In this paper, a new hybrid conjugate gradient algorithm is proposed for solving unconstrained optimization problems. This new method can generate sufficient descent directions unrelated to any line search. Moreover, the global convergence of the proposed method is proved under the Wolfe line search. Numerical experiments are also presented to show the efficiency of the proposed algorithm, espe...

Abstract Stochastic Gradient Algorithms (SGAs) are ubiquitous in computational statistics, machine learning and optimisation. Recent years have brought an influx of interest SGAs, the non-asymptotic analysis their bias is by now well-developed. However, relatively little known about optimal choice random approximation (e.g mini-batching) gradient SGAs as this relies on variance problem specific...

Deep Deterministic Policy Gradient (DDPG) algorithm is one of the most well-known reinforcement learning methods. However, this method inefficient and unstable in practical applications. On other hand, bias variance Q estimation target function are sometimes difficult to control. This paper proposes a Regularly Updated (RUD) policy gradient for these problems. theoretically proves that procedur...

Gradient-based algorithms, popular strategies to optimization problems, are essential for many modern machine-learning techniques. Theoretically, extreme points of certain cost functions can be found iteratively along the directions gradient. The time required calculating gradient $d$-dimensional problems is at a level $\mathcal{O}(poly(d))$, which could boosted by quantum techniques, benefitin...