Variational Design and Fairing of Spline Surfaces

Variational principles have become quite popular in the design of free form surfaces. Among others they are used for fairing purposes. The choice of the ‘right’ fairness functional is a crucial step. There is always a tradeoff between high quality and computational effort. In this paper we present fairness functionals that allow fairing efficiently, i.e., produce high quality surfaces in a reasonable amount of time. These functionals can be considered as simplified thin plate energy functionals for parametric surfaces or as simplified MVC functionals.     

Stochastic recovery problem

We consider the problem of estimating a functional defined on some functional class from observations (with noise) of values of other functionals at the same functional class. In general, all functionals are nonlinear. We propose a formal mathematical statement of the problem. For the proposed statement, we give a nonasymptotically optimal estimation method under rather weak constraints on the estimated functional and noise. Some examples are considered.     

A note on convergence properties of interval-valued capacity functionals and Choquet integrals

Based on the classical theory of random sets, Feng and Nguyen (2007) [5] studied the convergence of capacity functionals of random sets in terms of Choquet integrals. In this paper, we consider an interval-valued capacity functional which is motivated by the goal to generalize a capacity functional and the Choquet integral with respect to an interval-valued capacity. In particular, we discuss some convergence theorems for interval-valued capacity functionals and interval-valued probability measures in the Hausdorff metric and Choquet weak sense.     

Diagonal Lyapunov–Krasovskii functionals for discrete-time positive systems with delay

We consider the existence of diagonal Lyapunov–Krasovskii (L–K) functionals for positive discrete-time systems subject to time-delay. In particular, we show that the existence of a diagonal functional is necessary and sufficient for delay-independent stability of a positive linear time-delay system. We extend this result and provide conditions for the existence of diagonal L–K functionals for classes of nonlinear positive time-delay systems, which are not necessarily order preserving. We also describe sufficient conditions for the existence of common diagonal L–K functionals for switched positive systems subject to time-delay.     

Least-squares approximation of affine mappings for sweep mesh generation: functional analysis and applications

Sweep methods are one of the most robust techniques to generate hexahedral meshes in extrusion volumes. The main issue in sweep algorithms is the projection of cap surface meshes along the sweep path. The most competitive technique to determine this projection is to find a least-squares approximation of an affine mapping. Several functional formulations have been defined to carry out this least-squares approximation. However, these functionals generate unacceptable meshes for several common geometries in CAD models. In this paper we present a new comparative analysis between these classical functional formulations and a new functional presented by the authors. In particular, we prove under which conditions the minimization of the analyzed functionals leads to a full rank linear system. Moreover, we also prove the equivalences between these formulations. These allow us to point out the advantages of the proposed functional. Finally, from this analysis we outline an automatic algorithm to compute the nodes location in the inner layers.     

A new least-squares approximation of affine mappings for sweep algorithms

This paper presents a new algorithm to generate hexahedral meshes in extrusion geometries. Several algorithms have been devised to generate hexahedral meshes by projecting the cap surfaces along a sweep path. In all of these algorithms the crucial step is the placement of the inner layer of nodes. That is, the projection of the source surface mesh along the sweep path. From the computational point of view, sweep methods based on a least-squares approximation of an affine mapping are the fastest alternative to compute these projections. Several functionals have been introduced to perform the least-squares approximation. However, for very simple and typical geometrical configurations they may generate low-quality projected meshes. For instance, they may induce skewness and flattening effects on the projected discretizations. In addition, for these configurations the minimization of these functionals may lead to a set of normal equations with singular system matrix. In this work we analyze previously defined functionals. Based on this analysis we propose a new functional and show that its minimization overcomes these drawbacks. Finally, we present several examples to assess the properties of the proposed functional.     

Gauging the performance of some density functionals including dispersion and nonlocal corrections for relative energies of water 20-mers

Currently, development of density functional theory approximations and their benchmarking for accurately modeling different types of molecular interactions become a very active field of research. In this report, performance of the dispersion (D3) and nonlocal (NL) corrected density functionals has been compared with generalized energy-based fragmentation approach at the complete basis set limit for predicting the relative energies of 10 low-energy isomers of water nanoclusters (H₂O)₂₀ as an illustrative example of hydrogen bonded systems. Considering a variety of exchange-correlation density functionals in combination with D3 and NL corrections we find that the D3 based approximations outperform the functionals incorporating NL correction. It is also shown that the LC-ωPBE-D3 and rPW86PBE-NL functionals have the best trend from the viewpoint of the order of stabilities in water nanoclusters under study.     

