The purpose of this paper is to study the approximate optimality condition for composite convex optimization problems with a cone-convex system in locally convex spaces,where all functions involved are not necessaril...The purpose of this paper is to study the approximate optimality condition for composite convex optimization problems with a cone-convex system in locally convex spaces,where all functions involved are not necessarily lower semicontinuous.By using the properties of the epigraph of conjugate functions,we introduce a new regularity condition and give its equivalent characterizations.Under this new regularity condition,we derive necessary and sufficient optimality conditions ofε-optimal solutions for the composite convex optimization problem.As applications of our results,we derive approximate optimality conditions to cone-convex optimization problems.Our results extend or cover many known results in the literature.展开更多
Inertial methods play a vital role in accelerating the convergence speed of optimization algorithms.This work is concerned with an inertial semi-forward-reflected-backward splitting algorithm of approaching the soluti...Inertial methods play a vital role in accelerating the convergence speed of optimization algorithms.This work is concerned with an inertial semi-forward-reflected-backward splitting algorithm of approaching the solution of sum of a maximally monotone operator,a cocoercive operator and a monotone-Lipschitz continuous operator.The theoretical convergence properties of the proposed iterative algorithm are also presented under mild conditions.More importantly,we use an adaptive stepsize rule in our algorithm to avoid calculating Lipschitz constant,which is generally unknown or difficult to estimate in practical applications.In addition,a large class of composite monotone inclusion problem involving mixtures of linearly composed and parallel-sum type monotone operators is solved by combining the primal-dual approach and our proposed algorithm.As a direct application,the obtained inertial algorithm is exploited to composite convex optimization problem and some numerical experiments on image deblurring problem are also investigated to demonstrate the efficiency of the proposed algorithm.展开更多
基金the National Natural Science Foundation of China(Nos.11471059,11301571,and 11301570)the Chongqing Research Program of Basic Research and Frontier Technology(Nos.cstc2014jcyjA00037,cstc2015jcyjB00001,cstc2015jcyjA00025,and cstc2015jcyjA00002)+2 种基金the Education Committee Project Research Foundation of Chongqing(Nos.KJ1400618 and KJ1500626)the Postdoctoral Science Foundation of China(Nos.2015M580774 and 2016T90837)the Program for University Innovation Team of Chongqing(CXTDX201601026 and CXTDX201601022).
文摘The purpose of this paper is to study the approximate optimality condition for composite convex optimization problems with a cone-convex system in locally convex spaces,where all functions involved are not necessarily lower semicontinuous.By using the properties of the epigraph of conjugate functions,we introduce a new regularity condition and give its equivalent characterizations.Under this new regularity condition,we derive necessary and sufficient optimality conditions ofε-optimal solutions for the composite convex optimization problem.As applications of our results,we derive approximate optimality conditions to cone-convex optimization problems.Our results extend or cover many known results in the literature.
基金Supported by the National Natural Science Foundations of China(Grant Nos.11771193,11661056,12061045)。
文摘Inertial methods play a vital role in accelerating the convergence speed of optimization algorithms.This work is concerned with an inertial semi-forward-reflected-backward splitting algorithm of approaching the solution of sum of a maximally monotone operator,a cocoercive operator and a monotone-Lipschitz continuous operator.The theoretical convergence properties of the proposed iterative algorithm are also presented under mild conditions.More importantly,we use an adaptive stepsize rule in our algorithm to avoid calculating Lipschitz constant,which is generally unknown or difficult to estimate in practical applications.In addition,a large class of composite monotone inclusion problem involving mixtures of linearly composed and parallel-sum type monotone operators is solved by combining the primal-dual approach and our proposed algorithm.As a direct application,the obtained inertial algorithm is exploited to composite convex optimization problem and some numerical experiments on image deblurring problem are also investigated to demonstrate the efficiency of the proposed algorithm.
