In this work we introduce a relaxed version of the constant positive linear dependence constraint qualification (CPLD) that we call RCPLD. This development is inspired by a recent generalization of the constant rank constraint qualification from Minchenko and Stakhovski that was called RCR. We show that RCPLD is enough to ensure the convergence of an augmented Lagrangian algorithm and asserts t...

This paper presents a new approach for 3D view registration of stereo images. We introduce a hybrid error function which combines constraints from the ICP (Iterative Closest Point) algorithm and normal flow constraint. This new technique is more precise for small movements and noisy depth than ICP alone, and more robust for large movements than the normal flow constraint alone. Finally, we pres...

A particular form of constraint is incorporated to Linear Prediction lattice lter models in the form of unequal-length delays. This constraint amounts to reducing the number of intrinsic degrees of freedom de ned by the re ection coeÆcients without modifying the LPC order of the corresponding transfer function. It can be optimized by a simple exhaustive search scheme. Preliminary results show t...

This paper provides a general result on controlling local Rademacher complexities, which captures in an elegant form to relate the complexities with constraint on the expected norm to the corresponding ones with constraint on the empirical norm. This result is convenient to apply in real applications and could yield refined local Rademacher complexity bounds for function classes satisfying gene...

The paper presents analysis and simulation for the convolutional coded DH-PIM using 1⁄2 convolutional code with the constraint length of 3. Decoding is implemented using the Viterbi algorithm. The proposed scheme is simulated for a constraint length of 7 and the results are compared with a number of different modulation techniques. DH-PIM with convolutional coding requires about 4-5 dB lower SN...

We examine the convexity and tractability of the two-sided linear chance constraint model under Gaussian uncertainty. We show that these constraints can be applied directly to model a larger class of nonlinear chance constraints as well as provide a reasonable approximation for a challenging class of quadratic chance constraints of direct interest for applications in power systems. With a view ...

The Object Constraint Language (OCL) is often used for behavioral specification of software components. One current problem in specifying behavioral aspects comes from the fact that editing OCL constraints manually is time consuming and error-prone. To simplify constraint definition we propose to use specification patterns for which OCL constraints can be generated automatically. In this paper ...

Distributed constraint optimization (DCOP) problems are a popular way of formulating and solving agent-coordination problems. A DCOP problem is a problem where several agents coordinate their values such that the sum of the resulting constraint costs is minimal. It is often desirable to solve DCOP problems with memory-bounded and asynchronous algorithms. We introduce Branch-and-Bound ADOPT (BnB...

Distributed constraint optimization (DCOP) problems are a popular way of formulating and solving agent-coordination problems. A DCOP problem is a problem where several agents coordinate their values such that the sum of the resulting constraint costs is minimal. It is often desirable to solve DCOP problems with memory-bounded and asynchronous algorithms. We introduce Branch-and-Bound ADOPT (BnB...