TheoremBase

Quadratic Growth of the Mean-Field Hamiltonian in the Control along a Stationary Mean-Field Triple

Statement

Let ll, mm, A\mathcal{A}, β\beta with rate bound BB, (U,V,βˉ)(U,V,\bar{\beta}) with derivative bound KK, bˉ\bar{b}, (L,G)(L,G), (Uc,Lˉ,Gˉ)(U_c,\bar{L},\bar{G}) with second-derivative bound KcK_c, T>0T>0, and (S,A)(S,A) be as in the definition of a stationary mean-field triple (the open set of the cost extension, written WW in that definition, is written UcU_c here, so that Lˉ\bar{L} is defined on Uc×RmU_c\times\mathbb{R}^m and Gˉ\bar{G} on UcU_c), and let PP be a stationary co-state for these data. Adopt the coordinate and partial-derivative notation ∂i\partial_i, ∂j∂i\partial_j\partial_i of the extension definitions: the partial derivatives of orders one and two of each bˉδ\bar{b}^\delta exist and are continuous on U×VU\times V by part (i) of the regularity of the extended aggregate state drift, and those of Lˉ\bar{L} on Uc×RmU_c\times\mathbb{R}^m by clause 2 of the cost extension definition together with clauses 1 and 2 of the CkC^k definition. Let Hij(t)H_{ij}(t), for t∈[0,T]t\in[0,T] and i,j∈{1,…,l+m}i,j\in\{1,\dots,l+m\}, be the fluctuation Hessian coefficients of these data. Write R\mathbb{R} for the real numbers, dd for the Euclidean distance, ∣⋅∣|\cdot| for the Euclidean norm (Euclidean distance to the origin), and Δl\Delta^l for the probability simplex. Throughout, a real-valued function on a subinterval II of the real numbers R\mathbb{R} is called continuous on II when it is continuous relative to II, both II and the codomain R\mathbb{R} carrying the metric of the real line. Fix a real number CPC_P with ∑δ=1l∣Ptδ∣≤CP\sum_{\delta=1}^{l}|P^\delta_t|\le C_P for every t∈[0,T]t\in[0,T]; such a number exists because each component of PP is continuous on [0,T][0,T] by clause 1 of the co-state definition and therefore attains a maximum and a minimum on [0,T][0,T] by the extreme value theorem.

Define the mean-field Hamiltonian along the triple as the function on [0,T]×V[0,T]\times V given by

Ht(a)=Lˉ(St,a)−∑δ=1lPtδ bˉδ(St,a)(t∈[0,T], a∈V),\mathcal{H}_t(a)=\bar{L}(S_t,a)-\sum_{\delta=1}^{l}P^\delta_t\,\bar{b}^\delta(S_t,a)\qquad(t\in[0,T],\ a\in V),

which is well defined because St∈Δl⊂U∩UcS_t\in\Delta^l\subset U\cap U_c and V⊆RmV\subseteq\mathbb{R}^m; the script letter H\mathcal{H} is distinct from the Hessian coefficients HijH_{ij}. Define, for t∈[0,T]t\in[0,T], the real m×mm\times m matrix RtR_t with entries

Rtij=14(Hl+i,l+j(t)+Hl+j,l+i(t))(i,j∈{1,…,m}),R^{ij}_t=\tfrac{1}{4}\big(H_{l+i,l+j}(t)+H_{l+j,l+i}(t)\big)\qquad(i,j\in\{1,\dots,m\}),

and write a⋅Rta=∑i,j=1mRtijaiaja\cdot R_ta=\sum_{i,j=1}^{m}R^{ij}_ta^ia^j for a∈Rma\in\mathbb{R}^m; the displayed formula agrees with the definition of RtR_t in the completion-of-squares theorem, and only that formula and the wording of its hypothesis (H1) are borrowed from that theorem, not its setting.

Hypotheses. (A) The control set A\mathcal{A} is convex and compact for the topology determined by the Euclidean distance. (H1) There is a real number r>0r>0 with a⋅Rta≥r∣a∣2a\cdot R_ta\ge r|a|^2 for every t∈[0,T]t\in[0,T] and every a∈Rma\in\mathbb{R}^m (hypothesis (H1) of the completion-of-squares theorem). (U) For every t∈[0,T]t\in[0,T], the point AtA_t is the unique minimizer of Ht\mathcal{H}_t on A\mathcal{A}: Ht(a)>Ht(At)\mathcal{H}_t(a)>\mathcal{H}_t(A_t) for every a∈Aa\in\mathcal{A} with a≠Ata\neq A_t.

Then:

(a) (Continuity.) Regarding [0,T]×V[0,T]\times V as a subset of Euclidean space R1+m\mathbb{R}^{1+m}, the map (t,a)↦Ht(a)(t,a)\mapsto\mathcal{H}_t(a) is continuous at every point of [0,T]×V[0,T]\times V, and so is the map (t,a)↦Ht(At)(t,a)\mapsto\mathcal{H}_t(A_t); this uses none of the hypotheses (A), (H1), (U).

(b) (Stationarity and the Hessian in the control.) For every t∈[0,T]t\in[0,T] and j∈{1,…,m}j\in\{1,\dots,m\},

∂l+jLˉ(St,At)−∑δ=1lPtδ ∂l+jbˉδ(St,At)=0,\partial_{l+j}\bar{L}(S_t,A_t)-\sum_{\delta=1}^{l}P^\delta_t\,\partial_{l+j}\bar{b}^\delta(S_t,A_t)=0,

and for every h∈Rmh\in\mathbb{R}^m,

12∑i=1m∑j=1mHl+i,l+j(t) hihj=h⋅Rth.\tfrac{1}{2}\sum_{i=1}^{m}\sum_{j=1}^{m}H_{l+i,l+j}(t)\,h^ih^j=h\cdot R_th .

This uses none of the hypotheses (A), (H1), (U).

(c) (Upper quadratic bound.) Assume that A\mathcal{A} is convex (only the convexity half of (A) is used). Put M2=Kc+3 l K CPM_2=K_c+3\,l\,K\,C_P and CH=12(l+m)M2C_{\mathcal{H}}=\tfrac{1}{2}(l+m)M_2. Then for every t∈[0,T]t\in[0,T] and every a∈Aa\in\mathcal{A},

∣Ht(a)−Ht(At)∣≤CH ∣a−At∣2.\big|\mathcal{H}_t(a)-\mathcal{H}_t(A_t)\big|\le C_{\mathcal{H}}\,|a-A_t|^2 .

(d) (Quadratic growth.) Assume (A), (H1), and (U). Then there is a real number r0>0r_0>0 such that

Ht(a)−Ht(At) ≥ r0 ∣a−At∣2for every t∈[0,T] and every a∈A.\mathcal{H}_t(a)-\mathcal{H}_t(A_t)\ \ge\ r_0\,|a-A_t|^2\qquad\text{for every }t\in[0,T]\text{ and every }a\in\mathcal{A}.

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