The efficiency of subgradient projection methods for convex optimization .1. General level methods
The efficiency of subgradient projection methods for convex optimization .1. General level methods
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DOI：
10.1137/0334031
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发表时间：
1996-03-01
影响因子：
2.2
通讯作者：
Kiwiel, KC
中科院分区：
文献类型：
--
作者：
Kiwiel, KC
我们研究凸优化的次梯度方法，使用投影到目标的水平集的连续近似对应的最优值的估计。我们提出了几个变种，并表明他们享有几乎最佳的效率估计。在另一篇论文中，我们讨论了这种方法的可能实现。特别是，他们的投影子问题，他可以通过松弛方法不精确地解决，从而打开了并行实现的方式。他们还可以利用基于同时投影，代理约束，共轭和投影（条件）次梯度技术的松弛方法的加速。
We study subgradient methods for convex optimization that use projections onto successive approximations of level sets of the objective corresponding to estimates of the optimal value. We present several variants and show that they enjoy almost optimal efficiency estimates. In another paper we discuss possible implementations of such methods. In particular, their projection subproblems may he solved inexactly via relaxation methods, thus opening the way for parallel implementations. They can also exploit accelerations of relaxation methods based on simultaneous projections, surrogate constraints, and conjugate and projected (conditional) subgradient techniques.