The Experts below are selected from a list of 72648 Experts worldwide ranked by ideXlab platform

Guangming Lin - One of the best experts on this subject based on the ideXlab platform.

  • Average Convergence Rate of Evolutionary Algorithms
    IEEE Transactions on Evolutionary Computation, 2016
    Co-Authors: Guangming Lin
    Abstract:

    In evolutionary Optimization, it is important to understand how fast evolutionary algorithms converge to the optimum per generation, or their convergence rates. This letter proposes a new measure of the convergence rate, called the average convergence rate. It is a normalized geometric mean of the reduction ratio of the fitness difference per generation. The calculation of the average convergence rate is very simple and it is applicable for most evolutionary algorithms on both continuous and Discrete Optimization. A theoretical study of the average convergence rate is conducted for Discrete Optimization. Lower bounds on the average convergence rate are derived. The limit of the average convergence rate is analyzed and then the asymptotic average convergence rate is proposed.

  • average convergence rate of evolutionary algorithms
    arXiv: Neural and Evolutionary Computing, 2015
    Co-Authors: Guangming Lin
    Abstract:

    In evolutionary Optimization, it is important to understand how fast evolutionary algorithms converge to the optimum per generation, or their convergence rate. This paper proposes a new measure of the convergence rate, called average convergence rate. It is a normalised geometric mean of the reduction ratio of the fitness difference per generation. The calculation of the average convergence rate is very simple and it is applicable for most evolutionary algorithms on both continuous and Discrete Optimization. A theoretical study of the average convergence rate is conducted for Discrete Optimization. Lower bounds on the average convergence rate are derived. The limit of the average convergence rate is analysed and then the asymptotic average convergence rate is proposed.

Zdravko Kravanja - One of the best experts on this subject based on the ideXlab platform.

  • mixed integer nonlinear programming techniques for process systems engineering
    Computers & Chemical Engineering, 1995
    Co-Authors: Ignacio E Grossmann, Zdravko Kravanja
    Abstract:

    Abstract This paper presents an overview of mixed-integer nonlinear programming techniques by first providing a unified treatment of the Branch and Bound, Outer-Approximation, Generalized Benders and Extended Cutting Plane methods as applied to nonlinear Discrete Optimization problems that are expressed in algebraic form. The extension of these methods is also considered for logic based representations. Finally, an overview of the applications in many areas in process engineering is presented.

Pierre Schaus - One of the best experts on this subject based on the ideXlab platform.

  • generic constraint based block modeling using constraint programming
    Principles and Practice of Constraint Programming, 2019
    Co-Authors: Alex Mattenet, Ian Davidson, Siegfried Nijssen, Pierre Schaus
    Abstract:

    Block modeling has been used extensively in many domains including social science, spatial temporal data analysis and even medical imaging. Original formulations of the problem modeled the problem as a mixed integer programming problem, but were not scalable. Subsequent work relaxed the Discrete Optimization requirement, and showed that adding constraints is not straightforward in existing approaches. In this work, we present a new approach based on constraint programming, allowing Discrete Optimization of block modeling in a manner that is not only scalable, but also allows the easy incorporation of constraints. We introduce a new constraint filtering algorithm that outperforms earlier approaches, in both constrained and unconstrained settings. We show its use in the analysis of real datasets.

Andreas Geiger - One of the best experts on this subject based on the ideXlab platform.

  • Discrete Optimization for optical flow
    Lecture Notes in Computer Science, 2015
    Co-Authors: Moritz Menze, Christian Heipke, Andreas Geiger
    Abstract:

    We propose to look at large-displacement optical flow from a Discrete point of view. Motivated by the observation that sub-pixel accuracy is easily obtained given pixel-accurate optical flow, we conjecture that computing the integral part is the hardest piece of the problem. Consequently, we formulate optical flow estimation as a Discrete inference problem in a conditional random field, followed by sub-pixel refinement. Naive discretization of the 2D flow space, however, is intractable due to the resulting size of the label set. In this paper, we therefore investigate three different strategies, each able to reduce computation and memory demands by several orders of magnitude. Their combination allows us to estimate large-displacement optical flow both accurately and efficiently and demonstrates the potential of Discrete Optimization for optical flow. We obtain state-of-the-art performance on MPI Sintel and KITTI.

Mubdi Rahman - One of the best experts on this subject based on the ideXlab platform.

  • galaxy redshifts from Discrete Optimization of correlation functions
    The Astronomical Journal, 2016
    Co-Authors: Benjamin C G Lee, Tamas Budavari, Amitabh Basu, Mubdi Rahman
    Abstract:

    We propose a new method of constraining the redshifts of individual extragalactic sources based on celestial coordinates and their ensemble statistics. Techniques from integer linear programming (ILP) are utilized to optimize simultaneously for the angular two-point cross- and autocorrelation functions. Our novel formalism introduced here not only transforms the otherwise hopelessly expensive, brute-force combinatorial search into a linear system with integer constraints but also is readily implementable in off-the-shelf solvers. We adopt Gurobi, a commercial Optimization solver, and use Python to build the cost function dynamically. The preliminary results on simulated data show potential for future applications to sky surveys by complementing and enhancing photometric redshift estimators. Our approach is the first application of ILP to astronomical analysis.

  • galaxy redshifts from Discrete Optimization of correlation functions
    arXiv: Instrumentation and Methods for Astrophysics, 2016
    Co-Authors: Benjamin C G Lee, Tamas Budavari, Amitabh Basu, Mubdi Rahman
    Abstract:

    We propose a new method of constraining the redshifts of individual extragalactic sources based on celestial coordinates and their ensemble statistics. Techniques from integer linear programming are utilized to optimize simultaneously for the angular two-point cross- and autocorrelation functions. Our novel formalism introduced here not only transforms the otherwise hopelessly expensive, brute-force combinatorial search into a linear system with integer constraints but also is readily implementable in off-the-shelf solvers. We adopt Gurobi, a commercial Optimization solver, and use Python to build the cost function dynamically. The preliminary results on simulated data show potential for future applications to sky surveys by complementing and enhancing photometric redshift estimators. Our approach is the first application of integer linear programming to astronomical analysis.