TheoremBase

Pathwise Estimand Linearisation of an Open-Loop Aggregate Solution Along a Comparison Pair: Cell-Count Coefficients from the Fundamental Solution and the Three Error Terms

Statement

Adopt the setting and notation of Linearisation of a Perturbed Controlled Aggregate Flow Along a Comparison Pair: Exact Variation-of-Constants Identity and Residual Bound: natural numbers l≥2l\ge2, m≥1m\ge1, the control set A⊆Rm\mathcal{A}\subseteq\mathbb{R}^m, real numbers B≥0B\ge0 and T>0T>0, the transition-rate family β\beta with its twice continuously differentiable extension of derivative bound KK, the labels c=(σ,γ)∈Lc=(\sigma,\gamma)\in\mathcal{L} with vectors vcv_c, the label rates ψc\psi_c, state gradients gcg^{c}, drift Jacobian E\mathcal{E}, drift bb, the constants Λ2\Lambda_2, Λ3\Lambda_3, ΛE\Lambda_{\mathcal{E}}, the notions measurable and bounded measurable for maps on [0,T][0,T], the comparison pair (S,A)(S,\mathsf{A}) with Eu⋆=E(Su,Au)\mathcal{E}^{\star}_u=\mathcal{E}(S_u,\mathsf{A}_u) continuous in uu, the two-parameter fundamental solution ΦE(t,u)\Phi^{\mathcal{E}}(t,u) and its bound Φˉ\bar{\Phi}, and, for a control path aa and paths x,yx,y, the objects ee, ρ\rho, d\mathsf{d} and Lt(⋅)L_t(\cdot) defined there. Adopt also from Open-Loop Aggregate Solution Driven by Aggregate Transition Clocks a natural number N≥1N\ge1, the aggregate lattice GN⊆Δl\mathbb{G}_N\subseteq\Delta^l, clock families, control paths, and open-loop aggregate solutions with their consumed clock times Cc\mathsf{C}^{c}; and from Cell-Count Form of a Compensated Counting-Path Functional Along a Time Change: Window-Discrepancy Bounds for the Time-Change and Partial-Cell Errors, for a counting path pp and a real number R>0R>0, the compensated path Mˉ(x)=p(x)−x\bar{M}(x)=p(x)-x, the window discrepancy Discw(p)\mathrm{Disc}_w(p) (formed with this RR), the compensated functional Λ(⋅)\Lambda(\cdot), and, for a bounded measurable H:[0,T]→RlH:[0,T]\to\mathbb{R}^l, the norm ∥H∥1=∫[0,T]∣Hu∣ du\lVert H\rVert_1=\int_{[0,T]}|H_u|\,du. Write ∣⋅∣|\cdot| for the Euclidean norm, x⋅yx\cdot y for the dot product, and N\sqrt{N} for the nonnegative square root.

Cells and clocks. Let R>0R>0 be a real number with R≥NBTR\ge NBT; for every label cc let Jc≥1J_c\ge1 be a natural number and 0=b0c<b1c<⋯<bJcc=R0=\mathsf{b}^{c}_0<\mathsf{b}^{c}_1<\dots<\mathsf{b}^{c}_{J_c}=R real numbers, with cell lengths μc,j=bjc−bj−1c\mu_{c,j}=\mathsf{b}^{c}_j-\mathsf{b}^{c}_{j-1}; let L\mathsf{L} be the set of pairs q=(c,j)q=(c,j) with 1≤j≤Jc1\le j\le J_c, write μq=μc,j\mu_q=\mu_{c,j} and μmax⁡=max⁡q∈Lμq\mu_{\max}=\max_{q\in\mathsf{L}}\mu_q (the cells of Injection Weights from a Bounded Profile Along Mean-Field Clocks: Label-Rate Form of the Fluctuation Covariance, Weight Bounds, the Prior Quadratic Form, and the Step-Function Injection). Let p=(pc)c∈Lp=(p^{c})_{c\in\mathcal{L}} be a clock family, aa a control path, x0∈GNx_0\in\mathbb{G}_N, and let Σ:[0,T]→Rl\Sigma:[0,T]\to\mathbb{R}^l be an open-loop aggregate solution for the data (p,a,x0)(p,a,x_0), with consumed clock times Cc\mathsf{C}^{c}; define the cell counts Kc,j=pc(bjc)−pc(bj−1c)\mathsf{K}_{c,j}=p^{c}(\mathsf{b}^{c}_j)-p^{c}(\mathsf{b}^{c}_{j-1}) and write Kq=Kc,j\mathsf{K}_q=\mathsf{K}_{c,j}. Define the mean-field label rates and mean-field clocks

