
Convexification for a 1D Hyperbolic Coefficient Inverse Problem with Single Measurement Data
A version of the convexification numerical method for a Coefficient Inve...
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Numerical viscosity solutions to HamiltonJacobi equations via a Carleman estimate and the convexification method
We propose a globally convergent numerical method, called the convexific...
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The gradient descent method for the convexification to solve boundary value problems of quasilinear PDEs and a coefficient inverse problem
We study the global convergence of the gradient descent method of the mi...
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Convexification numerical algorithm for a 2D inverse scattering problem with backscatter data
This paper is concerned with the inverse scattering problem which aims t...
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Convergence of a series associated with the convexification method for coefficient inverse problems
This paper is concerned with the convergence of a series associated with...
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Convexification for a 3D inverse scattering problem with the moving point source
For the first time, we develop in this paper the globally convergent con...
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Preconditioned Gradient Descent Algorithm for Inverse Filtering on Spatially Distributed Networks
Graph filters and their inverses have been widely used in denoising, smo...
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Convexificationbased globally convergent numerical method for a 1D coefficient inverse problem with experimental data
To compute the spatially distributed dielectric constant from the backscattering data, we study a coefficient inverse problem for a 1D hyperbolic equation. To solve the inverse problem, we establish a new version of Carleman estimate and then employ this estimate to construct a cost functional which is strictly convex on a convex bounded set with an arbitrary diameter in a Hilbert space. The strict convexity property is rigorously proved. This result is called the convexification theorem and is considered as the central analytical result of this paper. Minimizing this convex functional by the gradient descent method, we obtain the desired numerical solution to the coefficient inverse problems. We prove that the gradient descent method generates a sequence converging to the minimizer and we also establish a theorem confirming that the minimizer converges to the true solution as the noise in the measured data and the regularization parameter tend to zero. Unlike the methods that are based on optimization, our convexification method converges globally in the sense that it delivers a good approximation of the exact solution without requiring any initial guess. Results of numerical studies of both computationally simulated and experimental data are presented.
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