Abstract
We derive a new genericity result for nonlinear semidefinite programming (NLSDP). Namely, almost all linear perturbations of a given NLSDP are shown to be nondegenerate. Here, nondegeneracy for NLSDP refers to the transversality constraint qualification, strict complementarity and second-order sufficient condition. Due to the presence of the second-order sufficient condition, our result is a nontrivial extension of the corresponding results for linear semidefinite programs (SDP) from Alizadeh et al. (Math Program 77(2, Ser. B):111–128, 1997). The proof of the genericity result makes use of Forsgren’s derivation of optimality conditions for NLSDP in Forsgren (Math Program 88(1, Ser. A):105–128, 2000). Due to the latter approach, the positive semidefiniteness of a symmetric matrix G(x), depending continuously on x, is locally equivalent to the fact that a certain Schur complement S(x) of G(x) is positive semidefinite. This yields a reduced NLSDP by considering the new semidefinite constraint \(S(x) \succeq 0\), instead of \(G(x) \succeq 0\). While deriving optimality conditions for the reduced NLSDP, the well-known and often mentioned “H-term” in the second-order sufficient condition vanishes. This allows us to access the proof of the genericity result for NLSDP.
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Notes
Robinson’s constraint qualification, a generalization of Mangasarian–Fromovitz constraint qualification (MFCQ), and strict complementarity.
The H-term is a special case of the “\(\sigma \)-term” from [7].
W.r.t. the so-called strong \(C^k\)-topology on the space of involved functions; see [21] for details.
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Acknowledgments
The authors would like to thank both anonymous referees, the associate editor and the co-editor for their valuable remarks and suggestions.
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This work was supported by Conicyt Chile under the Grant Fondecyt Nr. 1120028.
Appendix
Appendix
Appendix is devoted to the proofs of Lemmata 4 and 5.
Proof of Lemma 4
First, we derive a representation of DS(x) in terms of DG(x). Note that for x close to \(\bar{x}\) and an arbitrary \(h \in \mathbb {R}^n\) it holds:
Thus, using the formula (13) for S(x), we calculate
Now, let \(\omega \in \Lambda ^R\) be given, and let \(\Omega := \varOmega (\omega )\). One easily verifies that \(\Omega = W \omega W^\top \), where \(W := W(\bar{x})\). Since the columns of W span the kernel of \(G(\bar{x})\), we have
The application of basic symmetry properties of the trace yields
where we omit the argument \(\bar{x}\) in the involved matrices. This implies
and, hence, \(\Omega \in \Lambda \).
Now, we show that for each \(\Omega \in \Lambda \) there exists a unique \(\omega \in \Lambda ^R\) with \(\Omega = W \omega W^\top \). The uniqueness follows directly from the fact that the columns of W are linearly independent. Further, we set
where Q is a matrix with the property that the columns of W Q are orthonormal and span the same linear subspace as those of W. First, we show that \(W \omega W^\top \) is equal to \(\Omega \). Indeed, since the columns of WQ form an orthogonal basis of the kernel of G(x) and \(\Omega \bullet G(x) = 0\), there exists a diagonal matrix \(\Omega _\Delta \) such that \(\Omega = (WQ) \Omega _\Delta (WQ)^\top \). We calculate:
