Abstract
The recent advance of algorithms for nonlinear semidefinite optimization problems (NSDPs) is remarkable. Yamashita et al. first proposed a primal–dual interior point method (PDIPM) for solving NSDPs using the family of Monteiro–Zhang (MZ) search directions. Since then, various kinds of PDIPMs have been proposed for NSDPs, but, as far as we know, all of them are based on the MZ family. In this paper, we present a PDIPM equipped with the family of Monteiro–Tsuchiya (MT) directions, which were originally devised for solving linear semidefinite optimization problems as were the MZ family. We further prove local superlinear convergence to a Karush–Kuhn–Tucker point of the NSDP in the presence of certain general assumptions on scaling matrices, which are used in producing the MT search directions. Finally, we conduct numerical experiments to compare the efficiency among members of the MT family.
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Data Availability Statement
All data in the paper are available from the corresponding author on reasonable request. There is no conflict of interest in writing the paper.
Notes
We have the identity \(W=Y^{-\frac{1}{2}}(Y^{\frac{1}{2}}G(x)Y^{\frac{1}{2}})^{\frac{1}{2}}Y^{-\frac{1}{2}}\).
Since \(\mathcal {J}\Xi ^P_{\mu _k}(v^l)\varDelta v=-\Xi ^P_{\mu _k}(v^l)\) is assumed to be nonsingular, there exists a nonnegative integer \(\bar{\ell }\) such that \(\varPsi _{\mu _k}^P(v^l+\beta _2(\bar{\ell })\varDelta v)\le \left( 1 - 0.25 \cdot \beta _2(\bar{\ell })\right) \varPsi _{\mu _k}^P(v^l)\), which is rewritten as (68) because of \(\varPsi _{\mu _k}^P(w)=\varPsi _{\mu _k}^I(w)\).
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Acknowledgements
The author thanks Professor Yoshiko Ikebe, Professor Mirai Tanaka, and Professor Makoto Yamashita for numerous comments and suggestions. He is also sincerely grateful for the anonymous reviewers for many crucial suggestions.
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This research was supported in part by Grant-in-Aid for Young Scientists 20K19748 and Grant-in-Aid for Scientific Research (B)20H04145 from JSPS KAKENHI.
Omitted Proofs
Omitted Proofs
1.1 Proof of Proposition
Before the proof of Proposition , we first give two propositions. Choose arbitrary sequences \(\{\tilde{w}^{\ell }\}\) and \(\{\tilde{\mu }^{\ell }\}\) satisfying (22) in Condition (P2). To show the proposition, we prepare the following two claims.
Since the semidefinite complementarity condition that \(G(x^{*})\bullet Y_{*}=0\), \(G(x^{*})\in \mathbb {S}^m_+\), and \(Y_{*}\in \mathbb {S}^m_+\) holds, the matrices \(G(x^{*})\) and \(Y_{*}\) can be simultaneously diagonalized, namely, there exists an orthogonal matrix \(Q_{*}\in \mathbb {R}^{m\times m}\) such that
where \(\varLambda _1\in \mathbb {R}^{r_{*}\times r_{*}}\) with \(r_{*}:=\textrm{rank}\,G(x^{*})\) is a positive diagonal matrix and \(\varLambda _2\in \mathbb {R}^{(m-r_{*})\times (m-r_{*})}\) is a nonnegative diagonal matrix. The diagonal entries of \(\varLambda _1\) and \(\varLambda _2\) are the eigenvalues of \(G(x^{*})\) and \(Y_{*}\), respectively. The following proposition is obtained from [47, Lemma 3] under the assumption that \(\tilde{w}^{\ell }\in \mathcal {N}_{\tilde{\mu }_{\ell }}^{r_{\ell }}\) with \(r_{\ell }=\textrm{o}(\tilde{\mu }_{\ell })\).
Proposition A1
It holds that
where both the matrices are partitioned into the four blocks with the same sizes as those in (A.1). The above expressions indicate upper-bounds of the magnitude of the block matrices. For example, \(\Vert \text{ the } (1,1)\text{-block } \text{ of } G_{\ell }\Vert _{\text {F}}=\mathrm {\varTheta }(1)\). Moreover, the sequences of the inverse matrices satisfy
The next one will be used to prove Case (i) of Proposition .
