Abstract: The stochastic subgradient method is a widely used algorithm for solving large-scale optimization problems arising in machine learning. Often, these problems are neither smooth nor convex.

Abstract: The convex feasibility problem consists of finding a point in the intersection of closed convex sets. We propose a new type of algorithm to solve it in which randomly selected blocks of ...

ABSTRACT: This is the first paper to be written on the theory of structural learning. The first section outlines the overall concept; the second section proposes the logical universe as the ...

ABSTRACT: Two-stage problem of stochastic convex programming with fuzzy probability distribution is studied in this paper. Multicut L-shaped algorithm is proposed to solve the problem based on the ...

Stochastic Methods for Finance, attended at the University of Padova. The course focused on the application of stochastic processes and probabilistic methods to the modeling of financial products and ...

Segmentation fault occurring at https://github.com/Argonne-National-Laboratory/DSP/blob/release/src/Solver/DualDecomp/DdDriverSerial.cpp#L73, when Subgradient method is called. I believe Osi is ...