- Research Article
- Open Access
A Note on Convergence Analysis of an SQP-Type Method for Nonlinear Semidefinite Programming
© Yun Wang et al. 2008
- Received: 29 August 2007
- Accepted: 23 November 2007
- Published: 3 December 2007
We reinvestigate the convergence properties of the SQP-type method for solving nonlinear semidefinite programming problems studied by Correa and Ramirez (2004). We prove, under the strong second-order sufficient condition with the sigma term, that the local SQP-type method is quadratically convergent and the line search SQP-type method is globally convergent.
- Line Search
- Merit Function
- Constraint Qualification
- Augmented Lagrangian Method
- Nondegeneracy Condition
Fares et al. (2002)  studied robust control problems via sequential semidefinite programming technique. They obtained the local quadratic convergence rate of the proposed SQP-type method and employed a partial augmented Lagrangian method to deal with the problems addressed there. Correa and Ramirez (2004)  systematically studied an SQP-type method for solving nonlinear SDP problems and analyzed the convergence properties, they obtained the global convergence and local quadratic convergence rate. Both papers used the same subproblems to generate search directions, but employed different merit functions for line search. The convergence analysis of both papers depends on a set of second-order conditions without sigma term, which is stronger than no gap second-order optimality condition with sigma term.
Comparing with the work by Correa and Ramirez (2004) , in this note, we make some modifications to the convergence analysis, and prove that all results in  still hold under the strong second-order sufficient condition with the sigma term.
It should be pointed out that the importance of exploring numerical methods for solving nonlinear semidefinite programming problems has been recognized in the optimization community. For instance, Kočvara and Stingl [3, 4] have developed PENNLP and PENBMI codes for nonlinear semidefinite programming and semidefinite programming with bilinear matrix inequality constraints, respectively. Recently, Sun et al. (2007)  considered the rate of convergence of the classical augmented Lagrangian method and Noll (2007)  investigated the convergence properties of a class of nonlinear Lagrangian methods.
In Section 2, we introduce preliminaries including differential properties of the metric projector onto and optimality conditions for problem (1.1). In Section 3, we prove, under the strong second-order sufficient condition with the sigma term, that the local SQP-type method has the quadratic convergence rate and the global algorithm with line search is convergence.
2.1. Semismoothness of the Metric Projector
In this subsection, let , , and be three arbitrary finite-dimensional real spaces with a scalar product and its norm . We introduce some properties of the metric projector, especially its strong semismoothness.
The next lemma is about the generalized Jacobian for composite functions, proposed in .
2.2. Optimality Conditions
We state the strong second-order sufficient condition (SSOSC) coming from .
The next proposition relates the SSOSC and nondegeneracy condition to nonsingularity of Clarke's Jacobian of the mapping defined by (2.11). The details of this proof can be found in .
In this section, we analyze the local quadratic convergence rate of an SQP-type method and then prove that the SQP-type method proposed in  is globally convergent. The analysis is based on the strong second-order sufficient condition, which is weaker than the conditions used in [1, 2].
3.1. Local Convergence Rate
From item (f) of [7, Theorem 4.1], we obtain the error between and directly.
Now we are in a position to state that the sequence of primal-dual points generated by Algorithm 3.1 has quadratic convergence rate.
Suppose that are twice continuously differentiable and their derivatives are locally Lipschitz in a neighborhood of a local solution to (1.1). Suppose nondegeneracy condition (2.12) and SSOSC (2.14) hold at . Consider Algorithm 3.1, in which is a minimum norm stationary point of the tangential quadratic problem (3.1). Then there exists a neighborhood of such that, if , Algorithm 3.1 is well defined and the sequence converges quadratically to .
3.2. The Global Convergence
where is the smallest eigenvalue of , denote and is a positive constant. The following proposition comes from  directly.
Now we are in a position to state the global convergence of the line search SQP Algorithm 3.7, whose proof can be found in .
Suppose that , , are continuously differentiable and their derivatives are Lipschitz continuous. Consider Algorithm 3.7, if positive definite matrices and are bounded, then one of the following situations occurs:
The research is supported by the National Natural Science Foundation of China under Project no. 10771026 and by the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry of China.
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