this paper presents a simplifiedlagrangian multiplier based algorithm to solve the fixed head hydrothermalscheduling problem. in fixed head hydrothermal scheduling problem, waterdischarge rate is modeled as quadratic function of hydropower generation andfuel cost is modeled as quadratic function of thermal power generation. thepower output of each hydro unit varies with the rate of water discha...

Purpose This paper generalizes the quadratic framework introduced by Le Courtois (2016) and Sumpf (2018), to obtain new credibility premiums in balanced case, i.e. under squared error loss function. More precisely, authors construct a net function where are estimated based on values of past observations parametric non-parametric approaches, this is useful for practitioner who wants explicitly t...

& Conclusions-Risk analysis is discussed within a Bayes framework. Traditionally, Bayes parameter estimation is based on a quadratic loss-function. This paper introduces an alternative asymmetric precautionary loss-function, derives its main features, and presents a general class of precautionary loss-functions with the quadratic loss-function as a special case. These loss functions approach in...

A novel generalization of linear scale space is presented. The generalization allows for a sparse approximation of the function at a certain scale. To start with, we first consider the Tikhonov regularization viewpoint on scale space theory [15]. The sparsification is then obtained using ideas from support vector machines [22] and based on the link between sparse approximation and support vecto...

Abstract The current bottleneck of globally solving mixed-integer (nonconvex) quadratically constrained problems (MIQCPs) is still to construct strong but computationally cheap convex relaxations, especially when dense quadratic functions are present. We propose a cutting-surface method based on multiple diagonal perturbations to derive convex quadratic relaxations for nonconvex quadratic probl...

The optimal motion generation problem is solved subject to various actuator constraints while the motion is constrained to an arbitrary path. The considered objective function is a weighted time energy Jitnction while most of existing methods consider only the time-optimal problem. We present some simulation results using a mathematical programming technique (Sequential Quadratic Programming) e...

Since 2004, the Computational Optimization and Applications (COAP) editorial board has selected a paper from the preceding year’s COAP publications for the “Best Paper Award.” The award competition among papers published in 2008 culminated in a tie between two papers. This article concerns the award winning work of Peter Hahn, Bum-Jin Kim, Monique Guignard-Spielberg and Yi-Rong Zhu at the Unive...

In this paper, line search based on Sequential Quadratic Programming is implemented in order to find a solution to Fuzzy Relation Equations. Sequential Quadratic Programming is a gradient-based method that uses a quadratic estimation of the objective function in each iteration’s neighborhood. Unlike analytical approaches, the method can handle equations with any combinations of t-norms and t-co...

The estimation of quadratic functions of a multivariate normal mean is an inferential problem which, while being simple to state and often encountered in practice, leads to surprising complications both from frequentist and Bayesian points of view. The drawbacks of Bayesian inference using the constant noninformative prior are now well established and we consider in this paper the advantages an...

Abstract This paper concerns a method of selecting a subset of features for a sequential logit model. Tanaka and Nakagawa (2014) proposed a mixed integer quadratic optimization formulation for solving the problem based on a quadratic approximation of the logistic loss function. However, since there is a significant gap between the logistic loss function and its quadratic approximation, their fo...