Nonsmooth Potential Functions Research Articles

A correction function-based kernel-free boundary integral method for elliptic PDEs with implicitly defined interfaces

This work addresses a novel version of the kernel-free boundary integral (KFBI) method for solving elliptic PDEs with implicitly defined irregular boundaries and interfaces. We focus on boundary value problems and interface problems, which are reformulated into boundary integral equations and solved with the matrix-free GMRES method. In the KFBI method, evaluating boundary and volume integrals only requires solving equivalent but much simpler interface problems in a bounding box, for which fast solvers such as FFTs and geometric multigrid methods are applicable. For the simple interface problem, a correction function is introduced for both the evaluation of right-hand side correction terms and the interpolation of a non-smooth potential function. A mesh-free collocation method is proposed to compute the correction function near the interface. The new method avoids complicated derivation for derivative jumps of the solution and is easy to implement, especially for the fourth-order method in three space dimensions. Various numerical examples are presented, including challenging cases such as high-contrast coefficients, arbitrarily close interfaces and heterogeneous interface problems. The reported numerical results verify that the proposed method is both accurate and efficient.

Smoothing unadjusted Langevin algorithms for nonsmooth composite potential functions

This paper addresses a gradient-based Markov Chain Monte Carlo (MCMC) method to sample from the posterior distribution of problems with nonsmooth potential functions. Following the Bayesian paradigm, our potential function will be some of two convex functions, where one of which is smooth. We first approximate the potential function by the so-called forward-backward envelope function, which is a real-valued smooth function with the same critical points as the original one. Then, we incorporate this smoothing technique with the unadjusted Langevin algorithm (ULA), leading to smoothing ULA, called SULA. We next establish non-asymptotic convergence results of SULA under mild assumption on the original potential function. We finally report some numerical results to establish the promising performance of SULA on both synthetic and real chemoinformatics data.

New potential functions for passivity based sliding mode control

Recently, passivity-based sliding mode control has been proposed for mechanical port-Hamiltonian systems. This controller has properties of both sliding mode control and passivity-based control, and Lyapunov stability is ensured by a Hamiltonian function with a non-smooth potential function. In the authors’ previous study, a special form of non-smooth potential function is considered and there are few parameters to adjust the controller for various control objectives. This paper proposes a new passivity-based sliding mode controller based on a wider class of potential functions. This approach enables us to reduce chattering and improve the behavior in reaching mode by adjusting parameters. The effectiveness of the proposed method is demonstrated by a numerical simulation.

The lower bound of nonlocal gradient for non-convex and non-smooth image patches based regularization

In the inverse problem of image processing, we have witnessed that the non-convex and non-smooth regularizers can produce clearer image edges than convex ones such as total variation (TV). This fact can be explained by the uniform lower bound theory of the local gradient in non-convex and non-smooth regularization. In recent years, although it has been numerically shown that the nonlocal regularizers of various image patches based nonlocal methods can recover image textures well, we still desire a theoretical interpretation. To this end, we propose a non-convex non-smooth and block nonlocal regularization model based on image patches. By integrating the advantages of the non-convex and non-smooth potential function in the regularization term, the uniform lower bound theory of the image patches based nonlocal gradient is given. This approach partially explains why the proposed method can produce clearer image textures and edges. Compared to some classical regularization methods, such as TV, non-convex and non-smooth regularization, nonlocal total variation and block nonlocal total variation, our experimental results show that the proposed method improves restoration quality.

Nonconvex and nonsmooth total variation regularization method for diffuse optical tomography based on RTE * *Submitted Funding: this work is supported by the National Natural Science Foundation of China (Grant No. 11701159).

Diffuse optical tomography with near-infrared light is a promising technique for noninvasive study of the functional characters of human tissues. Mathematically, it is a seriously ill-posed parameter identification problem. For the purpose of better providing both segmentation and piecewise constant approximation of the underlying solution, nonconvex nonsmooth total variation based regularization functional is considered in this paper. We first give a theoretical study on the well-posedness of solutions corresponding to this minimization problem in the Banach space of piecewise constant functions. Moreover, our theoretical results show that the minimizers corresponding to a sequence nonconvex nonsmooth potential functions which converge to the 0–1 functions, can be used to approximate the solution to the weak Mumford–Shah regularization. Then from the numerical side, we propose a double graduated nonconvex Gauss–Newton algorithm to solve this nonconvex nonsmooth regularization. All illustrations and numerical experiments give a flavor of the possibilities offered by the minimizers of the proposed algorithm.

A general truncated regularization framework for contrast-preserving variational signal and image restoration: Motivation and implementation

Variational methods have become an important kind of methods in signal and image restoration - a typical inverse problem. One important minimization model consists of the squared $\ell_2$ data fidelity (corresponding to Gaussian noise) and a regularization term constructed by a potential function composed of first order difference operators. It is well known that total variation (TV) regularization, although achieved great successes, suffers from a contrast reduction effect. Using a typical signal, we show that, actually all convex regularizers and most nonconvex regularizers have this effect. With this motivation, we present a general truncated regularization framework. The potential function is a truncation of existing nonsmooth potential functions and thus flat on $(\tau,+\infty)$ for some positive $\tau$. Some analysis in 1D theoretically demonstrate the good contrast-preserving ability of the framework. We also give optimization algorithms with convergence verification in 2D, where global minimizers of each subproblem (either convex or nonconvenx) are calculated. Experiments numerically show the advantages of the framework.

