Sobolev Seminorm Research Articles

On the asymptotic behaviour of the fractional Sobolev seminorms: A geometric approach

We study the well-known asymptotic formulas for fractional Sobolev functions à la Bourgain–Brezis–Mironescu and Maz'ya–Shaposhnikova, in a geometric approach. We show that the key to these asymptotic formulas are Rademacher's theorem and volume growth at infinity respectively. Examples fitting our framework includes Euclidean spaces, Riemannian manifolds, Alexandrov spaces, finite dimensional Banach spaces, and some ideal sub-Riemannian manifolds.

A Weierstrass extremal field theory for the fractional Laplacian

Abstract In this paper, we extend, for the first time, part of the Weierstrass extremal field theory in the Calculus of Variations to a nonlocal framework. Our model case is the energy functional for the fractional Laplacian (the Gagliardo–Sobolev seminorm), for which such a theory was still unknown. We build a null-Lagrangian and a calibration for nonlinear equations involving the fractional Laplacian in the presence of a field of extremals. Thus, our construction assumes the existence of a family of solutions to the Euler–Lagrange equation whose graphs produce a foliation. Then the minimality of each leaf in the foliation follows from the existence of the calibration. As an application, we show that monotone solutions to fractional semilinear equations are minimizers. In a forthcoming work, we generalize the theory to a wide class of nonlocal elliptic functionals and give an application to the viscosity theory.

Convergence and error estimates for pseudo-polyharmonic div-curl and elastic interpolation on a bounded domain

This paper establishes convergence rates and error estimates for the pseudo-polyharmonic div-curl and elastic interpolation. This type of interpolation is based on a combination of the divergence and the curl of a multivariate vector field and minimizing an appropriate functional energy related to the divergence and curl. Convergence rates and error estimates are established when the interpolated vector field is assumed to be in the classical fractional vectorial Sobolev space on an open bounded set with a Lipschitz-continuous boundary. The error estimates introduced in this work are sharp and the rate of convergence depends algebraically on the fill distance of the scattered data nodes. More precisely, the order of convergence depends, essentially, on the smoothness of the target vector field, on the dimension of the Euclidean space and on the null space of corresponding Sobolev semi-norm.

Diffusion tensor regularization with metric double integrals

AbstractIn this paper, we propose a variational regularization method for denoising and inpainting of diffusion tensor magnetic resonance images. We consider these images as manifold-valued Sobolev functions, i.e. in an infinite dimensional setting, which are defined appropriately. The regularization functionals are defined as double integrals, which are equivalent to Sobolev semi-norms in the Euclidean setting. We extend the analysis of [14] concerning stability and convergence of the variational regularization methods by a uniqueness result, apply them to diffusion tensor processing, and validate our model in numerical examples with synthetic and real data.

Generalized Notions of Sparsity and Restricted Isometry Property. Part II: Applications

Restricted isometry property (RIP) provides a near isometric map for sparse signals. RIP of structured random matrices has played a key role for dimensionality reduction and recovery from compressive measurements. In a companion paper, we have developed a unified theory for RIP of group structured measurement operators on generalized sparsity models. The implication of the extended result will be further discussed in this paper in terms of its pros and cons over the conventional theory. We first show that the extended RIP theory enables the optimization of sample complexity over various relaxations of the canonical sparsity model. Meanwhile, the generalized sparsity model is no longer described as a union of subspaces. Thus the sparsity level is not sub-additive. This incurs that RIP of double the sparsity level does not imply RIP on the Minkowski difference of the sparsity model with itself, which is crucial for dimensionality reduction. We show that a group structured measurement operator provides an RIP-like property with additive distortion for non-sub-additive models. This weaker result can be useful for applications like locality-sensitive hashing. Moreover, we also present that the group structured measurements with random sign enables near isometric sketching on any set similar to the Gaussian measurements. Lastly, an extension of theory to infinite dimension is derived and illustrated over selected examples given by Lebesgue measure of support and Sobolev seminorms.

A surprising formula for Sobolev norms

We establish the equivalence between the Sobolev seminorm [Formula: see text] and a quantity obtained when replacing strong [Formula: see text] by weak [Formula: see text] in the Gagliardo seminorm [Formula: see text] computed at [Formula: see text] As corollaries we derive alternative estimates in some exceptional cases (involving [Formula: see text]) where the "anticipated" fractional Sobolev and Gagliardo-Nirenberg inequalities fail.

Reduction of integration domain in Triebel–Lizorkin spaces

We investigate the comparability of generalized Triebel–Lizorkin and Sobolev seminorms on uniform and nonuniform domains when the integration domain is truncated according to the distance from the boundary. We provide numerous examples of kernels and doma

Fractional Sobolev norms and BV functions on manifolds

The bounded variation seminorm and the Sobolev seminorm on compact manifolds are represented as a limit of fractional Sobolev seminorms. This establishes a characterization of functions of bounded variation and of Sobolev functions on compact manifolds. As an application the special case of sets of finite perimeter is considered.

