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On solving large sparse linear systems arising from linear programming
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This book in Computer Science is tailored towards solving the standard linear programming problem and the standard linear regression problem. First we p ose these problems as sequences of weighted linear systems We discuss a combination of a direct solver and an iterative solver for solving these sequences of weighted linear systems For this mixed solver approach a class of preconditioners based on low rank corrections is discussed and preconditioners constructed The choice of the low rank correction matrix is based on derived theoretical b ounds on the eigenvalues of the precondi toned matrix. In addition for linear programming we suggest a globally convergent in exact interior p oint algorithm Based on this algorithm we state a globally convergent mixed interior p oint algorithm that suits the class of preconditioners mentioned above. Furthermore for the case of linear regression we discuss another class of preconditioners based on downdating a constant factorized matrix at every iteration Also a new convex weighting function for linear regression is suggested and preconditioners based on this function discussed.
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