Proposition A2
Let \(U:=\mathcal {L}_{X^{\frac{1}{2}}}^{-1}(\varDelta X)\) for \(X\in \mathbb {S}^m_{++}\) and \(\varDelta X\in \mathbb {S}^m\). Then,
Proof
Since the first equality is obvious, we show the inequalities part. From \(U=\mathcal {L}_{X^{\frac{1}{2}}}^{-1}(\varDelta X)\) it follows that \(UX^{\frac{1}{2}}+X^{\frac{1}{2}}U=\varDelta X\), which implies
Then, the first desired inequality follows from [27, Lemma 2.1]. The second one is obtained from
where the second inequality follows from \(\Vert X^{-\frac{1}{2}}\Vert _\textrm{F}^2=\textrm{Tr}(X^{-1})\le \Vert I\Vert _{\textrm{F}}\Vert X^{-1}\Vert _\textrm{F}=\sqrt{m}\Vert X^{-1}\Vert _{\textrm{F}}\). Hence, the proof is complete. \(\square \)
Let us start proving Proposition . For each \(\ell \), let
For other notations such as \(\widehat{\mathcal {G}}_i\), see Condition (\(\textbf{P2}\)). Note that for \(w\in \mathcal {W}\) and \(\mu >0\),
where the equality is easily verified by comparing the squares of both the sides and the inequality follows from Proposition with \(X=G(x)\). Combining the above relation with \(\Vert G_{\ell }\widetilde{Y}_{\ell }-\tilde{\mu }_{\ell }I\Vert _{\textrm{F}}=\textrm{O}(\tilde{\mu }_{\ell }^{1+\xi })\), we have
We often use the following equations:
where the equations in (A.4) are derived from \(\lim _{\ell \rightarrow \infty }(\tilde{x}^{\ell },\widetilde{Y}_{\ell })=(x^{*},Y_{*})\) and the continuity of \(\mathcal {G}_i\) and G, and those in (A.5) follow from Proposition .
Now, we proceed to the proof of Cases (i)–(v). Fix \(i\in \{1,2,\ldots ,n\}\) arbitrarily and write \(\mathcal {U}_{\ell }^i:=\mathcal {L}^{-1}_{\widehat{G}_{\ell }^{\frac{1}{2}}}(\widehat{\mathcal {G}}_i(\tilde{x}^{\ell })).\) We first show Case (i) with \(\widetilde{P}_{\ell }=I\) for any \(\ell \). Note \(Z_{\ell }^i={\tilde{\mu }_{\ell }}\mathcal {U}_{\ell }^iG_{\ell }^{-\frac{1}{2}}\) with \(\mathcal {U}_{\ell }^i=\mathcal {L}^{-1}_{G_{\ell }^{\frac{1}{2}}}(\mathcal {G}_i(\tilde{x}^{\ell }))\) in this case. We then have \(\tilde{\mu }_{\ell }\mathcal {U}_{\ell }^iG_{\ell }^{\frac{1}{2}}+\tilde{\mu }_{\ell }G_{\ell }^{\frac{1}{2}}\mathcal {U}_{\ell }^i=\tilde{\mu }_{\ell }\mathcal {G}_i(\tilde{x}^{\ell })\), which together with Proposition with \((X,\varDelta X)=(G_{\ell },\mathcal {G}_i(\tilde{x}^{\ell }))\) implies
where we used (A.4) and (A.5). Hence, \(\{Z_{\ell }^i\}\) is bounded. We next show Case (ii) with \(\widetilde{P}_{\ell }=G_{\ell }^{-\frac{1}{2}}\). By \(\widehat{G}_{\ell }=I\) for each \(\ell \), we have \(\mathcal {U}_{\ell }^i=\widehat{\mathcal {G}}_i(\tilde{x}^{\ell })/2\), which together with (A.4) and (A.5) implies
Thus, \(\{Z_{\ell }^i\}\) is bounded for Case (ii).