Regularization with Metric Double Integrals of Functions with Values in a Set of Vectors

We present an approach for variational regularization of inverse and imaging problems for recovering functions with values in a set of vectors. We introduce regularization functionals, which are derivative-free double integrals of such functions. These regularization functionals are motivated from double integrals, which approximate Sobolev semi-norms of intensity functions. These were introduced in Bourgain et al. (Another look at Sobolev spaces. In: Menaldi, Rofman, Sulem (eds) Optimal control and partial differential equations-innovations and applications: in honor of professor Alain Bensoussan’s 60th anniversary, IOS Press, Amsterdam, pp 439–455, 2001). For the proposed regularization functionals, we prove existence of minimizers as well as a stability and convergence result for functions with values in a set of vectors.

A third Strang lemma and an Aubin–Nitsche trick for schemes in fully discrete formulation

In this work, we present an abstract error analysis framework for the approximation of linear partial differential equation (PDE) problems in weak formulation. We consider approximation methods in fully discrete formulation, where the discrete and continuous spaces are possibly not embedded in a common space. A proper notion of consistency is designed, and, under a classical inf-sup condition, it is shown to bound the approximation error. This error estimate result is in the spirit of Strang's first and second lemmas, but applicable in situations not covered by these lemmas (because of a fully discrete approximation space). An improved estimate is also established in a weaker norm, using the Aubin--Nitsche trick. We then apply these abstract estimates to an anisotropic heterogeneous diffusion model and two classical families of schemes for this model: Virtual Element and Finite Volume methods. For each of these methods, we show that the abstract results yield new error estimates with a precise and mild dependency on the local anisotropy ratio. A key intermediate step to derive such estimates for Virtual Element Methods is proving optimal approximation properties of the oblique elliptic projector in weighted Sobolev seminorms. This is a result whose interest goes beyond the specific model and methods considered here. We also obtain, to our knowledge, the first clear notion of consistency for Finite Volume methods, which leads to a generic error estimate involving the fluxes and valid for a wide range of Finite Volume schemes. An important application is the first error estimate for Multi-Point Flux Approximation L and G methods. In the appendix, not included in the published version of this work, we show that classical estimates for discontinuous Galerkin methods can be obtained with simplified arguments using the abstract framework.

A Hybrid High-Order discretisation of the Brinkman problem robust in the Darcy and Stokes limits

In this work, we develop and analyse a novel Hybrid High-Order discretisation of the Brinkman problem. The method hinges on hybrid discrete velocity unknowns at faces and elements and on discontinuous pressures. Based on the discrete unknowns, we reconstruct inside each element a Stokes velocity one degree higher than face unknowns, and a Darcy velocity in the Raviart–Thomas–Nédélec space. These reconstructed velocities are respectively used to formulate the discrete versions of the Stokes and Darcy terms in the momentum equation, along with suitably designed penalty contributions. The proposed construction is tailored to yield optimal error estimates that are robust throughout the entire spectrum of local (Stokes- or Darcy-dominated) regimes, as identified by a dimensionless number which can be interpreted as a friction coefficient. The singular limit corresponding to the Darcy equation is also fully supported by the method. Numerical examples corroborate the theoretical results. This paper also contains two contributions whose interest goes beyond the specific method and application treated in this work: an investigation of the dependence of the constant in the second Korn inequality on star-shaped domains and its application to the study of the approximation properties of the strain projector in general Sobolev seminorms.

Designing a Finite-Time Mixer: Optimizing Stirring for Two-Dimensional Maps

Mixing of a passive scalar in a fluid flow results from a two part process in which large gradients are first created by advection and then smoothed by diffusion. We investigate methods of designing efficient stirrers to optimize mixing of a passive scalar in a two-dimensional nonautonomous, incompressible flow over a finite time interval. The flow is modeled by a sequence of area-preserving maps whose parameters change in time, defining a mixing protocol. Stirring efficiency is measured by a negative Sobolev seminorm; its decrease implies creation of fine scale structure. A Perron-Frobenius operator is used to numerically advect the scalar for two examples: compositions of Chirikov standard maps and of Harper maps. In the former case, we find that a protocol corresponding to a single vertical shear composed with horizontal shearing at all other steps is nearly optimal. For the Harper maps, we devise a predictive, one-step scheme to choose appropriate fixed point stabilities and to control the Fourier spectrum evolution to obtain a near optimal protocol.

