Abstract
We derive a maximum principle for optimal control problems with constraints given by the coupling of a system of ordinary differential equations and a partial differential equation of Vlasov type with smooth interaction kernel. Such problems arise naturally as Gamma-limits of optimal control problems constrained by ordinary differential equations, modeling, for instance, external interventions on crowd dynamics by means of leaders. We obtain these first-order optimality conditions in the form of Hamiltonian flows in the Wasserstein space of probability measures with forward–backward boundary conditions with respect to the first and second marginals, respectively. In particular, we recover the equations and their solutions by means of a constructive procedure, which can be seen as the mean-field limit of the Pontryagin Maximum Principle applied to the optimal control problem for the discretized density, under a suitable scaling of the adjoint variables.
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Notes
We follow the notation of [33].
For simplicity of computation, we consider minimization of 4 times the variance.
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Acknowledgements
The authors acknowledge the support of the PHC-PROCOPE Project “Sparse Control of Multiscale Models of Collective Motion.” Mattia Bongini and Massimo Fornasier additionally acknowledge the support of the ERC-Starting Grant Project “High-Dimensional Sparse Optimal Control.” Francesco Rossi additionally acknowledges the support of the ANR project CroCo ANR-16-CE33-0008.
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Communicated by Aram Arutyunov.
Appendix: Semiconvexity Along Geodesics of \(\mathbb {H}_c\)
Appendix: Semiconvexity Along Geodesics of \(\mathbb {H}_c\)
Throughout the section, \({\mathcal {K}}\) shall denote a convex, compact subset of \({\mathbb R}^{2d}\). The following property shall be used to prove that the subdifferential of \(\mathbb {H}_c\) is nonempty.
Definition A.1
Let \(\psi : {\mathcal {P}}_2({\mathbb R}^n) \rightarrow ]-\infty ,+\infty ]\) be a proper, lower semicontinuous functional. We say that \(\psi \) is semiconvex along geodesics whenever, for every \(\nu _0, \nu _1 \in {\mathcal {P}}_2({\mathbb R}^n)\) and every optimal transport plan \(\rho \in \varPi _o(\nu _0, \nu _1)\) there exists \(C \in {\mathbb R}\) for which for every \(s \in [0,1]\) it holds
In order to prove the semiconvexity of \(\mathbb {H}_c\), we shall establish the semiconvexity of the following functionals:
where \(\hat{{\mathcal {F}}}\), \(\hat{{\mathcal {G}}}\), \(\hat{\ell }\), and \(\hat{\omega }\) are \({\mathcal {C}}^2\) functions. The desired result will then follow by noticing that \(\mathbb {H}_c(\nu ) = \hat{\mathbb {H}}_c^1(\nu ) + \hat{\mathbb {H}}_c^2(\nu )\) for \(\hat{{\mathcal {F}}} = {\mathcal {F}}\), \(\hat{{\mathcal {G}}} = {\mathcal {G}}\), \(\hat{\ell } = -\ell \circ (\pi _1, Id )\), \(\hat{\omega } = \omega \circ \pi _1\) and \({\mathcal {K}} = cl \!\left( {B(0,R_T)}\right) \).
The following simple property will be needed to prove semiconvexity of the above functionals.
Lemma A.1
Let \(\nu _0,\nu _1 \in {\mathcal P}_c({\mathbb R}^{2d})\) with support contained in \({\mathcal {K}}\). Let \(\rho \in \varPi (\nu _0,\nu _1)\) and set
for every \(s \in [0,1]\). Then, it holds
Proof
We first notice that for every \(\rho \in \varPi (\nu _0,\nu _1)\) it holds
This follows from the equality
and from the fact that both \({\mathbb R}^{2d} \times ({\mathbb R}^{2d} \backslash {\mathcal {K}}))\) and \(({\mathbb R}^{2d} \backslash {\mathcal {K}}) \times {\mathbb R}^{2d}\) are \(\rho \)-null sets by hypothesis.
To prove Lemma A.1, it suffices to show that for all \(f \in {\mathcal {C}}({\mathbb R}^{2d})\) satisfying \(f \equiv 0\) on \({\mathcal {K}}\) it holds
Indeed,
since, by (43), \(\mathrm {supp}(\rho ) \subseteq {\mathcal {K}} \times {\mathcal {K}}\). The convexity of \({\mathcal {K}}\) implies \((1-s)z_0 + sz_1 \in {\mathcal {K}}\) for every \(s \in [0,1]\), which, together with the assumption \(f \equiv 0\) in \({\mathcal {K}}\), yield (44), as desired. \(\square \)
In what follows, we shall make use of the following, well-known result.
Remark A.1
Let \({\mathcal {K}}\) be a convex, compact subset of \({\mathbb R}^{2d}\) and let \(f \in {\mathcal {C}}^2({\mathbb R}^{2d};{\mathbb R})\). Then, there exists \(C_{{\mathcal {K}},f} \in {\mathbb R}\) depending only on \({\mathcal {K}}\) and f such that
for every \(x_0,x_1 \in {\mathbb R}^{2d}\) and \(s \in [0,1]\).
We now prove the semiconvexity of \(\hat{\mathbb {H}}^1_c\).
