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Part of book or chapter of book . 1998 . Peer-reviewed
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SQ2P, Sequential Quadratic Constrained Quadratic Programming

descriptionPublicationkeyboard_double_arrow_right Part of book or chapter of book , Other literature type 01 Jan 1998Publisher:Springer US
Authors: Serge Kruk; Henry Wolkowicz;

doi: 10.1007/978-1-4613-3335-7_8

SQ2P, Sequential Quadratic Constrained Quadratic Programming

Abstract

We follow the popular approach for unconstrained minimization, i.e. we develop a local quadratic model at a current approximate minimizer in conjunction with a trust region. We then minimize this local model in order to find the next approximate minimizer. Asymptotically, finding the local minimizer of the quadratic model is equivalent to applying Newton’s method to the stationarity condition.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
8
Average
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Average
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