Hall, Julian - School of Mathematics, University of Edinburgh

• Computational linear optimization Julian Hall
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• PARSMI, a parallel revised simplex algorithm incorporating minor iterations and Devex pricing
• High performance implementations of the simplex method for linear programming
• Ordering Algorithms for Irreducible Sparse Linear Systems
• Subroutines This section describes the EMSOL subroutines. For each subroutine the
• Parallelisation of the revised simplex method for general large scale LP problems
• Parallel basis matrix triangularisation for hyper-sparse LP problems
• Computational linear optimization Julian Hall
• Hypersparsity in the revised simplex method and how to exploit it
• User exit subroutines This section describes the user exit subroutine.
• Parallelisation Mathematics
• Control variables This section contains a list of all of the control variables used by EMSOL
• Subroutines This section describes the EMSOL subroutines. For each subroutine the
• Exploiting hypersparsity in the revised simplex method
• Hypersparsity in the revised simplex method and how to exploit it
• Scalable massively parallel simplex algorithms for block-structured linear programs
• Interfaces to system utilities 94 EMSOL Subroutine Library: Version 0.1.n
• Subroutines This section describes the EMSOL subroutines. For each subroutine the
• Introduction The Edinburgh Management Science Optimization Library (EMSOL) is an
• E#cient global optimization: refinements and extensions
• Hypersparsity Mathematics
• Exploiting hyper-sparsity in the revised simplex method
• 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1
• Hyper-sparsity in the revised simplex method and how to exploit it
• Parallel triangularisation: Distribution of rows Processor p operates using the following row-wise data
• Computational issues in linear programming Julian Hall
• Parallel basis matrix triangularisation for hyper-sparse LP problems
• Flexible Retrofit Design of Multiproduct Batch Plants R. Fletcher 1 , J.A.J. Hall 2 , and W.R. Johns 3
• Exploiting hyper-sparsity when computing preconditioners for conjugate gradients in interior point methods
• EMSOL Subroutine Library: Version 0 J.A.J. Hall
• Scalable massively parallel simplex algorithms for block-structured linear programs
• A task-parallel scheme for the revised simplex method Julian Hall
• Hyper-sparsity in the revised simplex method and how to exploit it
• The simplest examples where the simplex method cycles and conditions where
• ASYNPLEX, an asynchronous parallel revised simplex algorithm
• User exit subroutines This section describes the user exit subroutine.
• The Mathematical legacy of ECOSSE Julian Hall
• A parallel revised simplex solver for large scale block angular LP problems
• Chapter 7 TTRec 57 7.1 Overview of TTRec
• GPU acceleration of the matrix-free interior point E. Smith, J. Gondzio and J. A. J. Hall
• A high performance dual revised simplex solver J. A. J. Hall and Q. Huangfu
• Towards a practical parallelisation of the simplex method
• Hyper-sparsity in the revised simplex method and how to exploit it
• High performance computing and the simplex method Julian Hall, Qi Huangfu and Edmund Smith
• The future of the simplex method Julian Hall
• The Mathematical legacy of ECOSSE Julian Hall
• High performance implementations of the simplex method for linear programming
• Computational linear optimization Julian Hall
• Parallel solution of block angular LP problems using Kaul's algorithm
• Parallel basis matrix triangularisation for hyper-sparse LP problems
• Parallel basis matrix triangularisation for hyper-sparse LP problems
• Exploiting hyper-sparsity when computing preconditioners for conjugate gradients in interior point methods
• Computational issues in linear programming Julian Hall
• Parallel matrix inversion for the revised simplex method -A study
• Parallel matrix inversion for the revised simplex method -A study
• Parallelisation of the revised simplex method for general large scale LP problems
• A task-parallel scheme for the revised simplex method Julian Hall
• A task-parallel scheme for the revised simplex method Julian Hall
• The practical revised simplex method Julian Hall
• The practical revised simplex method (Part 2) Julian Hall
• Parallelisation of the revised simplex method for general large scale LP problems
• Parallelisation of the revised simplex method for general large scale LP problems
• Efficient global optimization: refinements and extensions
• A high performance dual revised simplex solver Julian Hall, Qi Huangfu and Edmund Smith
• A task-parallel scheme for the revised simplex method Julian Hall
• Towards a practical parallelisation of the revised simplex method
• The following is a table of the pointers into dspace or ispace. Name Index Name Index
• Control variables This section contains a list of all of the control variables used by EMSOL
• The following is a table of the pointers into dspace or dspace. Name Index Name Index
• Aim of interfaces to system utili Bit masks, 45
• Introduction The Edinburgh Management Science Optimization Library (EMSOL) is an
• Update procedures for the parallel revised simplex method
• Hyper-sparsity Mathematics
• EMSOL Subroutine Library: Version J.A.J. Hall
• The following is a table of the pointers into dspace or ispace. Name Index Name Index Name Index
• Parallelisation Mathematics
• Parallel solution of block angular LP problems using Kaul's algorithm
• Computational linear optimization Julian Hall
• optimization: reliability
• User exit subroutines This section describes the user exit subroutine.
• Matrix-free IPM with GPU acceleration Julian Hall, Edmund Smith and Jacek Gondzio
• A high performance dual revised simplex solver Julian Hall and Qi Huangfu