An alternative characterization of fuzzy complement functional

In this short note we propose a novel fuzzy complement functional. This functional is different from other functionals known in the literature. However, it turns out to be an alternative characterization of the well-known negation function. We sincerely thank the anonymous reviewer whose insightful suggestions have significantly improved the paper.     

Generation of root functionals of a system of polynomials

A root functional is a functional that is defined over a polynomial ring and annihilates the ideal of polynomials. The concept of a root functional is a generalization of the concept of a root to the case of multiple roots. This article considers a bilinear operation of generation of root functionals for a system of polynomials in variables, which allows one to construct a root functional from two root functionals. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 1, pp. 22–46, January–February 2008.     

A practical guide to direct optimization for planar grid-generation

In this paper, we provide a guide to using the direct optimization formulation of variational grid-generation. Particular emphasis is placed on the smoothness, or length, functional; this is undoubtedly the most important functional in variational grid-generation, producing smooth grids and ensuring well-posedness of the minimization problem when combined with more unruly functionals. Unfortunately, in its most primary form, length can produce folded grids when used with nonconvex geometries. Historically, there have been two solutions to this dilemma: using an inverse mapping (the famous Winslow generator), or augmenting the functional with others that promote unicity. Both strategies have the drawback that the resulting minimization problem becomes complicated and expensive. As another alternative, we introduce a generalized strategy for length which does not use inverse mappings or auxiliary functionals, but makes strong use of reference grids. This strategy provides flexibility in controlling grid quality, and its minimization problems can be solved using a simple multigrid algorithm, yielding a robust grid-generation scheme with optimal complexity.We also survey recent developments in the use of variational grid-generation (in the form of direct optimization) for the alignment problem.     

Algorithmic differentiation: application to variational problems in computer vision

Many vision problems can be formulated as minimization of appropriate energy functionals. These energy functionals are usually minimized, based on the calculus of variations (Euler-Lagrange equation). Once the Euler-Lagrange equation has been determined, it needs to be discretized in order to implement it on a digital computer. This is not a trivial task and, is moreover, error- prone. In this paper, we propose a flexible alternative. We discretize the energy functional and, subsequently, apply the mathematical concept of algorithmic differentiation to directly derive algorithms that implement the energy functional's derivatives. This approach has several advantages: First, the computed derivatives are exact with respect to the implementation of the energy functional. Second, it is basically straightforward to compute second-order derivatives and, thus, the Hessian matrix of the energy functional. Third, algorithmic differentiation is a process which can be automated. We demonstrate this novel approach on three representative vision problems (namely, denoising, segmentation, and stereo) and show that state-of-the-art results are obtained with little effort.     

A dissipative dynamical systems approach to stability analysis of time delay systems

In this paper the concepts of dissipativity and the exponential dissipativity are used to provide sufficient conditions for guaranteeing asymptotic stability of a time delay dynamical system. Specifically, representing a time delay dynamical system as a negative feedback interconnection of a finite‐dimensional linear dynamical system and an infinite‐dimensional time delay operator, we show that the time delay operator is dissipative with respect to a quadratic supply rate and with a storage functional involving an integral term identical to the integral term appearing in standard Lyapunov–Krasovskii functionals. Finally, using stability of feedback interconnection results for dissipative systems, we develop sufficient conditions for asymptotic stability of time delay dynamical systems. The overall approach provides a dissipativity theoretic interpretation of Lyapunov–Krasovskii functionals for asymptotically stable dynamical systems with arbitrary time delay. Copyright © 2004 John Wiley & Sons, Ltd.     

Scale-Invariant Minimum-Cost Curves: Fair and Robust Design Implements

Four functionals for the computation of minimum cost curves are compared. Minimization of these functionals result in the widely studied Minimum Energy Curve (MEC), the recently introduced Minimum Variation Curve (MVC), and their scale-invariant counterparts, (SI-MEC, SI-MVC). We compare the stability and fairness of these curves using a variety of simple interpolation problems. Previously, we have shown MVC to possess superior fairness. In this paper we show that while MVC have fairness and stability superior to MEC they are still not stable in all configurations. We introduce the SI-MVC as a stable alternative to the MVC. Like the MVC, circular and helical arcs are optimal shapes for the SI-MVC. Additionally, the application of scale invariance to functional design allows us to investigate locally optimal curves whose shapes are dictated solely by their topology, free of any external interpolation or arc length constraints.     