In what follows, we show the remaining cases in a unified manner. As will be shown later, in each of Cases (iii)–(v), there exists some \(\rho _{*}>0\) such that
Let \(S_{\ell }^{i}:=\frac{\tilde{\mu }_{\ell }^{-\frac{\rho _{*}}{2}}}{2}\widehat{\mathcal {G}}_i(\tilde{x}^{\ell })\) for each \(\ell \). The expression \(\tilde{\mu }_{\ell }\left\| \widetilde{P}_{\ell }^{-1}S_{\ell }^{i}\widehat{G}_{\ell }^{-\frac{1}{2}}\widetilde{P}_{\ell }\right\| _{\textrm{F}}\) is evaluated as
where the first equality follows from \(\widehat{\mathcal {G}_i}(\tilde{x}^{\ell })=\widetilde{P}_{\ell }\mathcal {G}_i(\tilde{x}^{\ell })\widetilde{P}_{\ell }^{\top }\) and the last one from \(\Vert \widehat{G}_{\ell }^{-\frac{1}{2}}\Vert _{\textrm{F}}=\textrm{O}(\tilde{\mu }_{\ell }^{-\frac{\rho _{*}}{2}})\) by (A.6) and \(\Vert \mathcal {G}_i(\tilde{x}^{\ell })\Vert _{\textrm{F}}=\textrm{O}(1)\) as in (A.4). Furthermore, let
be an eigen-decomposition of \(\widehat{G}_{\ell }\) with an appropriate orthogonal matrix \(Q_{\ell }\in \mathbb {R}^{m\times m}\) and a diagonal matrix \(D_{\ell }\in \mathbb {R}^{m\times m}\) with the eigenvalues of \(\widehat{G}_{\ell }\) aligned on the diagonal. Notice that \(\widehat{G}_{\ell }^{\frac{1}{2}}=Q_{\ell }D_{\ell }^{\frac{1}{2}}Q_{\ell }^{\top }\). Denote \(d_{p,\ell }:=(D_{\ell })_{pp}\in \mathbb {R}\) for each \(p=1,2,\ldots ,m\) and \(\mathcal {U}_{Q_{\ell }}^i:=Q_{\ell }^{\top }\mathcal {U}_{\ell }^iQ_{\ell }\). By multiplying \(Q_{\ell }^{\top }\) and \(Q_{\ell }\) on both sides of \(\mathcal {U}_{\ell }^i\widehat{G}_{\ell }^{\frac{1}{2}}+\widehat{G}_{\ell }^{\frac{1}{2}}\mathcal {U}_{\ell }^i=\widehat{\mathcal {G}}_i(\tilde{x}^{\ell })\), it follows from (A.8) that \(\mathcal {U}_{Q_{\ell }}^iD_{\ell }^{\frac{1}{2}}+D_{\ell }^{\frac{1}{2}}\mathcal {U}_{Q_{\ell }}^i=Q_{\ell }^{\top }\widehat{\mathcal {G}}_i(\tilde{x}^{\ell })Q_{\ell }\), which together with \(d_{p,\ell }=(D_{\ell })_{pp}\) for each \(p,\ell \) yields
for each \(p,q=1,2,\ldots ,m\). From (A.6), for each \(p=1,2,\ldots ,m\), there exists some \(\{\delta _{p,\ell }\}\subseteq \mathbb {R}\) satisfying \(\delta _{p,\ell }=\textrm{O}(\tilde{\mu }_{\ell }^{1+\xi })\) and \( d_{p,\ell }=(\tilde{\mu }_{\ell }+\delta _{p,\ell })^{\rho _{*}}. \) By taking the fact of \(\tilde{\mu }_{\ell }>0\) into account, for each p, the mean-value theorem implies that for some \(\bar{s}_{p,\ell }\in [0,1]\)
