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Conjugate Gradients and Accelerated Methods Unified: The Approximate Duality Gap View
Jelena Diakonikolas, Lorenzo Orecchia
This note provides a novel, simple analysis of the method of conjugate gradients for the minimization of convex quadratic functions. In contrast with standard arguments, our proof…
On Acceleration with Noise-Corrupted Gradients
Michael B. Cohen, Jelena Diakonikolas, Lorenzo Orecchia
Accelerated algorithms have broad applications in large-scale optimization, due to their generality and fast convergence. However, their stability in the practical setting of noise…
Alternating Randomized Block Coordinate Descent
Jelena Diakonikolas, Lorenzo Orecchia
Block-coordinate descent algorithms and alternating minimization methods are fundamental optimization algorithms and an important primitive in large-scale optimization and machine…