Research on 3D SIP Conjugate Gradient Inversion Algorithm with Parameter Range Constraints

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

3D gravity data-space inversion with sparseness and bound constraints

One of the most remarkable basis of the gravity data inversion is the recognition of sharp boundaries between an ore body and its host rocks during the interpretation step. Therefore, in this work, it is attempted to develop an inversion approach to determine a 3D density distribution that produces a given gravity anomaly. The subsurface model consists of a 3D rectangular prisms of known sizes ...

متن کامل

3d gravity data-space inversion with sparseness and bound constraints

one of the most remarkable basis of the gravity data inversion is the recognition of sharp boundaries between an ore body and its host rocks during the interpretation step. therefore, in this work, it is attempted to develop an inversion approach to determine a 3d density distribution that produces a given gravity anomaly. the subsurface model consists of a 3d rectangular prisms of known sizes ...

متن کامل

An Efficient Conjugate Gradient Algorithm for Unconstrained Optimization Problems

In this paper, an efficient conjugate gradient method for unconstrained optimization is introduced. Parameters of the method are obtained by solving an optimization problem, and using a variant of the modified secant condition. The new conjugate gradient parameter benefits from function information as well as gradient information in each iteration. The proposed method has global convergence und...

متن کامل

Conjugate Gradient Algorithm with Data Selective Updating

This paper applies data selective updating to the Modified Conjugate Gradient algorithm. In search for a new conjugategradient-like filtering algorithm, two different approaches are developed: the first one results in the recently proposed set-membership affine projection (SM-AP) algorithm and the second one reduces the computational requirements of the modified congujate gradient algorithm whi...

متن کامل

Stochastic Conjugate Gradient Algorithm with Variance Reduction

Conjugate gradient methods are a class of important methods for solving linear equations and nonlinear optimization. In our work, we propose a new stochastic conjugate gradient algorithm with variance reduction (CGVR) and prove its linear convergence with the Fletcher and Revves method for strongly convex and smooth functions. We experimentally demonstrate that the CGVR algorithm converges fast...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Mathematical Problems in Engineering

سال: 2021

ISSN: 1563-5147,1024-123X

DOI: 10.1155/2021/6617794