We extend the classic convergence rate theory for subgradient methods to apply to non-Lipschitz functions. For the deterministic projected subgradient method, we present a global O(1/ √ T ) convergence rate for any convex function which is locally Lipschitz around its minimizers. This approach is based on Shor’s classic subgradient analysis and implies generalizations of the standard convergenc...

In the networking research literature, the problem of network utility optimization is often converted to the dual problem which, due to nondifferentiability, is solved with a particular subgradient technique. This technique is not an ascent scheme, hence each iteration does not necessarily improve the value of the dual function. This paper examines the performance of this computational techniqu...

We present a unifying framework for nonsmooth convex minimization bringing together -subgradient algorithms and methods for the convex feasibility problem. This development is a natural step for -subgradient methods in the direction of constrained optimization since the Euclidean projection frequently required in such methods is replaced by an approximate projection, which is often easier to co...

In this paper we develop a subgradient optimization methodology for convex multiparametric nonlinear programs. We define a parametric subgradient, extend some classical optimization results to the multiparametric case, and design a subgradient algorithm that is shown to converge under traditional conditions. We use this algorithm to solve two illustrative example problems and demonstrate its ac...

The subgradient projector is of considerable importance in convex optimization because it plays the key role in Polyak’s seminal work — and the many papers it spawned — on subgradient projection algorithms for solving convex feasibility problems. In this paper, we offer a systematic study of the subgradient projector. Fundamental properties such as continuity, nonexpansiveness, and monotonicity...

We describe efficient implementations of the Subtractive Optimally Localized Averages (SOLA) mollifier method for solving linear inverse problems in, e.g., inverse helioseismology. We show that the SOLA method can be regarded as a constrained least squares problem, which can be solved by means of standard “building blocks” from numerical linear algebra. We compare the standard implementation of...

We introduce a generalised subgradient for law-invariant closed convex risk measures on L and establish its relationship with optimal risk allocations and equilibria. Our main result gives sufficient conditions ensuring a non-empty generalised subgradient.

Abstract In this survey paper, we present a multiscale post-processing method in exploration. Based on physically relevant mollifier technique involving the elasto-oscillatory Cauchy–Navier equation, mathematically describe extractable information within 3D geological models obtained by migration as is commonly used for geophysical exploration purposes. More explicitly, developed approach extra...

The approximation properties of the finite element method can often be substantially improved by choosing smooth high-order basis functions. It is extremely difficult to devise such functions for partitions consisting arbitrarily shaped polytopes. We propose mollified arbitrary order and smoothness convex On each polytope an independent local polynomial approximant assumed. are defined as convo...