4 papers
Quantum Optimization via Gradient-Based Hamiltonian Descent
Jiaqi Leng, Bin Shi
With rapid advancements in machine learning, first-order algorithms have emerged as the backbone of modern optimization techniques, owing to their computational efficiency and low…
On Pseudospectral Concentration for Rank-1 Sampling
Kuo Gai, Bin Shi
Pseudospectral analysis serves as a powerful tool in matrix computation and the study of both linear and nonlinear dynamical systems. Among various numerical strategies, random sam…
A Family of Controllable Momentum Coefficients for Forward-Backward Accelerated Algorithms
Mingwei Fu, Bin Shi
Nesterov's accelerated gradient method (NAG) marks a pivotal advancement in gradient-based optimization, achieving faster convergence compared to the vanilla gradient descent metho…
Lyapunov Analysis For Monotonically Forward-Backward Accelerated Algorithms
Mingwei Fu, Bin Shi
Nesterov's accelerated gradient method (NAG) achieves faster convergence than gradient descent for convex optimization but lacks monotonicity in function values. To address this, B…