Glaydston Bento
  • Antoine Soubeyran
    • Categories Applications - Science and Engineering, Nonsmooth Optimization, Unconstrained Optimization Tags

    This paper shows how, in a quasi metric space, an inexact proximal algorithm with a generalized perturbation term appears to be a nice tool for Behavioral Sciences (Psychology, Economics, Management, Game theory,…). More precisely, the new perturbation term represents an index of resistance to change, defined as a “curved enough” function of the quasi distance … Read more

    Mathematical Programming techniques in Water Network Optimization

  • Cristiana Bragalli
  • Claudia D'Ambrosio
  • Andrea Lodi
  • Sven Wiese
    • Categories (Mixed) Integer Nonlinear Programming, Civil and Environmental Engineering Tags , , ,

    In this article we survey mathematical programming approaches to problems in the field of water network optimization. Predominant in the literature are two different, but related problem classes. One can be described by the notion of network design, while the other is more aptly termed by network operation. The basic underlying model in both cases … Read more

    Finding the largest low-rank clusters with Ky Fan 2-k-norm and l1-norm

  • Xuan Vinh Doan
  • Stephen A. Vavasis
    • Categories Convex Optimization, Data-Mining Tags , ,

    We propose a convex optimization formulation with the Ky Fan 2-k-norm and l1-norm to fi nd k largest approximately rank-one submatrix blocks of a given nonnegative matrix that has low-rank block diagonal structure with noise. We analyze low-rank and sparsity structures of the optimal solutions using properties of these two matrix norms. We show that, under … Read more

    Inverse optimal control with polynomial optimization

  • Didier Henrion
  • Jean-Bernard Lasserre
  • Edouard Pauwels
    • Categories Control Applications, Semi-definite Programming

    In the context of optimal control, we consider the inverse problem of Lagrangian identification given system dynamics and optimal trajectories. Many of its theoretical and practical aspects are still open. Potential applications are very broad as a reliable solution to the problem would provide a powerful modeling tool in many areas of experimental science. We … Read more

    Topology Optimization for Magnetic Circuits dedicated to Electric Propulsion

  • Carole Henaux
  • Frédéric Messine
  • Satafa Sanogo
  • Raphael Vilamot
    • Categories Applications - Science and Engineering, Optimization of Systems modeled by PDEs Tags , , , , , ,

    Abstract—In this paper, we present a method to solve inverse problems of electromagnetic circuit design which are formulated as a topology optimization problem. Indeed, by imposing the magnetic field inside a region, we search a best material distribution into variable domains. In order to perform this, we minimize the quadratic error between the prescribed magnetic … Read more

    Exploiting derivative-free local searches in DIRECT-type algorithms for global optimization

  • Giampaolo Liuzzi
  • Stefano Lucidi
  • Veronica Piccialli
    • Categories Biomedical Applications, Global Optimization

    In this paper we consider bound constrained global optimization problems where first-order derivatives of the objective function can be neither computed nor approximated explicitly. For the solution of such problems the DIRECT Algorithm has been proposed which has strong convergence properties and a good ability to locate promising regions of the feasible domain. However, the … Read more

    Problem Formulations for Simulation-based Design Optimization using Statistical Surrogates and Direct Search

  • Michael Kokkolaras
  • Sébastien Le Digabel
  • Bastien Talgorn
    • Categories Applications - Science and Engineering, Constrained Nonlinear Optimization, Nonsmooth Optimization

    Typical challenges of simulation-based design optimization include unavailable gradients and unreliable approximations thereof, expensive function evaluations, numerical noise, multiple local optima and the failure of the analysis to return a value to the optimizer. One possible remedy to alleviate these issues is to use surrogate models in lieu of the computational models or simulations and … Read more

    String-Averaging Expectation-Maximization for Maximum Likelihood Estimation in Emission Tomography

  • Yair Censor
  • Álvaro Rodolfo De Pierro
  • Elias Salomão Helou
  • Tai-Been Chen
  • I-Liang Chern
  • Ming Jiang
  • Henry Horng-Shing Lu
    • Categories Biomedical Applications, Convex Optimization Tags , , , , , ,

    We study the maximum likelihood model in emission tomography and propose a new family of algorithms for its solution, called String-Averaging Expectation-Maximization (SAEM). In the String-Averaging algorithmic regime, the index set of all underlying equations is split into subsets, called “strings,” and the algorithm separately proceeds along each string, possibly in parallel. Then, the end-points … Read more

    Feasibility-Seeking and Superiorization Algorithms Applied to Inverse Treatment Planning in Radiation Therapy

  • Yair Censor
  • Ran Davidi
  • Sarah Geneser
  • Reinhard W. Schulte
  • Lei Xing
    • Categories Biomedical Applications, Convex Optimization Tags , , , ,

    We apply the recently proposed superiorization methodology (SM) to the inverse planning problem in radiation therapy. The inverse planning problem is represented here as a constrained minimization problem of the total variation (TV) of the intensity vector over a large system of linear two-sided inequalities. The SM can be viewed conceptually as lying between feasibility-seeking … Read more

    Sparsity Optimization in Design of Multidimensional Filter Networks

  • Mats Andersson
  • Oleg Burdakov
  • Hans Knutsson
  • Spartak Zikrin
    • Categories Applications - Science and Engineering, Global Optimization Applications, Multi-Criteria Optimization

    Filter networks are used as a powerful tool aimed at reducing the image processing time and maintaining high image quality. They are composed of sparse sub-filters whose high sparsity ensures fast image processing. The filter network design is related to solving a sparse optimization problem where a cardinality constraint bounds above the sparsity level. In … Read more