In this paper, we consider a class of structured nonsmooth difference-of-convex (DC) constrained DC programs in which the first convex component objective and constraints is sum smooth function, their second supremum finitely many functions. The existing methods for problem usually have weak convergence guarantee or require feasible initial point. Inspired by recent work Pang et al. [Pang J-S, ...

We present a new particle tracking algorithm for accurately resolving large deformation and rotational motion fields, which takes advantage of both local global algorithms. call this method ScalE Rotation Invariant Augmented Lagrangian Particle Tracking (SerialTrack). This builds an iterative scale rotation invariant topology-based feature vector each within multi-scale algorithm. The kinematic...

Abstract A reformulation of cardinality-constrained optimization problems into continuous nonlinear with an orthogonality-type constraint has gained some popularity during the last few years. Due to special structure constraints, violates many standard assumptions and therefore is often solved using specialized algorithms. In contrast this, we investigate viability a safeguarded multiplier pena...

We consider the global convergence properties of a class of augmented Lagrangian methods for solving nonlinear programming problems. In the proposed method, linear constraints are treated separately from more general constraints. Thus only the latter are combined with the objective function in an augmented Lagrangian. The subproblem then consists of (approximately) minimizing this augmented Lag...

In this paper, we propose combining augmented Lagrangian optimization with the dual decomposition method to obtain a fast algorithm for approximate MAP (maximum a posteriori) inference on factor graphs. We also show how the proposed algorithm can efficiently handle problems with (possibly global) structural constraints. The experimental results reported testify for the state-of-the-art performa...

We propose a new algorithm for approximate MAP inference on factor graphs, which combines augmented Lagrangian optimization with the dual decomposition method. Each slave subproblem is given a quadratic penalty, which pushes toward faster consensus than in previous subgradient approaches. Our algorithm is provably convergent, parallelizable, and suitable for fine decompositions of the graph. We...

Given a set of corrupted data drawn from a union of multiple subspace, the subspace recovery problem is to segment the data into their respective subspace and to correct the possible noise simultaneously. Recently, it is discovered that the task can be characterized, both theoretically and numerically, by solving a matrix nuclear-norm and a `2,1-mixed norm involved convex minimization problems....

In this paper we introduced and analyzed the Log-Sigmoid (LS) multipliers method for constrained optimization. The LS method is to the recently developed smoothing technique as augmented Lagrangian to the penalty method or modified barrier to classical barrier methods. At the same time the LS method has some specific properties, which make it substantially different from other nonquadratic augm...

In this article, we discuss the numerical solution of a constrained minimization problem arising from the stress analysis of elasto-plastic bodies. This minimization problem has the flavor of a generalized non-smooth eigenvalue problem, with the smallest eigenvalue corresponding to the load capacity ratio of the elastic body under consideration. An augmented Lagrangian method, together with fin...