Lemma A.2
Let \(\nu _0,\nu _1 \in {\mathcal P}_c({\mathbb R}^{2d})\) and let \(\rho \in \varPi (\nu _0,\nu _1)\). Then, there exists \(C \in {\mathbb R}\) independent of \(\nu _0\) and \(\nu _1\) for which
holds for every \(s \in [0,1]\).
Proof
We may assume \(\mathrm {supp}(\nu _0),\mathrm {supp}(\nu _1) \subseteq {\mathcal {K}}\) for some convex and compact set \({\mathcal {K}} \subset {\mathbb R}^{2d}\), otherwise the inequality is trivial. Hence, from Lemma A.1, it follows \(\mathrm {supp}(\nu _s) \subseteq {\mathcal {K}}\) for every \(s \in [0,1]\). But then, since \(\hat{{\mathcal {F}}}\) and \(\hat{{\mathcal {G}}}\) are both \({\mathcal {C}}^2\), the result follows as in [33, Proposition 9.3.2, Proposition 9.3.5]. \(\square \)
Corollary A.1
Let \(\hat{\omega } \in {\mathcal {C}}^2({\mathbb R}^{2d};{\mathbb R}^{d})\), \(\nu _0,\nu _1 \in {\mathcal P}_c({\mathbb R}^{2d})\), \(\rho \in \varPi (\nu _0,\nu _1)\) and define \(\nu _s\) as in (42) for \(s \in [0,1]\). If we set
then
for all \(s\in [0,1]\), where C is independent of \(\nu _0\) and \(\nu _1\).
Proof
Follows from Lemma A.2 applied first to the functions \(\hat{{\mathcal {F}}} \equiv 0\) and \(\hat{{\mathcal {G}}} \equiv \hat{\omega }\), and then to \(\hat{{\mathcal {F}}} \equiv 0\) and \(\hat{{\mathcal {G}}} \equiv -\hat{\omega }\). \(\square \)
The semiconvexity of \(\hat{\mathbb {H}}^2_c\) will be deduced as a corollary of the following estimate.
Lemma A.3
Suppose that \(\hat{\ell } \in {\mathcal {C}}^2({\mathbb R}^{2d}\times {\mathbb R}^d;{\mathbb R})\), let \(z_0,z_1 \in {\mathcal {K}}\) and set \(z_s = (1-s)z_0 + sz_1\) for all \(s \in [0,1]\). Furthermore, let \(\nu _0,\nu _1 \in {\mathcal P}_c({\mathbb R}^{2d})\), \(\rho \in \varPi (\nu _0,\nu _1)\) and define \(\nu _s\) and \(\xi _s\) as in (42) and (46) for \(s \in [0,1]\). Then, for all \(s \in [0,1]\), it holds
for some constant \(C_{{\mathcal {K}},\hat{\ell },\hat{\omega }}\) depending only on \({\mathcal {K}},\hat{\ell }\) and \(\hat{\omega }\).
Proof
Since \({\mathcal {K}}\) is compact, \(z_s \in {\mathcal {K}}\) for all \(s \in [0,1]\). Moreover, \((1-s)\xi _0 + s\xi _1 \in {\mathcal {K}}'\) for all \(s \in [0,1]\), for some convex and compact set \({\mathcal {K}}' \subset {\mathbb R}^d\). Notice that from (45) follows
and from the definition of \(\xi _s\) and Jensen’s inequality, we get
Moreover, for every \(s\in [0,1]\) it holds
Hence, for every \(s\in [0,1]\), using (47), (48) and (49), we get
This concludes the proof. \(\square \)
Corollary A.2
Let \(\nu _0,\nu _1 \in {\mathcal P}_c({\mathbb R}^{2d})\) and \(\rho \in \varPi _o(\nu _0,\nu _1)\). Then, there exists \(C \in {\mathbb R}\) independent of \(\nu _0\) and \(\nu _1\) for which
holds for every \(s \in [0,1]\).
Proof
Notice that, by Lemma A.1, \(\hat{H}^2_c(\nu _s)\) can be rewritten as
Furthermore, since \(\rho \in \varPi _o(\nu _0,\nu _1)\) it holds
the thesis follows from Lemma A.3. \(\square \)
Proposition A.1
The functional \(\mathbb {H}_c\) is semiconvex along geodesics.
Proof
Follows directly from Lemma A.2 and Corollary A.2, by noticing that \(\mathbb {H}_c(\nu ) = \hat{\mathbb {H}}_c^1(\nu ) + \hat{\mathbb {H}}_c^2(\nu )\) for \(\hat{{\mathcal {F}}} = {\mathcal {F}}\), \(\hat{{\mathcal {G}}} = {\mathcal {G}}\), \(\hat{\ell } = -\ell \circ (\pi _1, Id )\), \(\hat{\omega } = \omega \circ \pi _1\) and \({\mathcal {K}} = cl \!\left( {B(0,R_T)}\right) \). \(\square \)
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Bongini, M., Fornasier, M., Rossi, F. et al. Mean-Field Pontryagin Maximum Principle. J Optim Theory Appl 175, 1–38 (2017). https://doi.org/10.1007/s10957-017-1149-5
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DOI: https://doi.org/10.1007/s10957-017-1149-5
Keywords
- Sparse optimal control
- Mean-field limit
- \(\varGamma \)-limit
- Optimal control with ODE–PDE constraints
- Subdifferential calculus
- Hamiltonian flows