Negative-binomial Lindley distribution is a two-parameter discrete model that has been recently introduced in statistical literature. It has been used mostly in the analysis of insurance data and has shown to provide suitable fits as compared with the Poisson and Negative -binomial distributions. The parameters of the Negativebinomial Lindley model were estimated using separate maximum likeliho...

Derivative-based methods are developed for uncertainty quantification (UQ) in largescale ocean state estimation. The estimation system is based on the adjoint method for solving a least-squares optimization problem, whereby the state-of-the-art MIT general circulation model (MITgcm) is fit to observations. The UQ framework is applied to quantify Drake Passage transport uncertainties in a global...

For image contrast enhancement operation, it is a keypoint to obtain more natural enhanced results and keep details without distortion. In this paper, novel Hessian-matrix weighted histogram for proposed, which can improve the of smooth regions simultaneously restrain texture regions. proposed method, multi-scale fractional-order firstly utilized detect quantify information input image, explore...

A new method to represent and approximate rotation matrices is introduced. The method represents approximations of a rotation matrix Q with linearithmic complexity, i.e. with 1 2 n lg(n) rotations over pairs of coordinates, arranged in an FFT-like fashion. The approximation is “learned” using gradient descent. It allows to represent symmetric matrices H as QDQ where D is a diagonal matrix. It c...

The seminal paper by Barzilai and Borwein [IMA J. Numer. Anal. 8 (1988)] has given rise to an extensive investigation aimed at developing effective gradient methods, able to deal with large-scale optimization problems. Several steplength rules have been first designed for unconstrained quadratic problems and then extended to general nonlinear problems; these rules share the common idea of attem...

This short note considers Hessian updates for a sequential quadratically constrained quadratic programming (SQCQP) method. The SQCQP method is an iterative method to solve an inequality constrained optimization problem. At each iteration, the SQCQP method solves a quadratically constrained quadratic programming subproblem whose objective function and constraints are approximations to the object...