Shrinkage-Thresholding Enhanced Born Iterative Method for Solving 2D Inverse Electromagnetic Scattering Problem

the algorithm for solving the inverse numerical range problem

برد عددی ماتریس مربعی a را با w(a) نشان داده و به این صورت تعریف می کنیم w(a)={x8ax:x ?s1} ، که در آن s1 گوی واحد است. در سال 2009، راسل کاردن مساله برد عددی معکوس را به این صورت مطرح کرده است : برای نقطه z?w(a)، بردار x?s1 را به گونه ای می یابیم که z=x*ax، در این پایان نامه ، الگوریتمی برای حل مساله برد عددی معکوس ارانه می دهیم.

A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems

We consider the class of iterative shrinkage-thresholding algorithms (ISTA) for solving linear inverse problems arising in signal/image processing. This class of methods, which can be viewed as an extension of the classical gradient algorithm, is attractive due to its simplicity and thus is adequate for solving large-scale problems even with dense matrix data. However, such methods are also kno...

Solving inverse problems for optical scanning holography using an adaptively iterative shrinkage-thresholding algorithm.

Optical scanning holography (OSH) records a three-dimensional object into a two-dimensional hologram through two-dimensional optical scanning. The recovery of sectional images from the hologram, termed as an inverse problem, has been previously implemented by conventional methods as well as the use of l₂ norm. However, conventional methods require time consuming processing of section by section...

The Linear Sampling Method for Solving the Electromagnetic Inverse Scattering Problem

A simple method for solving the inverse scattering problem

The inverse scattering problem of determining the boundary impedance from knowledge of the time harmonic incident wave and the far-field pattern of the scattered wave is considered. We use the finite-difference approximation for the Helmholtz equation along with the exact radiation condition for the discrete equation. The approach involves two steps. First, we reduce the problem to a well-posed...