A0330
Title: Subgradient methods for quasi-convex optimization with applications
Authors: Carisa Kwok Wai Yu - The Hang Seng University of Hong Kong (Hong Kong) [presenting]
Jacky Leung - The Hang Seng University of Hong Kong (Hong Kong)
Abstract: Subgradient methods form a class of popular and effective iterative algorithms used to solve constrained optimization problems. More recently, they have been developed to solve constrained quasi-convex optimization problems. An implementable subgradient method is proposed where a perturbation of the successive direction is employed at each iteration for solving quasi-convex optimization problems. The proposed subgradient method is applied to solve the Cobb-Douglas production efficiency problem. The numerical study on the efficiency problem shows that the proposed subgradient method outperforms several existing methods, such as the standard, stochastic and primal-dual subgradient methods.