Notice that \(\tilde{\mu }_{\ell }+\bar{s}_{p,\ell }\delta _{p,\ell }=\mathrm{\varTheta }(\tilde{\mu }_{\ell })\) and \(d_{p,\ell } =\mathrm{\varTheta }(\tilde{\mu }_{\ell }^{\rho _{*}})\) for all p. Then, for each \(p,q=1,2,\ldots ,m\), (A.9) yields
where the last equality follows from \(\delta _{p,\ell }=\textrm{O}(\tilde{\mu }_{\ell }^{1+\xi })\) and \(\delta _{q,\ell }=\textrm{O}(\tilde{\mu }_{\ell }^{1+\xi })\). From this fact together with \(\Vert Q_{\ell }^{\top }\widehat{\mathcal {G}}_i(\tilde{x}^{\ell })Q_{\ell }\Vert _{\textrm{F}}= \Vert Q_{\ell }^{\top }\widetilde{P}_{\ell }\mathcal {G}_i(\tilde{x}^{\ell })\widetilde{P}_{\ell }^{\top }Q_{\ell }\Vert _{\textrm{F}}= \textrm{O}(\Vert \widetilde{P}_{\ell }\Vert _\textrm{F}^2)\), we obtain, for each p, q,
which together with \(\Vert G_{\ell }^{-\frac{1}{2}}\Vert _\textrm{F}=\textrm{O}(\tilde{\mu }_{\ell }^{-\frac{\rho _{*}}{2}})\) from (A.6) implies
where we used the fact that \(\Vert Q_{\ell }\Vert _{\textrm{F}}=\sqrt{m}\) because \(Q_{\ell }\) is an orthogonal matrix. Hence, by recalling \(Z_{\ell }^i=\tilde{\mu }_{\ell }\widetilde{P}_{\ell }^{-1}\mathcal {U}_{\ell }^i\widehat{G}_{\ell }^{-\frac{1}{2}}\widetilde{P}_{\ell }\) and using (A.7) we obtain
Hereafter, for each of Cases (iii)–(v), we evaluate \(\Vert \widetilde{P}_{\ell }\Vert _\textrm{F}\), \(\Vert \widetilde{P}_{\ell }^{-1}\Vert _{\textrm{F}}\), and \(\rho _{*}\), and prove the boundedness of \(\{Z_{\ell }^i\}\) by showing that the rightmost hand expression in (A.11) is \(\textrm{O}(1)\).
Case (iii): Since \(\widehat{Y}_{\ell }=I\), \(\widetilde{P}_{\ell }=\widetilde{Y}_{\ell }^{\frac{1}{2}}\) for each \(\ell \), (A.3) implies \(\Vert \widehat{G}_{\ell }-\tilde{\mu }_{\ell }I\Vert _{\textrm{F}}=\Vert \widehat{G}_{\ell }^{\frac{1}{2}}\widehat{Y}_{\ell }\widehat{G}_{\ell }^{\frac{1}{2}}-\tilde{\mu }_{\ell }I\Vert _{\textrm{F}}=\textrm{O}(\tilde{\mu }_{\ell }^{1+\xi })\), which indicates \(\rho _{*}=1\) (see (A.6)). Furthermore, \(\Vert \widetilde{P}_{\ell }\Vert _{\textrm{F}}=\Vert \widetilde{Y}_{\ell }^{\frac{1}{2}}\Vert _{\textrm{F}}=\textrm{O}(1)\) and \(\Vert \widetilde{P}_{\ell }^{-1}\Vert _{\textrm{F}}=\Vert \widetilde{Y}_{\ell }^{-\frac{1}{2}}\Vert _{\textrm{F}}=\textrm{O}(\tilde{\mu }_{\ell }^{-\frac{1}{2}})\) by (A.5). Combined with (A.11) and the assumption \(\xi \ge \frac{1}{2}\), these results yield \(\Vert Z_{\ell }^i\Vert _\textrm{F}=\displaystyle {\textrm{O}(1+\tilde{\mu }_{\ell }^{\xi -\frac{1}{2}})}=\textrm{O}(1)\).
