Numerical verification of a gap condition for a linearized nonlinear Schrödinger equation

We make a detailed numerical study of the spectrum of two Schrödinger operators L± arising from the linearization of the supercritical nonlinear Schrödinger equation (NLS) about the standing wave, in three dimensions. This study was motivated by a recent result of the second author on the conditional asymptotic stability of solitary waves in the case of a cubic nonlinearity. Underlying the validity of this result is a spectral condition on the operators L±, namely that they have no eigenvalues nor resonances in the gap (a region of the positive real axis between zero and the continuous spectrum), which we call the gap property. The present numerical study verifies this spectral condition and shows further that the gap property holds for NLS exponents of the form 2 β + 1, as long as β* < β ≤ 1, where $$\begin{equation*}\beta_{\ast} = 0.913\,958\,905 \pm 1e-8.\end{equation*}$$ Our strategy consists of rewriting the original eigenvalue problem via the Birman–Schwinger method. From a numerical analysis viewpoint, our main contribution is an efficient quadrature rule for the kernel 1/|x - y| in {\mathbb R}^3 , i.e. proved spectrally accurate. As a result, we are able to give similar accuracy estimates for all our eigenvalue computations. We also propose an improvement in Petviashvili's iteration for the computation of standing wave profiles which automatically chooses the radial solution. All our numerical experiments are reproducible. The Matlab code can be downloaded from http://www.acm.caltech.edu/~demanet/NLS/

Scaling Law for Recovering the Sparsest Element in a Subspace

We address the problem of recovering a sparse $n$-vector within a given subspace. This problem is a subtask of some approaches to dictionary learning and sparse principal component analysis. Hence, if we can prove scaling laws for recovery of sparse vectors, it will be easier to derive and prove recovery results in these applications. In this paper, we present a scaling law for recovering the sparse vector from a subspace that is spanned by the sparse vector and $k$ random vectors. We prove that the sparse vector will be the output to one of $n$ linear programs with high probability if its support size $s$ satisfies $s \lesssim n/\sqrt{k \log n}$. The scaling law still holds when the desired vector is approximately sparse. To get a single estimate for the sparse vector from the $n$ linear programs, we must select which output is the sparsest. This selection process can be based on any proxy for sparsity, and the specific proxy has the potential to improve or worsen the scaling law. If sparsity is interpreted in an $\ell_1/\ell_\infty$ sense, then the scaling law can not be better than $s \lesssim n/\sqrt{k}$. Computer simulations show that selecting the sparsest output in the $\ell_1/\ell_2$ or thresholded-$\ell_0$ senses can lead to a larger parameter range for successful recovery than that given by the $\ell_1/\ell_\infty$ sense

Full waveform inversion with extrapolated low frequency data

The availability of low frequency data is an important factor in the success of full waveform inversion (FWI) in the acoustic regime. The low frequencies help determine the kinematically relevant, low-wavenumber components of the velocity model, which are in turn needed to avoid convergence of FWI to spurious local minima. However, acquiring data below 2 or 3 Hz from the field is a challenging and expensive task. In this paper we explore the possibility of synthesizing the low frequencies computationally from high-frequency data, and use the resulting prediction of the missing data to seed the frequency sweep of FWI. As a signal processing problem, bandwidth extension is a very nonlinear and delicate operation. It requires a high-level interpretation of bandlimited seismic records into individual events, each of which is extrapolable to a lower (or higher) frequency band from the non-dispersive nature of the wave propagation model. We propose to use the phase tracking method for the event separation task. The fidelity of the resulting extrapolation method is typically higher in phase than in amplitude. To demonstrate the reliability of bandwidth extension in the context of FWI, we first use the low frequencies in the extrapolated band as data substitute, in order to create the low-wavenumber background velocity model, and then switch to recorded data in the available band for the rest of the iterations. The resulting method, EFWI for short, demonstrates surprising robustness to the inaccuracies in the extrapolated low frequency data. With two synthetic examples calibrated so that regular FWI needs to be initialized at 1 Hz to avoid local minima, we demonstrate that FWI based on an extrapolated [1, 5] Hz band, itself generated from data available in the [5, 15] Hz band, can produce reasonable estimations of the low wavenumber velocity models

Does socio-economic disadvantage lead to acting out? A reinvigoration of an old question