ϕc(t)=ψc(St,At),Cˉtc=N∫[0,t]ϕc(u) du(c∈L, t∈[0,T]),\phi_c(t)=\psi_c(S_t,\mathsf{A}_t),\qquad \bar{\mathsf{C}}^{c}_t=N\int_{[0,t]}\phi_c(u)\,du\qquad(c\in\mathcal{L},\ t\in[0,T]),

as in Injection Weights from a Bounded Profile Along Mean-Field Clocks: Label-Rate Form of the Fluctuation Covariance, Weight Bounds, the Prior Quadratic Form, and the Step-Function Injection, whose cell boundaries bjcb^{c}_j are written bjc\mathsf{b}^{c}_j here (each ϕc\phi_c is measurable with values in [0,B][0,B] by claim 1). Let y:[0,T]→Δly:[0,T]\to\Delta^l be a measurable map with yt=y0+∫[0,t]b(yu,au) duy_t=y_0+\int_{[0,t]}b(y_u,a_u)\,du for every tt (the flow), and let c∈Rl\mathbf{c}\in\mathbb{R}^l (the estimand direction; the bold letter is unrelated to the labels cc). Put et=Σt−yte_t=\Sigma_t-y_t, and let ρu\rho_u and du\mathsf{d}_u be the residual and the control-gradient discrepancy of Linearisation of a Perturbed Controlled Aggregate Flow Along a Comparison Pair: Exact Variation-of-Constants Identity and Residual Bound for x=Σx=\Sigma and this yy. Assume the clock-discrepancy hypothesis: a real number w1≥0w_1\ge0 with

(CD)∣Cuc−Cˉuc∣≤w1for every c∈L and u∈[0,T].\textbf{(CD)}\qquad|\mathsf{C}^{c}_u-\bar{\mathsf{C}}^{c}_u|\le w_1\qquad\text{for every }c\in\mathcal{L}\text{ and }u\in[0,T].

For c∈Lc\in\mathcal{L} and u∈[0,T]u\in[0,T] put Huc=ΦE(T,u) Eu⋆ vc∈RlH^{c}_u=\Phi^{\mathcal{E}}(T,u)\,\mathcal{E}^{\star}_u\,v_c\in\mathbb{R}^l. Finally define the entry times and the cell coefficients: for q=(c,j)∈Lq=(c,j)\in\mathsf{L} with CˉTc≥bjc\bar{\mathsf{C}}^{c}_T\ge \mathsf{b}^{c}_j let τˉq\bar{\tau}_q be the least u∈[0,T]u\in[0,T] with Cˉuc≥bjc\bar{\mathsf{C}}^{c}_u\ge \mathsf{b}^{c}_j (it exists by claim 1) and put αq=c⋅(ΦE(T,τˉq) vc)\alpha_q=\mathbf{c}\cdot\bigl(\Phi^{\mathcal{E}}(T,\bar{\tau}_q)\,v_c\bigr); for q=(c,j)q=(c,j) with CˉTc<bjc\bar{\mathsf{C}}^{c}_T<\mathsf{b}^{c}_j put αq=0\alpha_q=0.

1. (Perturbation form of the solution.) Each ϕc\phi_c is measurable with values in [0,B][0,B]; Σ\Sigma is bounded measurable with values in GN⊆Δl\mathbb{G}_N\subseteq\Delta^l, and ee is bounded measurable with ∣et∣≤2|e_t|\le2; each Cc\mathsf{C}^{c} and each Cˉc\bar{\mathsf{C}}^{c} is continuous and nondecreasing on [0,T][0,T] with values in [0,NBT]⊆[0,R][0,NBT]\subseteq[0,R]; for every q=(c,j)q=(c,j) with CˉTc≥bjc\bar{\mathsf{C}}^{c}_T\ge \mathsf{b}^{c}_j the set {u∈[0,T]:Cˉuc≥bjc}\{u\in[0,T]:\bar{\mathsf{C}}^{c}_u\ge \mathsf{b}^{c}_j\} has a least element τˉq\bar{\tau}_q and equals [τˉq,T][\bar{\tau}_q,T]; and, with the perturbation

