Skip to main content

5 Result(s)

within D. W. Hearn Remove this filter

and

  1. No Access

    Book

    Large Scale Optimization

    State of the Art

    W. W. Hager, D. W. Hearn, P. M. Pardalos (1994)

  2. No Access

    Article

    Benders decomposition for variational inequalities

    The partitioning technique of J.F. Benders, which was generalized to nonlinear programming by Geoffrion, is further generalized to linearly constrained variational inequality problems. The conditions under whi...

    Siriphong Lawphongpanich, D. W. Hearn in Mathematical Programming (1990)

  3. No Access

    Chapter and Conference Paper

    Optimization Algorithms for Congested Network Models

    This paper describes recently developed nonlinear programming algorithms for certain large-scale congested network models. The techniques include Restricted Simplicial Decomposition (RSD) applied to the single...

    D. W. Hearn, S. Lawphongpanich, J. A. Ventura in Flow Control of Congested Networks (1987)

  4. No Access

    Chapter

    Restricted simplicial decomposition: Computation and extensions

    Restricted simplicial decomposition (RSD) is a very useful technique for certain large-scale pseudoconvex programming problems such as the traffic assignment problem and other network flow problems. The “restr...

    D. W. Hearn, S. Lawphongpanich, J.A. Ventura in Computation Mathematical Programming (1987)

  5. No Access

    Article

    A subgradient algorithm for certain minimax and minisum problems

    We present a subgradient algorithm for minimizing the maximum of a finite collection of functions. It is assumed that each function is the sum of a finite collection of basic convex functions and that the numb...

    J. A. Chatelon, D. W. Hearn, T. J. Lowe in Mathematical Programming (1978)