5 papers · 1 filter
Adaptive Newton-CG methods with global and local analysis for unconstrained optimization with Hölder continuous Hessian
Ziyang Zeng, Junyu Zhang, Chuan He
In this paper, we study Newton-conjugate gradient (Newton-CG) methods for minimizing a nonconvex function whose Hessian is -Hölder continuous with modulus an…
Complexity of normalized stochastic first-order methods with momentum under heavy-tailed noise
Chuan He, Zhaosong Lu, Defeng Sun +1
In this paper, we propose practical normalized stochastic first-order methods with Polyak momentum, multi-extrapolated momentum, and recursive momentum for solving unconstrained op…
Accelerated stochastic first-order method for convex optimization under heavy-tailed noise
Chuan He, Zhaosong Lu
We study convex composite optimization problems, where the objective function is given by the sum of a prox-friendly function and a convex function whose subgradients are estimated…
Newton-CG methods for nonconvex unconstrained optimization with Hölder continuous Hessian
Chuan He, Heng Huang, Zhaosong Lu
In this paper we consider a nonconvex unconstrained optimization problem minimizing a twice differentiable objective function with Hölder continuous Hessian. Specifically, we firs…
A Newton-CG based barrier-augmented Lagrangian method for general nonconvex conic optimization
Chuan He, Heng Huang, Zhaosong Lu
In this paper we consider finding an approximate second-order stationary point (SOSP) of general nonconvex conic optimization that minimizes a twice differentiable function subject…