Infinite horizon average cost dynamic programming subject to ambiguity on conditional distribution

This paper addresses the optimality of stochastic control strategies based on the infinite horizon average cost criterion, subject to total variation distance ambiguity on the conditional distribution of the controlled process. This stochastic optimal control problem is formulated using minimax theory, in which the minimization is over the control strategies and the maximization is over the conditional distributions. Under the assumption that, for every stationary Markov control law the maximizing conditional distribution of the controlled process is irreducible, we derive a new dynamic programming recursion which minimizes the future ambiguity, and we propose a new policy iteration algorithm. The new dynamic programming recursion includes, in addition to the standard terms, the oscillator semi-norm of the cost-to-go. The maximizing conditional distribution is found via a water-filling algorithm. The implications of our results are demonstrated through an example

Spontaneous symmetry-breaking vortex lattice transitions in pure niobium

We report an extensive investigation of magnetic vortex lattice (VL) structures in single crystals of pure niobium with the magnetic field applied parallel to a fourfold symmetry axis, so as to induce frustration between the cubic crystal symmetry and hexagonal VL coordination expected in an isotropic situation. We observe new VL structures and phase transitions; all the VL phases observed (including those with an exactly square unit cell) spontaneously break some crystal symmetry. One phase even has the lowest possible symmetry of a two-dimensional Bravais lattice. This is quite unlike the situation in high-Tc or borocarbide superconductors, where VL structures orient along particular directions of high crystal symmetry. The causes of this behavior are discussed. © 2006 The American Physical Society