On geometrical properties of preconditioners in IPMs for classes of block-angular problems
J. Castro (jordi.castroupc.edu)
Abstract: One of the most efficient interior-point methods for some classes of block-angular structured problems solves the normal equations by a combination of Cholesky factorizations and preconditioned conjugate gradient for, respectively, the block and linking constraints. In this work we show that the choice of a good preconditioner depends on geometrical properties of the constraints structure. In particular, it is seen that the principal angles between the subspaces generated by the diagonal blocks and the linking constraints can be used to estimate ex-ante the efficiency of the preconditioner. Numerical validation is provided with some generated optimization problems. An application to the solution of multicommodity network flow problems with nodal capacities and equal flows of up to 127 million of variables and up to 7.5 million of constraints is also presented.
Keywords: interior-point methods, structured problems, preconditioned conjugate gradient, principal angles, large-scale optimization
Category 1: Linear, Cone and Semidefinite Programming (Linear Programming )
Citation: J. Castro, S. Nasini, On geometrical properties of preconditioners in IPMs for classes of block-angular problems, Research Report DR 2016/03, Dept. of Statistics and Operations Research, Universitat Politècnica de Catalunya, 2016.
Entry Submitted: 02/29/2016
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