Choice Of Regularization Parameter Research Articles

Tikhonov regularized second-order plus first-order primal-dual dynamical systems with asymptotically vanishing damping for linear equality constrained convex optimization problems

In this paper, in the setting of Hilbert spaces, we consider a Tikhonov regularized second-order plus first-order primal-dual dynamical system with asymptotically vanishing damping for a linear equality constrained convex optimization problem. It is shown that convergence properties of the proposed inertial dynamical system depend upon the choice of the Tikhonov regularization parameter. When the Tikhonov regularization parameter decays rapidly to zero, we derive the fast convergence rates of the primal-dual gap, the objective residual, the feasibility violation and the gradient norm of the objective function along the generated trajectory. When the Tikhonov regularization parameter decreases slowly to zero, we prove the strong convergence of the primal trajectory of the Tikhonov regularized dynamical system to the minimal norm solution of the linear equality constrained convex optimization problem. Numerical experiments are performed to illustrate the efficiency of our approach.

Nesterov acceleration‐based iterative method for backward problem of distributed‐order time‐fractional diffusion equation

This paper is concerned with the inverse problem of determining the initial value of the distributed‐order time‐fractional diffusion equation from the final time observation data, which arises in some ultra‐slow diffusion phenomena in applied areas. Since the problem is ill‐posed, we propose an iterated regularization method based on the Nesterov acceleration strategy to deal with it. Convergence rates for the regularized approximation solution are given under both the a priori and a posteriori regularization parameter choice rules. It is shown that the proposed method can always yield the order optimal convergence rates as long as the iteration parameter which appears in the Nesterov acceleration strategy is chosen large enough. In numerical aspect, the main advantage of the proposed method lies in its simplicity. Specifically, due to the Nesterov acceleration strategy, only a few number of iteration steps are required to obtain the approximation solution, and at each iteration step, we only need to numerically solve the standard initial‐boundary value problem for the distributed‐order time‐fractional diffusion equation. Some numerical examples including one‐dimensional and two‐dimensional cases are presented to illustrate the validity and effectiveness of the proposed method.

Influencer detection meets network autoregression — Influential regions in the bitcoin blockchain

Known as an active global virtual money network, the Bitcoin blockchain, with millions of accounts, has played a continually increasingly important role in fund transition, digital payment, and hedging. We propose a method to Detect Influencers in Network AutoRegressive models (DINAR) via sparse-group regularization to detect regions influencing others across borders. For a granular analysis, we analyse whether the transaction size plays a role in the dynamics of the cross-border transactions in the network. With two-layer sparsity, DINAR enables discovering (1) the active regions with influential impact on the global digital money network and (2) whether changes in the size of the transaction affect the dynamic evolution of Bitcoin transactions. In the analysis of real data of the Bitcoin blockchain from Feb 2012 to December 2021, we find that influence from certain regions is linked to the economic need to use BTC, such as to circumvent sanctions, avoid high inflation, and to carry out transactions through off-shore markets. The effects are robust to different groupings, evaluation periods, and choices of regularization parameters.

Inference for possibly misspecified generalized linear models with nonpolynomial-dimensional nuisance parameters

Summary It is routine practice in statistical modelling to first select variables and then make inference for the selected model as in stepwise regression. Such inference is made upon the assumption that the selected model is true. However, without this assumption, one would not know the validity of the inference. Similar problems also exist in high-dimensional regression with regularization. To address these problems, we propose a dimension-reduced generalized likelihood ratio test for generalized linear models with nonpolynomial dimensionality, based on quasilikelihood estimation that allows for misspecification of the conditional variance. The test has nearly oracle performance when using the correct amount of shrinkage and has robust performance against the choice of regularization parameter across a large range. We further develop an adaptive data-driven dimension-reduced generalized likelihood ratio test and prove that, with probability going to one, it is an oracle generalized likelihood ratio test. However, in ultrahigh-dimensional models the penalized estimation may produce spuriously important variables that deteriorate the performance of the test. To tackle this problem, we introduce a cross-fitted dimension-reduced generalized likelihood ratio test, which is not only free of spurious effects, but robust against the choice of regularization parameter. We establish limiting distributions of the proposed tests. Their advantages are highlighted via theoretical and empirical comparisons to some competitive tests. An application to breast cancer data illustrates the use of our proposed methodology.