It remains to show that \(\omega \) is an element of \(\Lambda ^R\). Since \(\Omega \) is positive semidefinite, also \(\omega \) is. Analogously as in (29), one shows that \(D_x L^R (\bar{x},\omega ) = D_x L (\bar{x}, \Omega ) = 0\). The fact that \(\omega \bullet S(\bar{x}) = 0\) follows from \(S(\bar{x}) = W^\top G(\bar{x}) W\). Overall, we conclude that \(\omega \in \Lambda ^R\). \(\square \)
Proof of Lemma 5 (a)
By definition, the transversality constraint qualification means that \({{\mathrm{Im}}}DG(\bar{x}) + T_{G(\bar{x})}\mathbb {S}^m_r\) is equal to \(\mathbb {S}^m\). This is equivalent to the fact that the orthogonal complement \(( {{\mathrm{Im}}}DG(\bar{x}) + T_{G(\bar{x})} \mathbb {S}^m_r )^\perp \) – w.r.t. the trace inner product “\(\bullet \)” – is equal to \(\{ 0 \}\). Since the orthogonal complement is given by the intersection \(({{\mathrm{Im}}}DG(\bar{x}))^\perp \cap (T_{G(\bar{x})} \mathbb {S}^m_r )^\perp \), transversality constraint qualification is equivalent to \(({{\mathrm{Im}}}DG(\bar{x}))^\perp \cap (T_{G(\bar{x})} \mathbb {S}^m_r )^\perp = \{0\}\). In view of \(({{\mathrm{Im}}}DG(\bar{x}))^\perp = {{\mathrm{Ker}}}DG(\bar{x})^*\) and \((T_{G(\bar{x})}\mathbb {S}^{m}_r)^\perp = {{\mathrm{Im}}}D\varphi (G(\bar{x}))^*\), where \(\varphi : G \mapsto C - B^\top A^{-1} B\) (cf. (11)), the transversality constraint qualification holds if and only if
On the other hand, \(DS(\bar{x})\) is regular if and only if
The latter is equivalent to (30). \(\square \)
Proof of Lemma 5 (b)
The rank of \(G(\bar{x})\) is equal to r. Therefore, \({{\mathrm{rank}}}G(\bar{x}) + {{\mathrm{rank}}}\Omega = m\) if and only if the rank of \(\Omega \) equals \(m-r\). Now, the assertion follows directly from the fact that \(\Omega \) is given by \(W \omega W^\top \). \(\square \)
Proof of Lemma 5 (c)
Since the kernel of \(S(\bar{x}) = 0 \in \mathbb {S}^{m-r}\) is the whole \(\mathbb {R}^{m-r}\), it holds:
Recall from (28) that for all \(h \in \mathbb {R}^n\) and x close to \(\bar{x}\):
Further, from (10) we have
where the columns of E form a basis of the kernel of \(DG(\bar{x})\). Note that the columns of \(W(\bar{x})\) form also a basis of the kernel of \(DG(\bar{x})\). Then, in view of (31), \(T^R (\bar{x})=T(\bar{x})\).
Now, we show that for all \(h \in T (\bar{x})\) it holds:
We calculate:
We proceed by calculating the term \(D^2 S(\bar{x}) [h,h]\) using the formula in (31):
A straightforward matrix multiplication shows that if \(S(x) = 0\) (for x close to \(\bar{x}\)), then also \(G(x)W(x) = 0\). Hence, \(D \left( G W \right) (\bar{x})[h]=0\) for any direction \(h \in T(\bar{x})\), i.e.,
Using this and \(G(\bar{x}) W(\bar{x})=0\), the formula for \(D^2 S(\bar{x})[h,h]\) reduces to
Using this, we have the following:
which by the properties of the trace goes to
and finally, using \(W(\bar{x}) \omega W(\bar{x})^\top = \Omega \), to the expression
By definition of the Moore-Penrose pseudo inverse, we have \(G(\bar{x}) G(\bar{x})^\dag G(\bar{x})=G(\bar{x})\). Thus, we obtain
Taking again into account (32), it follows that
Recalling the definition of \(H(\bar{x}, \Omega )\) in (9), we proved that for all \(h \in T (\bar{x})\):
\(\square \)
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Dorsch, D., Gómez, W. & Shikhman, V. Sufficient optimality conditions hold for almost all nonlinear semidefinite programs. Math. Program. 158, 77–97 (2016). https://doi.org/10.1007/s10107-015-0915-0
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DOI: https://doi.org/10.1007/s10107-015-0915-0