This paper presents an efficient algorithm to solve total variation (TV) regularizations of images contaminated by a both blur and noise. The unconstrained structure of the problem suggests that one can solve a constrained optimization problem by transforming the original unconstrained minimization problem to an equivalent constrained minimization one. An augmented Lagrangian method is develope...

The dynamic modeling and analysis of planar rigid multibody systems that experience contact-impact events is presented and discussed throughout this work. The methodology is based on the nonsmooth dynamics approach, in which the interaction of the colliding bodies is modeled with multiple frictional unilateral constraints. Rigid multibody systems are stated as an equality of measures, which are...

This paper describes an approach to be used to apply Model Driven Software Development (MDSD) techniques to the development of software for 3D games on multiple platforms, with additional usage in Scientific Visualization, Virtual Reality (VR), Augmented Reality (AR), and Animation applications. Specific areas of applicability for MDSD techniques include development tooling and artifact creatio...

We provide a unifying geometric framework for the analysis of general classes of duality schemes and penalty methods for nonconvex constrained optimization problems. We present a separation result for nonconvex sets via general concave surfaces. We use this separation result to provide necessary and sufficient conditions for establishing strong duality between geometric primal and dual problems...

In this paper, we will study optimality conditions of semi-infinite programs and generalized semi-infinite programs, for which the gradient objective function when evaluated at any feasible direction for the linearized constraint set is non-negative. We consider three types of penalty functions for semi-infinite program and investigate the relationship among the exactness of these penalty funct...

This paper addresses the state-estimation problem for nonlinear systems in a context where prior knowledge, in addition to the model and the measurement data, is available in the form of a nonlinear equality constraint. Then three suboptimal algorithms based on the unscented Kalman filter (UKF) are developed, namely, the equality-constrained unscented Kalman filter (ECUKF), the projected unscen...

We give a sound and complete fence insertion procedure for concurrent finite-state programs running under the classical TSO memory model. This model allows “write to read” relaxation corresponding to the addition of an unbounded store buffer between each processor and the main memory. We introduce a novel machine model, called the Single-Buffer (SB) semantics, and show that the reachability pro...

This paper presents CONORBIT, a derivative-free algorithm for constrained black-box optimization where the objective and constraint functions are computationally expensive. CONORBIT employs a trust-region framework that uses interpolating radial basis function (RBF) models for the objective and constraint functions and is an extension of the ORBIT algorithm (Wild, Regis, and Shoemaker 2008). It...

To cope with a growing number of applications, the basic formalism of constraint satisfaction problems has to be augmented in various directions. One of these directions is the concept of dynamic constraint problems i.e. problems to which constraints can be added but also retracted at any time. To handle dynamic problems, it is important to adapt eeciently the solving procedures that are availa...

In this paper, we propose a novel test of the 3M-CAPM under a positivity constraint on the estimated stochastic discount factor (SDF) and, more importantly, an upper bound on its volatility. The positivity constraint rules out arbitrage opportunities, while the restriction on SDF volatility rules out unduly large Sharpe ratios and is based on a sensible upper bound on investors’ risk aversion. ...