Regularized Stokeslet surfaces

A variation of the Method of Regularized Stokeslets (MRS) in three dimensions is developed for triangulated surfaces with a piecewise linear force density. The work extends the regularized Stokeslet segment methodology used for piecewise linear curves. By using analytic integration of the regularized Stokeslet kernel over the triangles, the regularization parameter ϵ is effectively decoupled from the spatial discretization of the surface. This is in contrast to the usual implementation of the method in which the regularization parameter is chosen for accuracy reasons to be about the same size as the spatial discretization. The validity of the method is demonstrated through several examples, including the flow around a rigidly translating/rotating sphere and in the squirmer model for ciliate self-propulsion. Notably, second order convergence in the spatial discretization for fixed ϵ is demonstrated. Considerations of mesh design and choice of regularization parameter are discussed, and the performance of the method is compared with existing quadrature-based implementations.

Magnetics only real-time equilibrium reconstruction on ASDEX Upgrade

Real-time reconstruction of the magnetic equilibrium provides fundamental control of plasma shape and position in a tokamak. Details of the implementation of the equilibrium reconstruction code developed for the ASDEX Upgrade tokamak (JANET++) are summarized. Cubic Hermite splines are introduced as current density basis functions for solving the Grad–Shafranov equation. The choice of the optimal Tikhonov regularization parameter is discussed. The code is validated by comparing the results of the equilibrium reconstruction with those of further equilibrium reconstructions available on ASDEX Upgrade (CLISTE and IDE). In a high time resolution study of a discharge with edge localized modes (ELM), the poloidal asymmetry of the fits and magnetic probe measurements suggest that the real-time equilibrium reconstruction captures the essential features of the current density redistribution in an ELMing edge plasma. An efficient algorithm to locate multiple X-points and identify the active one in advanced X-divertor and snowflake divertor configurations is presented.

Two Regularization Methods for Identifying the Spatial Source Term Problem for a Space-Time Fractional Diffusion-Wave Equation

In this paper, we delve into the challenge of identifying an unknown source in a space-time fractional diffusion-wave equation. Through an analysis of the exact solution, it becomes evident that the problem is ill-posed. To address this, we employ both the Tikhonov regularization method and the Quasi-boundary regularization method, aiming to restore the stability of the solution. By adhering to both a priori and a posteriori regularization parameter choice rules, we derive error estimates that quantify the discrepancies between the regularization solutions and the exact solution. Finally, we present numerical examples to illustrate the effectiveness and stability of the proposed methods.

Fixed and random effect selections in generalized linear mixed models.

Generalized linear mixed models are commonly used to describe relationships between correlated responses and covariates in medical research. In this paper, we propose a simple and easily implementable regularized estimation approach to select both fixed and random effects in generalized linear mixed model. Specifically, we propose to construct and optimize the objective functions using the confidence distributions of model parameters, as opposed to using the observed data likelihood functions, to perform effect selections. Two estimation methods are developed. The first one is to use the joint confidence distribution of model parameters to perform simultaneous fixed and random effect selections. The second method is to use the marginal confidence distributions of model parameters to perform the selections of fixed and random effects separately. With a proper choice of regularization parameters in the adaptive LASSO framework, we show the consistency and oracle properties of the proposed regularized estimators. Simulation studies have been conducted to assess the performance of the proposed estimators and demonstrate computational efficiency. Our method has also been applied to two longitudinal cancer studies to identify demographic and clinical factors associated with patient health outcomes after cancer therapies.

Determine source of Cahn–Hilliard equation with time‐fractional derivative by iterative variational regularization method

In this paper, we consider the inverse problem for the time‐fractional two‐dimensional Cahn–Hilliard equation. The ill‐posedness and a conditional stability of the inverse problem are proved. We present a new iterative variational regularization method to solve it. The convergence rates of the regularized solutions under the a priori regularization parameter choice rule and a posteriori regularization parameter choice rule are obtained. Numerical examples illustrate the effectiveness and stability of our proposed method.

Regularized weighted sine least-squares spectral analysis for gas electron diffraction data.

