主  題:一類(lèi)凸優(yōu)化的混合下降算法及收斂性分析

內(nèi)容簡(jiǎn)介:The proximal point algorithm (PPA) is a classical method for solving convex minimization, which frequently finds an exact solution of implicit subproblems. To reduce the difficulty and complexity in computing implicit subproblems, the approximate proximal point method(APPA) establishes an approximate solution of implicit subproblems under some approximate rules. In this report, two directions were designed by making greater use of historical information of approximate rules and the prediction-correction step length extension with the random number series, and a hybrid descent method(HD Method) for convex minimization was developed through convex combinations of the two directions with the random number series. Subsequently we established the strong convergence of HD method for convex minimization under some approximate rules. Moreover, it is also worth noting that the efficiency of HD method is confirmed through a series of numerical experiments

報(bào)告人:徐海文    教授    博士

時(shí)  間:2018-09-20    15:00

地  點(diǎn):競(jìng)慧東樓302

舉辦單位:統(tǒng)計(jì)與數(shù)學(xué)學(xué)院