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a nonquadratic cost function
, 2004
"... Background removal from spectra by designing and minimising ..."
Halfquadratic cost functions for phase unwrapping,”Optics
 Letters
, 2004
"... We present a generic regularized formulation, based on robust half–quadratic regularization, for unwrapping noisy and discontinuous wrapped phase maps. Two cases are presented: the convex and the non–convex one. The unwrapped phase with the convex formulation is unique and robust to noise; however, ..."
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Cited by 13 (4 self)
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We present a generic regularized formulation, based on robust half–quadratic regularization, for unwrapping noisy and discontinuous wrapped phase maps. Two cases are presented: the convex and the non–convex one. The unwrapped phase with the convex formulation is unique and robust to noise; however
Linear Optimal Control Problems and Quadratic Cost Functions Estimation
"... Abstract — Inverse optimal control is a classical problem of control theory. It was first posed by Kalman in the early sixties. The problem, as addressed in literature, answers to the following two questions: (a) Given system matrices A,B and a gain matrix K, find necessary and sufficient conditions ..."
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Cited by 3 (0 self)
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Abstract — Inverse optimal control is a classical problem of control theory. It was first posed by Kalman in the early sixties. The problem, as addressed in literature, answers to the following two questions: (a) Given system matrices A,B and a gain matrix K, find necessary and sufficient conditions for K to be the optimal of an infinite time LQ problem. (b) Determine all weight matrices Q, R and S which yield the given gain matrix K. In this paper, we tackle a related, but different problem. Starting from the state trajectories of an LTI system, identify the matrices Q, R and S that have generated those trajectories. Both infinite and finite time optimal control problems are considered. The motivation lies in the characterization of the trajectories of LTI systems in terms of the control task. I.
PERFORMANCE MONITORING OF PI CONTROLLERS USING A SYNTHETIC GRADIENT OF A QUADRATIC COST FUNCTION
"... Abstract The paper shows how a synthetic gradient of a quadratic cost function can be used to monitor performance. The method requires a model of the closed loop system. In the current paper this is obtained from the recommended tuning rule. The tuning rule requires a simple model of the process. Th ..."
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Abstract The paper shows how a synthetic gradient of a quadratic cost function can be used to monitor performance. The method requires a model of the closed loop system. In the current paper this is obtained from the recommended tuning rule. The tuning rule requires a simple model of the process
Approximate Solutions to the HamiltonJacobi Equations for Generating Functions with a Quadratic Cost Function with Respect to the Input
"... Abstract: An algorithm to approximate a solution to the HamiltonJacobi equation for a generating function for a nonlinear optimal control problem with a quadratic cost function with respect to the input is proposed in this paper. An approximate generating function based on Taylor series up to the o ..."
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Cited by 1 (1 self)
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Abstract: An algorithm to approximate a solution to the HamiltonJacobi equation for a generating function for a nonlinear optimal control problem with a quadratic cost function with respect to the input is proposed in this paper. An approximate generating function based on Taylor series up
Gaussian Cheap Talk Game with Quadratic Cost Functions: When Herding Between Strategic Senders is a Virtue∗
"... We consider a Gaussian cheap talk game with quadratic cost functions. The cost function of the receiver is equal to the estimation error variance, however, the cost function of each senders contains an extra term which is captured by its private information. Following the cheap talk literature, we m ..."
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We consider a Gaussian cheap talk game with quadratic cost functions. The cost function of the receiver is equal to the estimation error variance, however, the cost function of each senders contains an extra term which is captured by its private information. Following the cheap talk literature, we
Background removal from spectra by designing and minimising a nonquadratic cost function, Chemometrics and Intelligent Laboratory Systems 76 (2
, 2005
"... In this paper, the problem of estimating the background of a spectrum is addressed. We propose to fit this background to a loworder polynomial, but rather than determining the polynomial parameters that minimise a leastsquares criterion (i.e. a quadratic cost function), nonquadratic cost function ..."
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Cited by 14 (1 self)
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In this paper, the problem of estimating the background of a spectrum is addressed. We propose to fit this background to a loworder polynomial, but rather than determining the polynomial parameters that minimise a leastsquares criterion (i.e. a quadratic cost function), nonquadratic cost
Fuzzy Geometric Programming in Multivariate Stratified Sample Surveys in Presence of NonResponse with Quadratic Cost Function
"... In this paper, the problem of nonresponse with significant travel costs in multivariate stratified sample surveys has been formulated of as a MultiObjective Geometric Programming Problem (MOGPP). The fuzzy programming approach has been described for solving the formulated MOGPP. The formulated MOG ..."
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In this paper, the problem of nonresponse with significant travel costs in multivariate stratified sample surveys has been formulated of as a MultiObjective Geometric Programming Problem (MOGPP). The fuzzy programming approach has been described for solving the formulated MOGPP. The formulated
SNOPT: An SQP Algorithm For LargeScale Constrained Optimization
, 2002
"... Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first deriv ..."
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Cited by 597 (24 self)
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Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first
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