不光滑凸函数的下降算法

给出一个易在计算机上实现的极小化凸不光滑函数的下降算法，在目标函数下有界的条件下，证明了算法的全局收敛性；此外还把算法推广到约束凸规划问题，得到了与无约束问题同样的收敛结果，如果约束是线性的，则算法是数值可行的。和已有的下降算法相比，该算法具有更好的适应性。

A Descent Algorithm of Nonsmooth Convex Optimization

A descent algorithm of non-smooth convex optimization is given, which is a modification and an extension of algorithm introduced by Fukushima and Lemarechbal. Global convergence for the algorithm is established under the assumption that the objective function is bounded from below, The authors also generalize the algorithm to constrained convex optimization and obtain the convergence that is the same as unconstrained algorithm. If constraints are linear the algorithm is implementable.