In this paper, an augmented Lagrangian proximal alternating (ALPA) method is proposed for two class of large-scale sparse discrete constrained optimization problems in which a sequence of augmented Lagrangian subproblems are solved by utilizing proximal alternating linearized minimization framework and sparse projection techniques. Under the MangasarianFromovitz and the basic constraint qualifi...

In this paper, we design a numerical algorithm for solving a simple bilevel program where the lower level program is a nonconvex minimization problem with a convex set constraint. We propose to solve a combined problem where the first order condition and the value function are both present in the constraints. Since the value function is in general nonsmooth, the combined problem is in general a...

Resumen This research presents how the augmented reality can be used in assistance tasks in indoor environments. The approach of human-robot interaction supported in AR arises two classical questions in robot navigation: What does the robot need to do to show the AR? (task planning) and, which are the best movements to reach the destination allowing the user get the AR information? (path planni...

Abstract Intelligence Augmentation Systems (IAS) allow for more efficient and effective corporate processes by means of an explicit collaboration between artificial intelligence human judgment. However, the higher degree system autonomy, along with enrichment capabilities, amplifies pre-existing issues distribution moral responsibility: If IAS has caused harm, firms who have operated might argu...

Abstract We study the performance of some preconditioning techniques for a class block three-by-three linear systems equations arising from finite element discretizations coupled Stokes–Darcy flow problem. In particular, we investigate including preconditioners, constraint and augmented Lagrangian-based ones. Spectral field-of-value analyses are established exact versions these preconditioners....

We contribute improvements to a Lagrangian dual solution approach applied to large-scale optimization problems whose objective functions are convex, continuously differentiable and possibly nonlinear, while the nonrelaxed constraint set is compact but not necessarily convex. Such problems arise, for example, in the split-variable deterministic reformulation of stochastic mixed-integer optimizat...

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Augmented Lagrangian methods with convergence to second-order stationary points in which any constraint can be penalized or carried out to the subproblems are considered in this work. The resolution of each subproblem can be done by any numerical algorithm able to return approximate second-order stationary points. The developed global convergence theory is stronger than the ones known for curre...

Although it is known that correct dynamical equations of motion for a nonholonomic system cannot be obtained from a Lagrangean that has been augmented with a sum of the nonholonomic constraint equations weighted with multipliers, previous publications suggest otherwise. An example has been proposed in support of augmentation and purportedly demonstrates that an accepted method fails to produce ...

In this paper we present the concurrent constraint functional programming language CCFL and an abstract machine for the evaluation of CCFL programs in a multicore environment. The source language CCFL is a simple lazy functional language with a polymorphic type system augmented by ask-/tell-constraints and conjunctions to express concurrent coordination patterns. As execution model for CCFL we ...