Research into socio-economic determinants of school deviance is inconclusive. Recently, scholars argued that economic deprivation, rather than SES background, affects delinquency. Using multilevel analyses on data of 9,174 students across 111 schools in 4 European cities (2013-2014), we investigate the association of SES and economic deprivation with school-deviant behavior. Furthermore, we study the role of academic self-efficacy. Lower-SES and deprived students might perceive goal blockage with regard to study-related goals, leading to deviant coping – that is self-efficacy as mediator – or self-efficacy might condition SES and deprivation effects – that is self-efficacy as moderator. Results showed that deprivation relates to school-deviant behavior. This association was not mediated, nor moderated, by academic self-efficacy. The relationship with SES was moderated by academic self-efficacy. We conclude that deprived and lower SES-students are prone to break school rules, the latter more so when feeling less competent at reaching academic goals

Compressed absorbing boundary conditions via matrix probing

Absorbing layers are sometimes required to be impractically thick in order to offer an accurate approximation of an absorbing boundary condition for the Helmholtz equation in a heterogeneous medium. It is always possible to reduce an absorbing layer to an operator at the boundary by layer-stripping elimination of the exterior unknowns, but the linear algebra involved is costly. We propose to bypass the elimination procedure, and directly fit the surface-to-surface operator in compressed form from a few exterior Helmholtz solves with random Dirichlet data. The result is a concise description of the absorbing boundary condition, with a complexity that grows slowly (often, logarithmically) in the frequency parameter.Comment: 29 pages with 25 figure

Fast Computation of Fourier Integral Operators

We introduce a general purpose algorithm for rapidly computing certain types of oscillatory integrals which frequently arise in problems connected to wave propagation and general hyperbolic equations. The problem is to evaluate numerically a so-called Fourier integral operator (FIO) of the form $\int e^{2\pi i \Phi(x,\xi)} a(x,\xi) \hat{f}(\xi) \mathrm{d}\xi$ at points given on a Cartesian grid. Here, $\xi$ is a frequency variable, $\hat f(\xi)$ is the Fourier transform of the input $f$, $a(x,\xi)$ is an amplitude and $\Phi(x,\xi)$ is a phase function, which is typically as large as $|\xi|$; hence the integral is highly oscillatory at high frequencies. Because an FIO is a dense matrix, a naive matrix vector product with an input given on a Cartesian grid of size $N$ by $N$ would require $O(N^4)$ operations. This paper develops a new numerical algorithm which requires $O(N^{2.5} \log N)$ operations, and as low as $O(\sqrt{N})$ in storage space. It operates by localizing the integral over polar wedges with small angular aperture in the frequency plane. On each wedge, the algorithm factorizes the kernel $e^{2 \pi i \Phi(x,\xi)} a(x,\xi)$ into two components: 1) a diffeomorphism which is handled by means of a nonuniform FFT and 2) a residual factor which is handled by numerical separation of the spatial and frequency variables. The key to the complexity and accuracy estimates is that the separation rank of the residual kernel is \emph{provably independent of the problem size}. Several numerical examples demonstrate the efficiency and accuracy of the proposed methodology. We also discuss the potential of our ideas for various applications such as reflection seismology.Comment: 31 pages, 3 figure

Velocity estimation via registration-guided least-squares inversion

This paper introduces an iterative scheme for acoustic model inversion where the notion of proximity of two traces is not the usual least-squares distance, but instead involves registration as in image processing. Observed data are matched to predicted waveforms via piecewise-polynomial warpings, obtained by solving a nonconvex optimization problem in a multiscale fashion from low to high frequencies. This multiscale process requires defining low-frequency augmented signals in order to seed the frequency sweep at zero frequency. Custom adjoint sources are then defined from the warped waveforms. The proposed velocity updates are obtained as the migration of these adjoint sources, and cannot be interpreted as the negative gradient of any given objective function. The new method, referred to as RGLS, is successfully applied to a few scenarios of model velocity estimation in the transmission setting. We show that the new method can converge to the correct model in situations where conventional least-squares inversion suffers from cycle-skipping and converges to a spurious model.Comment: 20 pages, 13 figures, 1 tabl

Stable optimizationless recovery from phaseless linear measurements

We address the problem of recovering an n-vector from m linear measurements lacking sign or phase information. We show that lifting and semidefinite relaxation suffice by themselves for stable recovery in the setting of m = O(n log n) random sensing vectors, with high probability. The recovery method is optimizationless in the sense that trace minimization in the PhaseLift procedure is unnecessary. That is, PhaseLift reduces to a feasibility problem. The optimizationless perspective allows for a Douglas-Rachford numerical algorithm that is unavailable for PhaseLift. This method exhibits linear convergence with a favorable convergence rate and without any parameter tuning