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<ArticleSet>
<Article>
<Journal>
				<PublisherName>Iranian Mathematical Society (IMS)</PublisherName>
				<JournalTitle>Bulletin of the Iranian Mathematical Society</JournalTitle>
				<Issn>1017-060X</Issn>
				<Volume>38</Volume>
				<Issue>3</Issue>
				<PubDate PubStatus="epublish">
					<Year>2012</Year>
					<Month>09</Month>
					<Day>15</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Superlinearly convergent exact penalty projected structured Hessian updating schemes for constrained nonlinear least squares: asymptotic analysis</ArticleTitle><FirstPage>767</FirstPage>
			<LastPage>786</LastPage>
			<Language>en</Language>
<AuthorList>
<Author>
					<FirstName>N. </FirstName>
					<LastName>Mahdavi-Amiri</LastName>
					<Affiliation>Sharif University of Technology</Affiliation>
				</Author>
<Author>
					<FirstName>Mohammad Reza </FirstName>
					<LastName>Ansari</LastName>
					<Affiliation>Sharif University of Technology</Affiliation>
				</Author>
</AuthorList>
			<History>
				<PubDate PubStatus="received">
					<Year>2010</Year>
					<Month>11</Month>
					<Day>03</Day>
				</PubDate>
			</History>
		<Abstract><![CDATA[We present a structured algorithm for solving constrained nonlinear least squares problems, and establish its local two-step Q-superlinear convergence. The approach is based on an adaptive structured scheme due to Mahdavi-Amiri and Bartels of the exact penalty method of Coleman and Conn for nonlinearly constrained optimization problems. The structured adaptation also makes use of the ideas of Nocedal and Overton for handling the quasi-Newton updates of projected Hessians. We discuss the comparative results of the testing of our programs and three nonlinear programming codes from KNITRO on some randomly generated test problems due to Bartels and Mahdavi-Amiri. The results indeed confirm the practical significance of our special considerations for the inherent structure of the least squares.]]></Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Constrained nonlinear programming</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">exact penalty method</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">nonlinear least squares</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">projected structured Hessian update</Param>
			</Object>
		</ObjectList>
</Article>
</ArticleSet>