Z-mappings

for mathematicians

Flavien Léger

Joint work with Alfred Galichon (NYU)

Nonvariational, nonlinear equations discrete and continuous

Common structure to problems in network flows, optimal transport, optimal control, interface dynamics, submodular functions...

\operatorname{det}D^2u = f
H(x,\nabla u) = f

Dates back to Rheinboldt ('70s)

-\operatorname{div}(A \nabla u) = f

Ideal to prove comparison principles

\operatorname{Ric}(g) = f
\partial_tu=-Q(u)

Persists through regularization and continuous ↔ discrete

Prelude

Natural algorithm: Jacobi

Outline

1. Z-mappings

 

2. M-mappings

 

3. The Jacobi algorithm

 

4. Examples

1. Z-mapping

\(Q\) is a Z-mapping if for all \(u,\tilde u\), for all \(x\),

D E F I N I T I O N

\left\{\begin{aligned} u &\leq \tilde u\\ u(x) &= \tilde u(x) \end{aligned}\right. \implies Q_x(\tilde u) \le Q_x(u).

Want to solve equations of the form
$$Q(u) = 0,$$

\(u\colon X\to\mathbb{R}\),   \(X\) finite or \(X\subseteq\mathbb{R}^d\)

\(Q\colon \mathbb{R}^X\to\mathbb{R}^X\)

X

Toy example: \(Q_i(u)=2u_i-u_{i+1}-u_{i-1}.\)

Examples

Square matrix \(A\), solve

\[Au=f\]

Let \(Q(u) = Au-f\). Then

\[Q\text{ is a Z-mapping }\quad\iff\quad a_{ij}\le 0 \text{ for all }i\neq j.\]

Z-matrix

x
y
M_{xy}(u)

On a network \((X,A)\),

\[-\mathrm{div} M(u)=f\]

\(M\colon\mathbb{R}^X\to\mathbb{R}^A\) "flow mapping" such that:

\(M_{xy}(u)\) increasing in \(u_y\) decreasing in \(u_x\).

Ex: \(M_{xy}(u)=u_y-u_x\),

      \(M_{xy}(u)=e^{u_y-u_x}\)

Examples

\[\partial_tu=-Q(u)\]

If \(Q\) is a Z-mapping then so is the spacetime mapping

$$\hat Q_{t,x}(u)\coloneqq \frac{u_{t,x}-u_{t-1,x}}{\tau}+ Q_x(u_t)$$

\(Q(u)\coloneqq u - T(u)\) is a Z-mapping.

(Shortest path, stochastic shortest path, Markov Decision Process)

x

Dynamic programming principle:

\[u(x)=\min_{y}c(x,y)+u(y)\eqqcolon T(u)\]

(Eikonal equation, Hamilton–Jacobi Bellman, viscosity solutions)

2. M-mappings

A Z-mapping \(Q\) is an M-mapping if it satisfies a comparison principle 

$$Q(u)\leq Q(\tilde u)\implies u\leq \tilde u$$

D E F I N I T I O N

\(Q(u)=f,\,Q(\tilde u)=\tilde f \text{  with  } f\leq \tilde f \implies u\leq \tilde u\)

Uniqueness of \(Q(u)=f\)

Maximum principle

Strong comparison principle: if \(V\) is a strongly connected component of an M-mapping \(Q\) then
$$Q(u)\leq Q(\tilde u)\implies u=\tilde u \text{  or  } u <\tilde u \text{ on } V.$$

Suppose that a Z-mapping \(Q\) satisfies: for all \(V\subseteq X\),

\[\left\{\begin{aligned}u&<\tilde u \text{ on } V\\u&=\tilde u \text{ on }X\setminus V\end{aligned}\right.\implies\sum_{x\in V} Q_x(u)<\sum_{x\in V} Q_x(\tilde u)\]

Then \(Q\) is an M-mapping.

T H E O R E M

Berry, Gandhi, Haile '2013; Chen, Choo, Galichon, Weber '2021

Example: \(Q(u)=-\operatorname{div}(a(x,u)\nabla u)-f,\quad a(x,u)\geq c_0>0\)

\[\int_V Q(u)dx=\int_{\partial V} a(x,u)\langle\nabla u,- n\rangle\]

Works for quasilinear PDEs, Monge–Ampère, semi-discrete optimal transport, entropic transport...

Proving a comparison principle

Basic idea: combine the Z-property and “some isotonicity”

3. The Jacobi algorithm

D E F I N I T I O N

The Jacobi transform \(J\colon \mathbb{R}^X\to\mathbb{R}^X\) is defined by

\[Q_x(J_x(u),u_{-x})=0.\]

Jacobi algorithm

\[u_{n+1} = J(u_n)\]

A L G O R I T H M

“coordinate update”

Also related: Gauss–Seidel

Q(u)=0

Properties of Jacobi 

Let \(Q\) be a continuous Z-mapping.

1. If \(Q(u_n)\le 0\) then

\[Q(u_{n+1})\le 0 \quad\text{and}\quad  u_n\leq u_{n+1}\]

2. \[u_n\le \tilde u_n \implies u_{n+1}\leq \tilde{u}_{n+1}\]

P R O P O S I T I O N

1: Useful for algorithm or showing existence of solutions

 "method of subsolutions", "Perron's method"...

2: Useful when sandwich \(v_0\le u_0\le w_0\), then

\[v_n\le u_n\le w_n\]

Rheinbolt ’70

Optimal transport (continuous/discrete/semi-discret/entropic), generated Jacobian equations

\[S_u(y)\coloneqq \argmax_x u(x)-c(x,y)\]

⏵ Discrete: \(Q\) is discontinuous \(\to\) need regularization.

Real auction, or replace min by softmin: Entropic OT.

\[S_{u\#}\nu=\mu\]

\(Q(u) = S_{u\#}\nu\) is a Z-mapping

(X,\mu)
(Y,\nu)
c(x,y)

⏵ Algorithm: Jacobi  = Bertsekas' (naive) auction algorithm

4. Examples

⏵ Monge–Ampère has a comparison principle

⏵ Jacobi = Sinkhorn

Mean curvature motion

mean curvature motion = \(L^2\) gradient flow of
\[P(u)=\frac12\int_{\mathbb{R}^d} \lvert\nabla u\rvert\,dx\]

(\(u=1_K\)).   Heat content:

\[P_\varepsilon(u)=-\frac{1}{\varepsilon}\iint G_\varepsilon(x-y) u(x) (1-u(y))\,dxdy + \int \chi(u(x))\,dx\]

 

submodular, nonconvex

\(Q(u)=DP(u)\) is a Z-mapping.

⏵ Jacobi = MBO

(Merriman–Bence–Osher ’92)

Thank you!