A fundamental problem in some applications including group testing and communications is to acquire the support of a K-sparse signal x, whose nonzero elements are 1, from an underdetermined noisy linear model. This paper first designs an algorithm called binary least squares (BLS) to reconstruct x and analyzes its complexity. Then, we establish two sufficient conditions for the exact reconstruction of x's support with K iterations of BLS based on the mutual coherence and restricted isometry property of the measurement matrix, respectively. Finally, extensive numerical tests are performed to compare the efficiency and effectiveness of BLS with those of batch orthogonal matching pursuit (Batch-OMP) which to our best knowledge is the fastest implementation of OMP, orthogonal least squares (OLS), compressive sampling matching pursuit (CoSaMP), hard thresholding pursuit (HTP), Newton-step-based iterative hard thresholding (NSIHT), Newton-step-based hard thresholding pursuit (NSHTP), binary matching pursuit (BMP) and 1-regularized least squares. Test results show that: (1) BLS can be 10-200 times more efficient than Batch-OMP, OLS, CoSaMP, HTP, NSIHT and NSHTP with higher probability of support reconstruction, and the improvement can be 20%-80%; (2) BLS has more than 25% improvement on the support reconstruction probability than the explicit BMP algorithm with a little higher computational complexity; (3) BLS is around 100 times faster than 1-regularized least squares with lower support reconstruction probability for small K and higher support reconstruction probability for large K. Numerical tests on the generalized space shift keying (GSSK) detection indicate that although BLS is a little slower than BMP, it is more efficient than the other seven tested sparse recovery algorithms, and although it is less effective than 1-regularized least squares, it is more effective than the other seven algorithms.

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In this paper, we establish the oracle inequalities of highly corrupted linear observations b = Ax0 + f0 + e ∈ R m .Here the vector x0 ∈ R n with n ≫ m is a (approximately) sparse signal and f0 ∈ R m is a sparse error vector with nonzero entries that can be possible infinitely large, e ∼ N (0, σ 2 Im) represents the Gaussian random noise vector.We extend the oracle inequality x -x0 2 2 i min{|x0(i)| 2 , σ 2 } for Dantzig selector and Lasso models in [E.

<p style="text-align: justify;">Consider the inverse scattering of time-harmonic acoustic waves by a mixed-type scatterer consisting of an inhomogeneous penetrable medium with a conductive transmission condition and various impenetrable obstacles with different kinds of boundary conditions. Based on the establishment of the well-posedness result of the direct problem, we intend to develop a modified factorization method to simultaneously reconstruct both the support of the inhomogeneous conductive medium and the shape and location of various impenetrable obstacles by means of the far-field data for all incident plane waves at a fixed wave number. Numerical examples are carried out to illustrate the feasibility and effectiveness of the proposed inversion algorithms.

Image restoration based on total variation has been widely studied owing to its edgepreservation properties. In this study, we consider the total variation infimal convolution (TV-IC) image restoration model for eliminating mixed Poisson-Gaussian noise. Based on the alternating direction method of multipliers (ADMM), we propose a complete splitting proximal bilinear constraint ADMM algorithm to solve the TV-IC model. We prove the convergence of the proposed algorithm under mild conditions. In contrast with other algorithms used for solving the TV-IC model, the proposed algorithm does not involve any inner iterations, and each subproblem has a closed-form solution. Finally, numerical experimental results demonstrate the efficiency and effectiveness of the proposed algorithm.

An explicit numerical method is developed for a class of non-autonomous time-changed stochastic differential equations, whose coefficients obey Hölder’s continuity in terms of the time variables and are allowed to grow super-linearly in terms of the state variables. The strong convergence of the method in the finite time interval is proved and the convergence rate is obtained. Numerical simulations are provided.

We present a decoupled, linearly implicit numerical scheme with energy stability and mass conservation for solving the coupled Cahn-Hilliard system.The time-discretization is done by leap-frog method with the scalar auxiliary variable (SAV) approach.It only needs to solve three linear equations at each time step, where each unknown variable can be solved independently.It is shown that the semi-discrete scheme has second-order accuracy in the temporal direction.Such convergence results are proved by a rigorous analysis of the boundedness of the numerical solution and the error estimates at different time-level.Numerical examples are presented to further confirm the validity of the methods.

Recently, the numerical methods for long-time dynamics of PDEs with weak nonlinearity have received more and more attention. For the nonlinear Schrödinger equation (NLS) with wave operator (NLSW) and weak nonlinearity controlled by a small value ε ∈ (0, 1], an exponential wave integrator Fourier pseudo-spectral (EWIFP) discretization has been developed (Guo et al., 2021) and proved to be uniformly accurate about ε up to the time at O(1/ε 2 ). However, the EWIFP method is not time symmetric and can not preserve the discrete energy. As we know, the time symmetry and energy-preservation are the important structural features of the true solution and we hope that this structure can be inherited along the numerical solution. In this work, we propose a time symmetric and energy-preserving exponential wave integrator Fourier pseudo-spectral (SEPEWIFP) method for the NLSW with periodic boundary conditions. Through rigorous error analysis, we establish uniform error bounds of the numerical solution at O(h m 0 + ε 2−β τ 2 ) up to the time at O(1/ε β ) for β ∈ [0, 2], where h and τ are the mesh size and time step, respectively, and m0 depends on the regularity conditions. The tools for error analysis mainly include cut-off technique and the standard energy method. We also extend the results on error bounds, energy-preservation and time symmetry to the oscillatory NLSW with wavelength at O(ε 2 ) in time which is equivalent to the NLSW with weak nonlinearity. Numerical experiments confirm that the theoretical results in this paper are correct. Our method is novel because that to the best of our knowledge there has not been any energy-preserving exponential wave integrator method for the NLSW.

The system of generalized absolute value equations (GAVE) has attracted more and more attention in the optimization community. In this paper, by introducing a smoothing function, we develop a smoothing Newton algorithm with non-monotone line search to solve the GAVE. We show that the non-monotone algorithm is globally and locally quadratically convergent under a weaker assumption than those given in most existing algorithms for solving the GAVE. Numerical results are given to demonstrate the viability and efficiency of the approach.

In this paper, a radial basis function method combined with the stochastic Galerkin method is considered to approximate elliptic optimal control problem with random coefficients. This radial basis function method is a meshfree approach for solving high dimensional random problem. Firstly, the optimality system of the model problem is derived and represented as a set of deterministic equations in high-dimensional parameter space by finite-dimensional noise assumption. Secondly, the approximation scheme is established by using finite element method for the physical space, and compactly supported radial basis functions for the parameter space. The radial basis functions lead to the sparsity of the stiff matrix with lower condition number. A residual type a posteriori error estimates with lower and upper bounds are derived for the state, co-state and control variables. An adaptive algorithm is developed to deal with the physical and parameter space, respectively. Numerical examples are presented to illustrate the theoretical results.