Applicable Analysis · 1971 · 35 citations · 15 references
Natural SciencesMathematicsAlgorithmsOptimizationRange ImprovementNumerical AlgorithmsOperator TheoryBounded Linear OperatorsConvergence For Singular SolutionsLeast Squares ProblemsSteepest Descent
Let T be a bounded linear operator between two Hilbert spaces with the range of T not necessarily closed, and let T † denote the generalized inverse of T. The method of steepest descent for minimizing is shown to converge monotonically starting with x 0 = 0, to T † y for any y whose orthogonal projection on the closure of the range of T, is in the range of TT *. This set of y's is dense in the domain of T†. The method is also applied to generalized least squares solutions of a class of unbounded linear operator equations
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