DCA corresponds to Euler discretization of a Bregman gradient flow, with a damped version providing monotone descent, global linear rates under metric DC-PL, and local exponential convergence near nondegenerate minima.
USSR Computational Math- ematics and Mathematical Physics3(4), 864–878 (1963)
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Approximate one-time preconditioning in face for MPGP algorithms yields error bounds, a sharp condition-number estimate, and large observed speedups on quadratic programs with constraints.
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DCA corresponds to Euler discretization of a Bregman gradient flow, with a damped version providing monotone descent, global linear rates under metric DC-PL, and local exponential convergence near nondegenerate minima.
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Approximate one-time preconditioning in face for MPGP algorithms yields error bounds, a sharp condition-number estimate, and large observed speedups on quadratic programs with constraints.
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