Proximal-like incremental aggregated gradient method with Bregman distance in weakly convex optimization problems

2021 ◽  
Author(s):  
Zehui Jia ◽  
Jieru Huang ◽  
Xingju Cai
Keyword(s):  
Convex Optimization ◽  
Gradient Method ◽  
Optimization Problems ◽  
Bregman Distance ◽  
Weakly Convex ◽  
Convex Optimization Problems

scholarly journals Strong Convergence of a Projected Gradient Method

2012 ◽  
Vol 2012 ◽  
pp. 1-10
Author(s):  
Shunhou Fan ◽  
Yonghong Yao
Keyword(s):  
Convex Optimization ◽  
Strong Convergence ◽  
Convergence Analysis ◽  
Projection Method ◽  
Minimization Problem ◽  
Gradient Method ◽  
Optimization Problems ◽  
Projected Gradient Method ◽  
Projected Gradient ◽  
Convex Optimization Problems

The projected-gradient method is a powerful tool for solving constrained convex optimization problems and has extensively been studied. In the present paper, a projected-gradient method is presented for solving the minimization problem, and the strong convergence analysis of the suggested gradient projection method is given.

scholarly journals A zeroth order method for stochastic weakly convex optimization

2021 ◽  
Author(s):  
V. Kungurtsev ◽  
F. Rinaldi
Keyword(s):  
Convex Optimization ◽  
Optimization Problems ◽  
State Of The Art ◽  
Similar Rate ◽  
Numerical Results ◽  
Order Method ◽  
Zeroth Order ◽  
Weakly Convex ◽  
Convex Optimization Problems ◽  
Derivative Free

AbstractIn this paper, we consider stochastic weakly convex optimization problems, however without the existence of a stochastic subgradient oracle. We present a derivative free algorithm that uses a two point approximation for computing a gradient estimate of the smoothed function. We prove convergence at a similar rate as state of the art methods, however with a larger constant, and report some numerical results showing the effectiveness of the approach.

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