When nonsmooth, convex minimizationproblems are solved by subgradientoptimizationmethods, the subgradients used will in general not accumulate to subgradients which verify the optimal-ity of a solution obtained in the limit. It is therefore not a straightforward task to monitor the progress of a subgradient method in terms of the approximate fulllment of optimality conditions. Further, certain ...

This paper studies the effect of stochastic errors on two constrained incremental subgradient algorithms. The incremental subgradient algorithms are viewed as decentralized network optimization algorithms as applied to minimize a sum of functions, when each component function is known only to a particular agent of a distributed network. First, the standard cyclic incremental subgradient algorit...

We consider computing the saddle points of a convex-concave function using subgradient methods. The existing literature on finding saddle points has mainly focused on establishing convergence properties of the generated iterates under some restrictive assumptions. In this paper, we propose a subgradient algorithm for generating approximate saddle points and provide per-iteration convergence rat...

3 Convergence proof 4 3.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 3.2 Some basic inequalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3.3 A bound on the suboptimality bound . . . . . . . . . . . . . . . . . . . . . . 7 3.4 A stopping criterion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.5 Numerical examp...

Some new results which generalize the Hahn-Banach theorem from scalar or vector-valued case to set-valued case are obtained. The existence of the Borwein-strong subgradient and Yang-weak subgradient for set-valued maps are also proven. we present a new Lagrange multiplier theorem and a new Sandwich theorem for set-valued maps.

In the recent paper [3], it was shown that the stochastic subgradient method applied to a weakly convex problem, drives the gradient of the Moreau envelope to zero at the rate O(k−1/4). In this supplementary note, we present a stochastic subgradient method for minimizing a convex function, with the improved rate Õ(k−1/2).

Mikhail A. Bragin • Peter B. Luh • Joseph H. Yan • Nanpeng Yu • Gary A. Stern Communicated by Fabián Flores-Bazàn Abstract Studies have shown that the surrogate subgradient method, to optimize non-smooth dual functions within the Lagrangian relaxation framework, can lead to significant computational improvements as compared to the subgradient method. The key idea is to obtain surrogate subgradi...

Given a set of basic binary features, we propose a new L1 norm SVM based feature selection method that explicitly selects the features in their polynomial or tree kernel spaces. The efficiency comes from the anti-monotone property of the subgradients: the subgradient with respect to a combined feature can be bounded by the subgradient with respect to each of its component features, and a featur...

In this paper, we consider a generic inexact subgradient algorithm to solve a nondifferentiable quasi-convex constrained optimization problem. The inexactness stems from computation errors and noise, which come from practical considerations and applications. Assuming that the computational errors and noise are deterministic and bounded, we study the effect of the inexactness on the subgradient ...