A²ODA method achieves accelerated convergence in p-norm geometry
Adaptive accelerated optimistic dual averaging provides line-search-free preconditioning for convex optimization.
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Stories tagged #convex-optimization
Optimal stepsize schedules for stochastic gradient descent in convex minimization
Exact analysis proves monotonically decreasing optimal stepsize schedules for SGD under bounded noise in smooth convex problems.
Improved complexity bounds for second-order convex finite-sum optimization
The paper matches a new lower bound of Omega(n + n^{5/7} eps^{-2/7}) using a Taylor-corrected lazy Newton method with Monteiro-Svaiter acceleration.
Convex-SKM-Net speeds convex constraint repair up to 144.3×
Fixed-depth dual PGD keeps SOC-MPC and power-system tests inside a 10^-3 feasibility tolerance.
A Nearly Quadratic Lower Bound for Linear Optimization over Convex Bodies in the Membership Oracle Model
Researchers prove nearly quadratic lower bounds for linear optimization over convex bodies in the membership oracle model.
Near-Optimal Pure Single-Loop Extragradient Method for Strongly Convex--Strongly Concave Minimax Optimization
A pure single-loop damped extragradient method achieves last-iterate linear convergence for strongly convex–strongly concave minimax optimization with near-optimal gradient complexity.
Silver Rate Is (Almost) Optimal for Gradient Descent Acceleration
New optimization theory paper proves near-optimal lower bounds for gradient descent with predetermined stepsizes, confirming silver-schedule optimality.