Technical Note—Undiscounted Markov Renewal Programming Via Modified Successive Approximations

A Note on Markov-renewal Programming

Multiple Instance Classification via Successive Linear Programming

The multiple instance classification problem [6,2,12] is formulated using a linear or nonlinear kernel as the minimization of a linear function in a finite dimensional (noninteger) real space subject to linear and bilinear constraints. A linearization algorithm is proposed that solves a succession of fast linear programs that converges in a few iterations to a local solution. Computational resu...

Successive approximations for Markov decision processes and Markov games with unbounded rewards

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Stochastic Training of Neural Networks via Successive Convex Approximations

This paper proposes a new family of algorithms for training neural networks (NNs). These are based on recent developments in the field of non-convex optimization, going under the general name of successive convex approximation (SCA) techniques. The basic idea is to iteratively replace the original (non-convex, highly dimensional) learning problem with a sequence of (strongly convex) approximati...

The Functional Equations of Undiscounted Markov Renewal Programming

This paper investigates the solutions to the functional equations that arise inter alia in Undiscounted Markov Renewal Programming. We show that the solution set is a connected, though possibily nonconvex set whose members are unique up to n* constants, characterize n* and show that some of these n* degrees of freedom are locally rather than globally independent. Our results generalize those ob...