Abstract In engineering applications one often has to trade-off among several objectives as, for example, the mechanical stability of a component, its efficiency, weight and cost. We consider biobjective shape optimization problem maximizing ceramic component under tensile load while minimizing volume. Stability is thereby modeled using Weibull-type formulation probability failure external load...

We present a new way to derive dissipative, optimizing dynamics from the Lagrangian formulation of mechanics. It can be used to obtain both standard and novel neural net dynamics for optimization problems. To demonstrate this we derive standard descent dynamics as well as nonstandard variants that introduce a computational attention mechanism.

Regional plantar pressures during stair walking may be injurious in at risk populations. However, limited data are available examining the reliability of plantar pressure data collected during stair walking. The aims of this study were three fold; to assess the reliability of the plantar pressure data recorded during stair walking, to assess the effects of level ground and stair walking on plan...

We investigate pattern avoidance in alternating permutations and an alternating analogue of Young diagrams. In particular, using an extension of Babson and West’s notion of shape-Wilf equivalence described in our recent paper (with N. Gowravaram), we generalize results of Backelin, West, and Xin and Ouchterlony to alternating permutations. Unlike Ouchterlony and Bóna’s bijections, our bijection...

Multistage model predictive control is a robust MPC formulation that takes into account parametric uncertainty by constructing finite set of coupled scenarios. As the amount scenarios increase so does computational cost and real-time implementation might not be possible. Scenario decomposition has been proposed to distribute computations make possible, however, typically subproblems are coordin...

Stochastic dual coordinate ascent (SDCA) is an effective technique for solving regularized loss minimization problems in machine learning. This paper considers an extension of SDCA under the minibatch setting that is often used in practice. Our main contribution is to introduce an accelerated minibatch version of SDCA and prove a fast convergence rate for this method. We discuss an implementati...

Stochastic Gradient Descent (SGD) has become popular for solving large scale supervised machine learning optimization problems such as SVM, due to their strong theoretical guarantees. While the closely related Dual Coordinate Ascent (DCA) method has been implemented in various software packages, it has so far lacked good convergence analysis. This paper presents a new analysis of Stochastic Dua...