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Quadratic programming

by Health Care Manag Sci, René Bekker, Paulien M. Koeleman, R. Bekker (b, P. M. Koeleman
"... © The Author(s) 2011. This article is published with open access at Springerlink.com Abstract Variability in admissions and lengths of stay inherently leads to variability in bed occupancy. The aim of this paper is to analyse the impact of these sources of variability on the required amount of capac ..."
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is not Poisson. Also, given a structural weekly admission pattern, we apply a timedependent analysis to determine the mean offered load per day. This time-dependent analysis is combined with a Quadratic Programming model to determine the optimal number of elective admissions per day, such that an average desired

Sequential Quadratic Programming

by Paul T. Boggs, Jon W. Tolle , 1995
"... this paper we examine the underlying ideas of the SQP method and the theory that establishes it as a framework from which effective algorithms can ..."
Abstract - Cited by 166 (4 self) - Add to MetaCart
this paper we examine the underlying ideas of the SQP method and the theory that establishes it as a framework from which effective algorithms can

quadratic programs

by Fredrik Carlsson, Anders Forsgren , 2008
"... conjugate-gradient based approach for approximate solutions of ..."
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conjugate-gradient based approach for approximate solutions of

GORDIAN: VLSI placement by quadratic programming and slicing optimization

by Jürgen M. Kleinhans, Georg Sigl, Frank M. Johannes, Kurt J. Antreich - IEEE TRANS. COMPUTER-AIDED DESIGN , 1991
"... In this paper we present a new placement method for cell-based layout styles. It is composed of alternating and interacting global optimization and partitioning steps that are followed by an optimization of the area utilizaiton. Methods using the divide-and-conquer paradigm usually lose the global v ..."
Abstract - Cited by 225 (7 self) - Add to MetaCart
view by generating smaller and smaller subproblems. In contrast, GORDIAN maintains the simultaneous treatment of all cells over all global optimization steps, thereby considering constraints that reflect the current dissection of the circuit. The global optimizations are performed by solving quadratic

LOQO: An interior point code for quadratic programming

by Robert J. Vanderbei , 1994
"... ABSTRACT. This paper describes a software package, called LOQO, which implements a primaldual interior-point method for general nonlinear programming. We focus in this paper mainly on the algorithm as it applies to linear and quadratic programming with only brief mention of the extensions to convex ..."
Abstract - Cited by 194 (10 self) - Add to MetaCart
ABSTRACT. This paper describes a software package, called LOQO, which implements a primaldual interior-point method for general nonlinear programming. We focus in this paper mainly on the algorithm as it applies to linear and quadratic programming with only brief mention of the extensions to convex

A Quadratic Programming Bibliography

by Nicholas I. M. Gould, Philippe L. Toint , 2001
"... A method for restoring an optical image which is subjected to low-pass frequency filtering is presented. It is assumed that the object whose image is restored is of finite spatial extent. The problem is treated as an algebraic image-restoration problem which is then solved as a quadratic programming ..."
Abstract - Cited by 8 (0 self) - Add to MetaCart
programming problem with bounded variables. The regularization technique for the ill-posed system is to replace the consistent system of the quadratic programming problem by an approximate system of smaller rank. The rank which gives a best or near-best solution is estimated. This method is a novel one

The Sequential Quadratic Programming Method

by Roger Fletcher , 2007
"... Sequential (or Successive) Quadratic Programming (SQP) is a technique for the solution of Nonlinear Programming (NLP) problems. It is, as we shall see, an idealized concept, permitting and indeed necessitating many variations and modifications before becoming available as part of a reliable and effi ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
Sequential (or Successive) Quadratic Programming (SQP) is a technique for the solution of Nonlinear Programming (NLP) problems. It is, as we shall see, an idealized concept, permitting and indeed necessitating many variations and modifications before becoming available as part of a reliable

Preprocessing for quadratic programming

by Nicholas I. M. Gould, Philippe L. Toint , 2002
"... Techniques for the preprocessing of (not-necessarily convex) quadratic programs are discussed. Most of the procedures extend known ones from the linear to quadratic cases, but a few new preprocessing techniques are introduced. The implementation aspects are also discussed. Numerical results are nal ..."
Abstract - Cited by 14 (1 self) - Add to MetaCart
Techniques for the preprocessing of (not-necessarily convex) quadratic programs are discussed. Most of the procedures extend known ones from the linear to quadratic cases, but a few new preprocessing techniques are introduced. The implementation aspects are also discussed. Numerical results

On non-approximability for quadratic programs

by Sanjeev Arora, Eli Berger, Elad Hazan, Guy Kindler, Muli Safra - IN 46TH ANNUAL SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE , 2005
"... This paper studies the computational complexity of the following type of quadratic programs: given an arbitrary matrix whose diagonal elements are zero, find x ∈ {−1, 1} n that maximizes x T Mx. This problem recently attracted attention due to its application in various clustering settings, as well ..."
Abstract - Cited by 28 (4 self) - Add to MetaCart
This paper studies the computational complexity of the following type of quadratic programs: given an arbitrary matrix whose diagonal elements are zero, find x ∈ {−1, 1} n that maximizes x T Mx. This problem recently attracted attention due to its application in various clustering settings, as well

Approximating Quadratic Programming With Bound Constraints

by Yinyu Ye - Mathematical Programming , 1997
"... We consider the problem of approximating the global maximum of a quadratic program (QP) with n variables subject to bound constraints. Based on the results of Goemans and Williamson [4] and Nesterov [6], we show that a 4=7 approximate solution can be obtained in polynomial time. Key words. Quadratic ..."
Abstract - Cited by 78 (12 self) - Add to MetaCart
We consider the problem of approximating the global maximum of a quadratic program (QP) with n variables subject to bound constraints. Based on the results of Goemans and Williamson [4] and Nesterov [6], we show that a 4=7 approximate solution can be obtained in polynomial time. Key words
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