Proposes the 'approximately optimal stepsize' as a unified inexact adaptive stepsize framework for gradient, CG, and quasi-Newton methods on strictly convex quadratic problems, with convergence proofs for the gradient case linked to Barzilai-Borwein stepsizes and supporting numerical tests.
An efficient gradient method with approximately optimal stepsizes based on regularization models for unconstrained optimization
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A unified framework for inexact adaptive stepsizes in the gradient methods, the conjugate gradient methods and the quasi-Newton methods for strictly convex quadratic optimization
Proposes the 'approximately optimal stepsize' as a unified inexact adaptive stepsize framework for gradient, CG, and quasi-Newton methods on strictly convex quadratic problems, with convergence proofs for the gradient case linked to Barzilai-Borwein stepsizes and supporting numerical tests.