Asymmetric anisotropic fractional Sobolev norms

Bourgain, Brezis & Mironescu showed that (with suitable scaling) the fractional Sobolev $s$-seminorm of a function $f\in W^{1,p}(\mathbb{R}^n)$ converges to the Sobolev seminorm of $f$ as $s\rightarrow1^-$. Ludwig introduced the anisotropic fractional Sobolev $s$-seminorms of $f$ defined by a norm on $\mathbb{R}^n$ with unit ball $K$, and showed that they converge to the anisotropic Sobolev seminorm of $f$ defined by the norm whose unit ball is the polar $L_p$ moment body of $K$, as $s\rightarrow1^-$. The asymmetric anisotropic $s$-seminorms are shown to converge to the anisotropic Sobolev seminorm of $f$ defined by the Minkowski functional of the polar asymmetric $L_p$ moment body of $K$.

Anisotropic fractional Sobolev norms

Bourgain, Brezis & Mironescu showed that (with suitable scaling) the fractional Sobolev s-seminorm of a function f∈W1,p(Rn) converges to the Sobolev seminorm of f as s→1−. The anisotropic s-seminorms of f defined by a norm on Rn with unit ball K are shown to converge to the anisotropic Sobolev seminorm of f defined by the norm with unit ball Zp⁎K, the polar Lp moment body of K. The limiting behavior for s→0+ is also determined (extending results by Mazʼya & Shaposhnikova).

Sobolev seminorm of quadratic functions with applications to derivative-free optimization

This paper studies the $$H^1$$ Sobolev seminorm of quadratic functions. The research is motivated by the least-norm interpolation that is widely used in derivative-free optimization. We express the $$H^1$$ seminorm of a quadratic function explicitly in terms of the Hessian and the gradient when the underlying domain is a ball. The seminorm gives new insights into least-norm interpolation. It clarifies the analytical and geometrical meaning of the objective function in least-norm interpolation. We employ the seminorm to study the extended symmetric Broyden update proposed by Powell. Numerical results show that the new thoery helps improve the performance of the update. Apart from the theoretical results, we propose a new method of comparing derivative-free solvers, which is more convincing than merely counting the numbers of function evaluations.

Extension of sampling inequalities to Sobolev semi-norms of fractional order and derivative data

This paper, devoted to sampling inequalities, provides some extensions of previous results by Arcangeli et al. (Numer Math 107(2):181–211, 2007; J Approx Theory 161:198–212, 2009). Given a function u in a suitable Sobolev space defined on a domain Ω in $${{\mathbb{R}}^n}$$, sampling inequalities typically yield bounds of integer order Sobolev semi-norms of u in terms of a higher order Sobolev semi-norm of u, the fill distance d between $${\overline\Omega}$$ and a discrete set $${A\subset\overline\Omega}$$, and the values of u on A. The extensions established in this paper allow us to bound fractional order semi-norms and to incorporate, if available, values of partial derivatives on the discrete set. Both the cases of a bounded domain Ω and $${\Omega={\mathbb{R}}^n}$$ are considered.

A quantitative isoperimetric inequality for fractional perimeters

Recently Frank and Seiringer have shown an isoperimetric inequality for nonlocal perimeter functionals arising from Sobolev seminorms of fractional order. This isoperimetric inequality is improved here in a quantitative form.

A note on the local approximation properties of piecewise polynomials with applications to LDG methods

In this article we generalize some standard local approximation properties of piecewise polynomials, which are measured only in the Sobolev seminorms of order 0 and 1, to the case of fractional Sobolev seminorms. The simplicity of the corresponding proofs is an interesting feature of the presentation. In addition, our analysis makes clear at each step that the constants involved are independent of the meshsizes. As an application, we show that the corresponding generalized estimates simplify the a priori error analysis of the local discontinuous Galerkin methods in the case of general non-convex polyhedral domains.

Does Increased Regularity Lower Complexity?

Intuitively, the more regular a problem, the easier it should be to solve. Examples drawn from ordinary and partial differential equations, as well as from approximation, support the intuition. Traub and Woźniakowski conjectured that this is always the case. In this paper, we study linear problems. We prove a weak form of the conjecture, and show that this weak form cannot be strengthened. To do this, we consider what happens to the optimal error when regularity is increased. If regularity is measured by a Sobolev norm, increasing the regularity improves the optimal error, which allows us to establish the conjecture in the normed case. On the other hand, if regularity is measured by a Sobolev seminorm, it is no longer true that increasing the regularity improves the optimal error. However, a "shifted" version of this statement holds, which enables us to establish the conjecture in the seminormed case.

Does increased regularity lower complexity?

Intuitively, the more regular a problem, the easier it should be to solve. Examples drawn from ordinary and partial differential equations, as well as from approximation, support the intuition. Traub and Woźniakowski conjectured that this is always the case. In this paper, we study linear problems. We prove a weak form of the conjecture, and show that this weak form cannot be strengthened. To do this, we consider what happens to the optimal error when regularity is increased. If regularity is measured by a Sobolev norm, increasing the regularity improves the optimal error, which allows us to establish the conjecture in the normed case. On the other hand, if regularity is measured by a Sobolev seminorm, it is no longer true that increasing the regularity improves the optimal error. However, a "shifted" version of this statement holds, which enables us to establish the conjecture in the seminormed case.