Generalizing Diffuse Interface Methods on Graphs: Nonsmooth Potentials and Hypergraphs

Diffuse interface methods have recently been introduced for the task of semisupervised learning. The underlying model is well known in materials science but was extended to graphs using a Ginzburg--Landau functional and the graph Laplacian. We here generalize the previously proposed model by a nonsmooth potential function. Additionally, we show that the diffuse interface method can be used for the segmentation of data coming from hypergraphs. For this we show that the graph Laplacian in almost all cases is derived from hypergraph information. Additionally, we show that the formerly introduced hypergraph Laplacian coming from a relaxed optimization problem is well suited to be used within the diffuse interface method. We present computational experiments for graph and hypergraph Laplacians.

A Improvement for GNC Method of Nonconvex Nonsmooth Image Restoration

Nonconvex nonsmooth potential functions have superior restoration performance for the images with neat boundaries. But the nondifferentiality could cause many numerical difficulties. Thus the graduated nonconvex (GNC) method is suggested to deal with these problems. In this paper, a class of nonconvex nonsmooth approximate potential functions have been constructed, which can help our get a better initial value of the original problem. The numerical results show the restored perfprmance of the proposed methods.

On the Existence of Three Nontrivial Solutions for Periodic Problems Driven by the Scalar p-Laplacian

Abstract We consider a nonlinear periodic problem driven by the scalar p-Laplacian with a nonsmooth potential function. First we establish an alternative minimax expression for the first nonzero eigenvalue for the negative periodic scalar p-Laplacian and then using it we prove the existence of three nontrivial solutions, two of which have constant sign. Our approach is variational, based on the nonsmooth critical point theory.

Existence and multiplicity results for nonlinear nonautonomous second-order systems

In this paper we consider nonlinear periodic systems driven by the ordinary p -Laplacian and having a nonsmooth potential function. Under minimal and natural hypotheses on the nonsmooth potential and using variational methods based on the nonsmooth critical point theory we prove four existence theorems and a multiplicity theorem. We conclude the paper with an existence theorem for the scalar problem, in which the energy functional is indefinite (i.e. it is unbounded both above and below).

Periodic solutions for nonautonomous systems with nonsmooth quadratic or superquadratic potential

We study a semilinear nonautonomous second order periodic system with a nonsmooth potential function which exhibits a quadratic or superquadratic growth. We establish the existence of a solution, using minimax methods of the nonsmooth critical point theory.

Markovian reconstruction using a GNC approach

This paper is concerned with the reconstruction of images (or signals) from incomplete, noisy data, obtained at the output of an observation system. The solution is defined in maximum a posteriori (MAP) sense and it appears as the global minimum of an energy function joining a convex data-fidelity term and a Markovian prior energy. The sought images are composed of nearly homogeneous zones separated by edges and the prior term accounts for this knowledge. This term combines general nonconvex potential functions (PFs) which are applied to the differences between neighboring pixels. The resultant MAP energy generally exhibits numerous local minima. Calculating its local minimum, placed in the vicinity of the maximum likelihood estimate, is inexpensive but the resultant estimate is usually disappointing. Optimization using simulated annealing is practical only in restricted situations. Several deterministic suboptimal techniques approach the global minimum of special MAP energies, employed in the field of image denoising, at a reasonable numerical cost. The latter techniques are not directly applicable to general observation systems, nor to general Markovian prior energies. This work is devoted to the generalization of one of them, the graduated nonconvexity (GNC) algorithm, in order to calculate nearly-optimal MAP solutions in a wide range of situations. In fact, GNC provides a solution by tracking a set of minima along a sequence of approximate energies, starting from a convex energy and progressing toward the original energy. In this paper, we develop a common method to derive efficient GNC-algorithms for the minimization of MAP energies which arise in the context of any observation system giving rise to a convex data-fidelity term and of Markov random field (MRF) energies involving any nonconvex and/or nonsmooth PFs. As a side-result, we propose how to construct pertinent initializations which allow us to obtain meaningful solutions using local minimization of these MAP energies. Two numerical experiments-an image deblurring and an emission tomography reconstruction-illustrate the performance of the proposed technique.

Nonconvex energy functions, related eigenvalue hemivariational inequalities on the sphere and applications

The present paper deals with a new type of eigenvalue problems arising in problems involving nonconvex nonsmooth energy functions. They lead to the search of critical points (e.g. local minima) for nonconvex nonsmooth potential functions which in turn give rise to hemivariational inequalities. For this type of variational expressions the eigenvalue problem is studied here concerning the existence and multiplicity of solutions by applying a critical point theory appropriate for nonsmooth nonconvex functionals.