Estimating the characteristics of randomized dynamic data models (the Entropy-Robust Approach)

We propose a new approach to finding dependencies between small volumes of input and output data based on randomized dynamic models and density estimation for the distributions of their parameters. Randomized dynamic models are defined by functional Volterra polynomials. To construct robust nonparametric estimation procedures, we develop an entropybased approach that employs functionals of generalized informational Boltzmann and Fermi entropies.     

Semi-Inner-Products for Convex Functionals and Their Use in Image Decomposition

Semi-inner-products in the sense of Lumer are extended to convex functionals. This yields a Hilbert-space like structure to convex functionals in Banach spaces. In particular, a general expression for semi-inner-products with respect to one homogeneous functionals is given. Thus one can use the new operator for the analysis of total variation and higher order functionals like total-generalized-variation. Having a semi-inner-product, an angle between functions can be defined in a straightforward manner. It is shown that in the one homogeneous case the Bregman distance can be expressed in terms of this newly defined angle. In addition, properties of the semi-inner-product of nonlinear eigenfunctions induced by the functional are derived. We use this construction to state a sufficient condition for a perfect decomposition of two signals and suggest numerical measures which indicate when those conditions are approximately met.     

On one class of model optimization problems with a continuum of solutions

We consider critical sets of an H-regular functional. We propose a condition under which the set of all critical points forms a critical set. We discuss several problems that lead to such sets and show a connection with the notion of Morse index. As examples we consider integral functionals for functions defined on a segment.     

Graduated nonconvexity by functional focusing

Reconstruction of noise-corrupted surfaces may be stated as a (in general nonconvex) functional minimization problem. For functionals with quadratic data term, this paper addresses the criteria for such functionals to be convex, and the variational approach for minimization. I present two automatic and general methods of approximation with convex functionals based on Gaussian convolution. They are compared to the Blake-Zisserman graduated nonconvexity (GNC) method (1987) and Bilbro et al. (1992) and Geiger and Girosi's (1991) mean field annealing (MFA) of a weak membrane     

Ergodicity of filtering process by vanishing discount approach

A new approach to study ergodicity of filtering processes is presented. It is based on the vanishing discount approach to discounted functional of filtering process. We show that limit superior of the Cesaro averages of the functionals is the same for all initial conditions from which the uniqueness of invariant measures of filtering processes follows. The approach is based on certain assumption for which we provide a sufficient condition using concavity arguments. In addition we show the existence of solutions to the Poisson equation corresponding to filtering process with concave functional. The assumptions are then extended to the controlled case and using similar concave arguments we obtain the existence of solutions to the Bellman equation corresponding to partially observed average cost per unit time problem.     

Optimal Level Curves and Global Minimizers of Cost Functionals in Image Segmentation

We propose a variational framework for determining global minimizers of rough energy functionals used in image segmentation. Segmentation is achieved by minimizing an energy model, which is comprised of two parts: the first part is the interaction between the observed data and the model, the second is a regularity term. The optimal boundaries are the curves that globally minimize the energy functional. Our motivation comes from the observation that energy functionals are traditionally complex, for which it is usually difficult to precise global minimizers corresponding to best segmentations. Therefore, we focus on basic energy models, which global minimizers can be characterized. None of the proposed segmentation models captures all the important scene variables but may be useful to get an insight into objects, surfaces or parts of objects in the scene. In this paper, we prove that the set of curves that minimizes the cost functionals is a subset of level lines, i.e. the boundaries of level sets of the image. For the completeness of the paper, we present a fast algorithm for computing partitions with connected components. It leads to a sound initialization-free algorithm without any hidden parameter to be tuned. We illustrate the performance of our algorithm with several examples on both 2D biomedical and aerial images, and synthetic images.     

Lyapunov functionals in complex /spl mu/ analysis

Conditions for robust stability of linear time-invariant systems subject to structured linear time-invariant uncertainties can be derived in the complex /spl mu/ framework, or, equivalently, in the framework of integral quadratic constraints. These conditions can be checked numerically with linear matrix inequality (LMI)-based convex optimization using the Kalman-Yakubovich-Popov lemma. We show how LMI tests also yield a convex parametrization of (a subset of) Lyapunov functionals that prove robust stability of such uncertain systems. We show that for uncertainties that are pure delays, the Lyapunov functionals reduce to the standard Lyapunov-Krasovksii functionals that are encountered in the stability analysis of delay systems. We demonstrate the practical utility of the Lyapunov functional parametrization by deriving bounds for a number of measures of robust performance (beyond the usual H/sub /spl infin// performance); these bounds can be efficiently computed using convex optimization over linear matrix inequalities.