Here, we present a new approach for obtaining radial distribution functions (RDF) from the electron diffraction data using a regularized weighted sine least-squares spectral analysis. It allows for explicitly transferring the measured experimental uncertainties in the reduced molecular scattering function to the produced RDF. We provide a numerical demonstration, discuss the uncertainties and correlations in the RDFs, and suggest a regularization parameter choice criterion. The approach is also applicable for other diffraction data, e.g., for x-ray or neutron diffraction of liquid samples.

The iterative generalized quasi‐boundary value method for an inverse source problem of time fractional diffusion‐wave equation in a cylinder

This paper is devoted to solve an inverse source problem of time fractional diffusion‐wave equation in a cylinder. The ill‐posedness, existence, and uniqueness of the inverse source problem are proved. We propose an iterative generalized quasi‐boundary value regularization method to deal with the inverse source problem. The convergence rates of the regularized solution are obtained under the a priori regularization parameter choice rule and the a posteriori regularization parameter choice rule. Numerical examples are presented to show the validity and stability of the proposed method.

Filter Regularization Method for Inverse Source Problem of the Rayleigh–Stokes Equation

In this paper, we consider a problem of recovering a space-dependent source term for the Rayleigh–Stokes equation, where the additional data is the observation at a final moment $t = T$, which is ill-posed in the sense of Hadamard. Firstly, the uniqueness, ill-posedness and the conditional stability of inverse source problem is given. Next, we develop a filter regularization method to overcome the ill-posedness of the problem. Under reasonable a priori bound assumption about the source function, a Hölder-type error estimate of the regularized solution is proved for a priori regularization parameter choice rule. Furthermore, a logarithmic-type error estimate between the exact solution and the regularized solution is established based on a posteriori regularization parameter choice rule.

The Landweber Iterative Regularization Method for Identifying the Unknown Source of Caputo-Fabrizio Time Fractional Diffusion Equation on Spherically Symmetric Domain

In this article, the inverse problem for identifying the space-dependent source of time fractional diffusion equation on a spherically symmetric domain with Caputo–Fabrizio fractional derivative is discussed. This problem is a typical ill-posed problem and the Landweber iterative regularization method is used to obtain the approximation solution. The convergent error estimates under a priori regularization parameter choice rule and a posteriori regularization parameter choice rule are given, respectively. The numerical examples are given to show the effectiveness of the Landweber iterative regularization method.

An iterative generalized quasi-boundary value regularization method for the backward problem of time fractional diffusion-wave equation in a cylinder

In this paper, we consider the backward problem for a time fractional diffusion-wave equation in a cylinder. The ill-posedness and a conditional stability of the inverse problem are proved. Based on the generalized quasi-boundary value regularization method, we propose an iterative generalized quasi-boundary value regularization method to deal with the inverse problem, and this iterative method has a higher convergence rate. The convergence rates of the regularized solution under an a priori regularization parameter choice rule and an a posteriori regularization parameter choice rule are obtained. Numerical examples illustrate the effectiveness and stability of our proposed method.

New regularization techniques for ill-conditioning problems and their applications: Choices of regularization parameters

This is a continued study of Li et al. (2011) for the regularization of ill-conditioning problems, where the minimal singular value σmin of discrete matrices is infinitesimal. To remove the effects of high frequency caused by the singular vector of σmin better, we combine the Tikhonov regularization (TR) with the truncated singular value decomposition (TSVD) (simply denoted by T-TR). New computational formulas of the traditional condition number (Cond) and the effective condition number (Cond_eff) are derived, and a brief error analysis is made. The regularization parameter λ is involved in the TR, and its better (or optimal) choices are essential in both theory and computation. For ill-conditioning problems, stability is more imperative than accuracy. The most important criterion is to reduce the Cond. Consider the linear algebraic equations Ax = b. The optimal regularization parameter for the TR is derived as λ=σmaxσmin, where σmax and σmin(>0) are the maximal and the minimal singular values of matrix A, respectively. The L-curve techniques emphasize the ill-conditioning of ‖xλ‖ (see Hansen (1998)). Note that the norm ‖xλ‖ is only part of the Cond in stability. In fact, by using regularization, the Cond may be greatly reduced, while the errors do not increase much. The second important criterion is for both stability and solution accuracy. The sensitivity index proposed in Zhang et al. (2021) indicates the severity of ill-conditioning via accuracy for comparing different numerical methods and techniques. The sensitivity index is effective not only to select the source nodes in the method of fundamental solutions (MFS), but also to select the regularization parameter in the TR. The regularization choices of the T-TR are similar to those of the TR because differences of the Cond and the errors between the TR and the T-TR are insignificant. Numerical experiments are reported by the MFS for Laplace’s equation, to support the new regularization techniques proposed. For data fitting, imaging processing, pattern recognition, and machine learning, the ‖xλ‖ is, however, more important than Cond for ill-conditioning. The L-curve techniques are used in wide applications, and discussed in Hansen (1998). The parameter choices are rather complicated if the crossover region is not small. In this paper, we apply the sensitivity index to L-curves, the optimal parameter λL−curve can be found by the minimal sensitivity index. The λL−curve may also be found by the lowest line that passes the origin. The new algorithms are simple and effective.