Case (iv): Since \(\widetilde{P}_{\ell }=(\widetilde{Y}_{\ell }G_{\ell }\widetilde{Y}_{\ell })^{\frac{1}{2}}\) and \(\widehat{G}_{\ell }^{-\frac{1}{2}}=\widehat{Y}_{\ell }\), we have, from (A.3), \( \Vert \widehat{G}_{\ell }^{\frac{1}{2}}-\tilde{\mu }_{\ell }I\Vert _\textrm{F}=\textrm{O}(\tilde{\mu }_{\ell }^{1+\xi }) \) yielding \(\rho _{*}=2\). Moreover, by (A.4) and \(\Vert G_{\ell }\widetilde{Y}_{\ell }-\tilde{\mu }_{\ell }I\Vert _\textrm{F}=\textrm{O}(\tilde{\mu }_{\ell }^{1+\xi })\),
where \(K_{\ell }:=G_{\ell }^{-\frac{1}{2}}\widetilde{Y}_{\ell }^{-1}G_{\ell }^{-\frac{1}{2}}\) and we used \(\Vert K_{\ell }\Vert _\textrm{F}=\textrm{O}(\tilde{\mu }_{\ell }^{-1})\) from (A.3) to derive the last equality. These results combined with (A.11), \(\rho _{*}=2\), and \(\xi \ge \frac{1}{2}\) yield \(\Vert Z_{\ell }^i\Vert _{\textrm{F}} =\displaystyle {\textrm{O}(1+\tilde{\mu }_{\ell }^{\xi -\frac{1}{2}})}=\textrm{O}(1).\)
Case (v): By \(\widehat{G}_{\ell }=\widehat{Y}_{\ell }\) and (A.3), we have \(\Vert \widehat{G}_{\ell }^2-\tilde{\mu }_{\ell }I\Vert _\textrm{F}=\textrm{O}(\tilde{\mu }_{\ell }^{1+\xi })\) yielding \(\rho _{*}=\frac{1}{2}\). Recall that the MTW scaling matrix \(W_{\ell }\) is defined by \( W_{\ell }:=G_{\ell }^{\frac{1}{2}}\left( G_{\ell }^{\frac{1}{2}}\widetilde{Y}_{\ell }G_{\ell }^{\frac{1}{2}}\right) ^{-\frac{1}{2}}G_{\ell }^{\frac{1}{2}} \) for each \(\ell \). Note that \(\Vert G_{\ell }\Vert _{\textrm{F}}=\textrm{O}(1)\) and \(\Vert G_{\ell }^{\frac{1}{2}}\widetilde{Y}_{\ell }G_{\ell }^{\frac{1}{2}}\Vert _{\textrm{F}}=\mathrm{\varTheta }(\tilde{\mu }_{\ell })\) follow from (A.4) and (A.3), respectively. The first equality in (A.2) then implies
which entails \(\Vert W_{\ell }\Vert _{\textrm{F}}=\textrm{O}(\tilde{\mu }_{\ell }^{-\frac{1}{2}})\). Using \(\Vert G_{\ell }^{\frac{1}{2}}\widetilde{Y}_{\ell }G_{\ell }^{\frac{1}{2}}\Vert _{\textrm{F}}=\mathrm{\varTheta }(\tilde{\mu }_{\ell })\) again and \(\Vert G_{\ell }^{-1}\Vert _{\textrm{F}}=\textrm{O}(\tilde{\mu }_{\ell }^{-1})\) from (A.5), we have \(\Vert W_{\ell }^{-1}\Vert _{\textrm{F}} = \Vert G_{\ell }^{-\frac{1}{2}}\left( G_{\ell }^{\frac{1}{2}}\widetilde{Y}_{\ell }G_{\ell }^{\frac{1}{2}}\right) ^{\frac{1}{2}}G_{\ell }^{-\frac{1}{2}}\Vert _\textrm{F} =\textrm{O}(\tilde{\mu }_{\ell }^{-\frac{1}{2}}).\) Hence, we obtain that \( \Vert \widetilde{P}_{\ell }\Vert _\textrm{F}^2=\textrm{Tr}(W_{\ell }^{-1})=\textrm{O}(\tilde{\mu }_{\ell }^{-\frac{1}{2}}) \) and \( \Vert \widetilde{P}_{\ell }^{-1}\Vert _\textrm{F}^2=\textrm{Tr}(W_{\ell })=\textrm{O}(\tilde{\mu }_{\ell }^{-\frac{1}{2}})\). Therefore, \( \Vert \widetilde{P}_{\ell }\Vert _\textrm{F}=\textrm{O}(\tilde{\mu }_{\ell }^{-\frac{1}{4}}),\ \Vert \widetilde{P}_{\ell }^{-1}\Vert _\textrm{F}=\textrm{O}(\tilde{\mu }_{\ell }^{-\frac{1}{4}}).\) These results combined with (A.11), \(\rho _{*}=\frac{1}{2}\), and \(\xi \ge \frac{1}{2}\) yield \(\Vert Z_{\ell }^i\Vert _\textrm{F}=\textrm{O}(1+\tilde{\mu }_{\ell }^{\xi -\frac{1}{2}})=\textrm{O}(1)\). We complete the proof. \(\square \)
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Okuno, T. Local convergence of primal–dual interior point methods for nonlinear semidefinite optimization using the Monteiro–Tsuchiya family of search directions. Comput Optim Appl (2024). https://doi.org/10.1007/s10589-024-00562-y
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DOI: https://doi.org/10.1007/s10589-024-00562-y