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Global and Unconditional $R$-Linear Convergence of Rank-Compressed Weighted LMSD Sweeps for Strictly Convex Quadratics
We study limited memory steepest descent (LMSD) with exact algebraic rank compression for strictly convex quadratic optimization. At each cycle, the method restricts the gradient history to its column space and applies the reciprocals of all Ritz values of the compressed projection in the next sweep
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