mt=1N∑c∈Lvc Mˉc(Ctc),Mˉc(x)=pc(x)−x(x∈[0,R]),\mathfrak{m}_t=\frac{1}{N}\sum_{c\in\mathcal{L}}v_c\,\bar{M}^{c}(\mathsf{C}^{c}_t),\qquad \bar{M}^{c}(x)=p^{c}(x)-x\quad(x\in[0,R]),

which is bounded measurable with m0=0\mathfrak{m}_0=0, one has Σt=x0+∫[0,t]b(Σu,au) du+mt\Sigma_t=x_0+\int_{[0,t]}b(\Sigma_u,a_u)\,du+\mathfrak{m}_t for every t∈[0,T]t\in[0,T]. Consequently Linearisation of a Perturbed Controlled Aggregate Flow Along a Comparison Pair: Exact Variation-of-Constants Identity and Residual Bound applies to x=Σx=\Sigma, yy, aa and m\mathfrak{m}.

2. (Linear response in cell-count form.) N c⋅LT(m)=1N∑c∈Lc⋅Λc(Cc)\sqrt{N}\,\mathbf{c}\cdot L_T(\mathfrak{m})=\frac{1}{\sqrt{N}}\sum_{c\in\mathcal{L}}\mathbf{c}\cdot\Lambda^{c}(\mathsf{C}^{c}), where Λc\Lambda^{c} is the compensated functional of Cell-Count Form of a Compensated Counting-Path Functional Along a Time Change: Window-Discrepancy Bounds for the Time-Change and Partial-Cell Errors formed from pcp^{c}, RR, s=Ts=T, v=vcv=v_c and H=HcH=H^{c} (bounded measurable with ∥Hc∥1≤2 Φˉ2ΛET\lVert H^{c}\rVert_1\le\sqrt{2}\,\bar{\Phi}^{2}\Lambda_{\mathcal{E}}T); and

∣N c⋅LT(m)−1N∑q∈Lαq (Kq−μq)∣ ≤ ∣c∣N∑c∈L(2+∥Hc∥1)(Discw1(pc)+Discμmax⁡(pc))=:Ecell.\Bigl|\sqrt{N}\,\mathbf{c}\cdot L_T(\mathfrak{m})-\frac{1}{\sqrt{N}}\sum_{q\in\mathsf{L}}\alpha_q\,(\mathsf{K}_q-\mu_q)\Bigr|\ \le\ \frac{|\mathbf{c}|}{\sqrt{N}}\sum_{c\in\mathcal{L}}\bigl(\sqrt{2}+\lVert H^{c}\rVert_1\bigr)\bigl(\mathrm{Disc}_{w_1}(p^{c})+\mathrm{Disc}_{\mu_{\max}}(p^{c})\bigr)=:\mathsf{E}_{\mathrm{cell}} .

3. (Estimand linearisation.) Writing eˉ=sup⁡u∈[0,T]N ∣eu∣\bar{e}=\sup_{u\in[0,T]}\sqrt{N}\,|e_u|,

∣N c⋅(ΣT−yT)−1N∑q∈Lαq (Kq−μq)∣ ≤ ∣c∣ Φˉ2 N ∣x0−y0∣+∣c∣ Φˉ2 Eres+Ecell,\Bigl|\sqrt{N}\,\mathbf{c}\cdot(\Sigma_T-y_T)-\frac{1}{\sqrt{N}}\sum_{q\in\mathsf{L}}\alpha_q\,(\mathsf{K}_q-\mu_q)\Bigr|\ \le\ |\mathbf{c}|\,\bar{\Phi}^{2}\,\sqrt{N}\,|x_0-y_0|+|\mathbf{c}|\,\bar{\Phi}^{2}\,\mathsf{E}_{\mathrm{res}}+\mathsf{E}_{\mathrm{cell}},

where

Eres=N∫[0,T]∣ρu∣ du ≤ 2 l(l−1)Λ2T eˉ2N+2 l(l−1)Λ3 eˉ∫[0,T]∣yu−Su∣ du+2 eˉ∫[0,T]du du,\mathsf{E}_{\mathrm{res}}=\sqrt{N}\int_{[0,T]}|\rho_u|\,du\ \le\ \sqrt{2}\,l(l-1)\Lambda_2T\,\frac{\bar{e}^{2}}{\sqrt{N}}+\sqrt{2}\,l(l-1)\Lambda_3\,\bar{e}\int_{[0,T]}|y_u-S_u|\,du+\sqrt{2}\,\bar{e}\int_{[0,T]}\mathsf{d}_u\,du ,

the two last integrands being bounded measurable.

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