Traditional transmission travel-time tomography hinges on ray tracing techniques. We propose a PDE-based Eulerian approach to travel-time tomography so that we can avoid the cumbersome ray-tracing. We start from the eikonal equation, define a mismatching functional and derive the gradient of the nonlinear functional by an adjoint state method. The resulting forward and adjoint problems can be e...

The BFGS and DFP updates are perhaps the most successful Hessian and inverse Hessian approximations respectively for unconstrained minimization problems. This paper describes these methods in terms of two successive steps: rank reduction and rank restoration. From rank subtractivity and a powerful spectral result, the first step must necessarily result in a positive semidefinite matrix; and the...

Abstract Numerical optimization is a common technique in various areas of computational chemistry, molecular modeling and drug design. It key element 3D techniques, for example, the protein–ligand poses small‐molecule conformers. Here, often BFGS algorithm or variants thereof are used. However, tends to make unreasonable large changes optimized system under certain circumstances. This behavior ...

Image registration is a vital problem in medical imaging. It has many potential applications in clinical diagnosis (Diagnosis of cardiac, retinal, pelvic, renal, abdomen, liver, tissue etc.,). It is a process of aligning them in order to monitor subtle changes between the two. There are lots of image registration techniques evolved for soothing the image registration process. This paper propose...

An algorithm for solving large nonlinear optimization problems with simple bounds is de scribed It is based on the gradient projection method and uses a limited memory BFGS matrix to approximate the Hessian of the objective function It is shown how to take advan tage of the form of the limited memory approximation to implement the algorithm e ciently The results of numerical tests on a set of l...

Design optimization problems are often formulated as PDEconstrained optimization problems where the objective is a function of the output of a large-scale parametric dynamical system, obtained from the discretization of a PDE. To reduce its high computational cost, model order reduction techniques can be used. Two-sided Krylov-Padé type methods are very well suited since also the gradient to th...

Abstract. Timely and accurate estimation of reference evapotranspiration (ET0) is indispensable for agricultural water management efficient use. This study aims to estimate the amount ET0 with machine learning approaches by using minimum meteorological parameters in Corum region, which has an arid semi-arid climate regarded as important centre Turkey. In this context, monthly averages variables...

In the framework of ab initio simulations, search for energy minimum atomic structures is first step to perform in studying properties a system. One most used and efficient optimization algorithms quasi-Newton line-search scheme based on Broyden–Fletcher–Goldfarb–Shanno (Bfgs) Hessian updating formula. However, recent studies [Bitzek et al., Phys. Rev. Lett. 97, 170201 (2006) Guénolé Comput. Ma...

This paper addresses the equal circle packing problem, and proposes an efficient Quasi-physical Quasi-human (QPQH) algorithm. QPQH is based on a modified Broyden-Fletcher-GoldfarbShanno (BFGS) algorithm which we call the local BFGS and a new basin hopping strategy based on a Chinese idiom: alternate tension with relaxation. Starting from a random initial layout, we apply the local BFGS algorith...

The focus of this dissertation is on matrix decompositions that use a limited amount of computer memory, thereby allowing problems with a very large number of variables to be solved. Speci cally, we will focus on two applications areas: optimization and information retrieval. We introduce a general algebraic form for the matrix update in limited-memory quasiNewton methods. Many well-known metho...