Superlinear convergence of a stabilized SQP-type method for nonlinear semidefinite programming

The stabilized sequential quadratic programming (SQP) method can effectively deal with degenerate nonlinear optimization problems. In the case of nonunique Lagrange multipliers associated with a stationary point of an optimization problem, the stabilized SQP method still obtains superlinear and/or q...

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Published in:Journal of applied mathematics & computing Vol. 71; no. 1; pp. 1309 - 1338
Main Authors: Zhang, Dongdong, Chen, Zhongwen
Format: Journal Article
Language:English
Published: Dordrecht Springer Nature B.V 01.02.2025
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ISSN:1598-5865, 1865-2085
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Abstract The stabilized sequential quadratic programming (SQP) method can effectively deal with degenerate nonlinear optimization problems. In the case of nonunique Lagrange multipliers associated with a stationary point of an optimization problem, the stabilized SQP method still obtains superlinear and/or quadratic convergence to a primal-dual solution. In this paper, we propose a stabilized sequential quadratic semidefinite programming method for degenerate nonlinear semidefinite programming problems. Under the local error bound condition, the strict complementarity condition, and the second-order sufficient condition, we establish superlinear and/or quadratic convergence of the proposed method.
AbstractList The stabilized sequential quadratic programming (SQP) method can effectively deal with degenerate nonlinear optimization problems. In the case of nonunique Lagrange multipliers associated with a stationary point of an optimization problem, the stabilized SQP method still obtains superlinear and/or quadratic convergence to a primal-dual solution. In this paper, we propose a stabilized sequential quadratic semidefinite programming method for degenerate nonlinear semidefinite programming problems. Under the local error bound condition, the strict complementarity condition, and the second-order sufficient condition, we establish superlinear and/or quadratic convergence of the proposed method.
Author Zhang, Dongdong
Chen, Zhongwen
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Snippet The stabilized sequential quadratic programming (SQP) method can effectively deal with degenerate nonlinear optimization problems. In the case of nonunique...
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StartPage 1309
SubjectTerms Algorithms
Convergence
Lagrange multiplier
Methods
Neighborhoods
Optimization
Quadratic programming
Semidefinite programming
Title Superlinear convergence of a stabilized SQP-type method for nonlinear semidefinite programming
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