An iterative method based on Nesterov acceleration for identifying space-dependent source term in a time-fractional diffusion-wave equation

In this paper, we investigate an inverse problem of identifying a space-dependent source in a time-fractional diffusion-wave equation by using the final time data. Since the problem is ill-posed, we propose an iterative method based on the Nesterov acceleration strategy to deal with it. Two kinds of convergence rates for the regularized solution are given under both the a priori and a posteriori regularization parameter choice rules. Compared with the classical regularization methods, our method is easy to implement with a fewer number of iterations and can always yield the order optimal error estimates provided the iteration parameter is chosen large enough. Some numerical examples including one-dimensional and two-dimensional cases are presented to illustrate the validity and effectiveness of the proposed method.

Parameter choices for sparse regularization with the norm *

We consider a regularization problem whose objective function consists of a convex fidelity term and a regularization term determined by the norm composed with a linear transform. Empirical results show that the regularization with the norm can promote sparsity of a regularized solution. The goal of this paper is to understand theoretically the effect of the regularization parameter on the sparsity of the regularized solutions. We establish a characterization of the sparsity under the transform matrix of the solution. When the objective function is block-separable or an error bound of the regularized solution to a known function is available, the resulting characterization can be taken as a regularization parameter choice strategy with which the regularization problem has a solution having a sparsity of a certain level. When the objective function is not block-separable, we propose an iterative algorithm which simultaneously determines the regularization parameter and its corresponding solution with a prescribed sparsity level. Moreover, we study choices of the regularization parameter so that the regularization term can alleviate the ill-posedness and promote sparsity of the resulting regularized solution. Numerical experiments demonstrate that the proposed algorithm is effective and efficient, and the choices of the regularization parameters can balance the sparsity of the regularized solution and its approximation to the minimizer of the fidelity function.

Identifying a space-dependent source term in distributed order time-fractional diffusion equations

<p style='text-indent:20px;'>The aim of this paper is to investigate an inverse problem of recovering a space-dependent source term governed by distributed order time-fractional diffusion equations in Hilbert scales. Such a problem is ill-posed and has important practical applications. For this problem, we propose a general regularization method based on the idea of the filter method. With a suitable source condition, we prove that the method is of optimal order under various choices of regularization parameter. One is based on the a priori regularization parameter choice rule and another one is the discrepancy principle. Finally, the capabilities of our method are illustrated by both the Tikhonov and the Landweber method.</p>

Source term identification of time‐fractional diffusion equation under Robin boundary condition

The source term identification of the time‐fractional diffusion equation under Robin boundary condition is studied. This problem is ill‐posed. Therefore, we apply Landweber iterative regularization method, fractional Landweber iterative regularization method, TSVD method, combining TSVD method, and fractional Landweber iterative regularization method, respectively. The comparisons of these four methods are given, which can help us select the most effective method. The error estimates between the regularized approximate solutions and the exact solution are given under the a priori and a posteriori regularization parameter choice rules. Finally, numerical examples verify the effectiveness of the methods.

Identification of the Initial Value for a Time-Fractional Diffusion Equation

In this manuscript, we study the inverse problem for identifying the initial value of a time-fractional diffusion equation in an axisymmetric region. This is an ill-posed problem, i.e., the solution does not depend continuously on the data. We choose the Landweber iterative regularization method to solve this problem. Under the a priori and the a posteriori regularization parameter choice rules, we present the error estimates between the regularization solutions and the exact solution. We present some examples to show this method’s effectiveness.