3 présentations

• 
  09h45 - 10h10

  Novel update techniques for the revised simplex method

  • Julian Hall, prés., University of Edinburgh
  • Qi Huangfu, FICO

  The development of techniques for creating and updating the invertible representation of the simplex basis matrix were a feature of my PhD studies under Roger in 1987-90. So it is appropriate that this talk presents recent recent work in this area. Specifically, it will describe two variants of the product form update and the multiple Forrest-Tomlin update which are valuable in the context of a recently released high performance revised simplex solver.

• 
  10h10 - 10h35

  On the behavior of a Class of Symmetric Conjugate Gradient Methods for Large-Scale Optimization

  • Mehiddin Al-Baali, prés., Sultan Qaboos University

  A recent class of symmetric conjugate gradient methods for large-scale unconstrained optimization will be considered. The class, with any line search, suggests strategies for enforcing the sufficient descent and other useful properties. Numerical results on a set of standard test problems will be described. They show that the proposed symmetrical-strategy improves the performance of several conjugate gradient methods substantially (in particular, those of the well-known Fletcher-Reeves, Polak-Ribi\'{e}re and Hestenes-Stiefel methods).

• 
  10h35 - 11h00

  Nonlinear optimization problems on the sphere

  • Robert Womersley, prés., University of New South Wales

  The problem of placing N points uniformly on the unit sphere has many different solutions depending on the particular objective: Riesz s-energy, separation/packing, covering, numerical integration (including spherical t-designs) and approximation. Different parametrizations of the variables, either as points in Euclidean space, spherical coordinates or positive semi-definite matrices of inner products with a rank condition, each have their own advantages and disadvantages. The aim is to have many (millions) points, and a global minimizer, when these highly nonlinear problems are characterized by many different local minimizers. Here there are applications for a wide range of efficient scalable nonlinear optimzation algorithms - areas that greatly benefited from Roger Fletcher's pioneering work.