A universal adaptive proximal gradient method converges at rates matching standard proximal gradient methods up to logarithmic factors for three problem classes without requiring knowledge of problem parameters.
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math.OC 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
APAPC integrates Nesterov acceleration into primal-dual forward-backward schemes by exploiting dual strong convexity to achieve optimal sublinear and accelerated linear convergence rates.
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Universal Adaptive Proximal Gradient Methods via Gradient Mapping Accumulation
A universal adaptive proximal gradient method converges at rates matching standard proximal gradient methods up to logarithmic factors for three problem classes without requiring knowledge of problem parameters.
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A Nesterov-Accelerated Primal-Dual Splitting Algorithm for Convex Nonsmooth Optimization
APAPC integrates Nesterov acceleration into primal-dual forward-backward schemes by exploiting dual strong convexity to achieve optimal sublinear and accelerated linear convergence rates.