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Oriented swap process and last passage percolation notes

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Definitions

  1. Oriented swap process (OSP)
  2. Last passage percolation (LPP)
  3. Point-to-line and line-to-line LPP
  4. Particle and block finishing times
  5. The absorbing time

add definitions here

\begin{align*} T(a,b;c,d) &= \max_{\pi \in \Pi_{(a,b)\to(c,d)}} \sum_{(i,j)\in \pi} X_{i,j} \quad \textrm{(last passage percolation time from }(a,b)\textrm{ to }(c,d) \textrm{)} \\ \mathbf{V}^n &= (V^n_1,\ldots,V^n_{n-1}) \quad \qquad \qquad \qquad \qquad \qquad \qquad \textrm{(block finishing times)} \\ \mathbf{U}^n &= (T(1,1; n,1), T(1,1; n-1,2), \ldots, T(1,1; 1,n)) \qquad \textrm{(point to line last passage percolation)} \\ \mathbf{W}^n &= (T(1,1; n,1), T(2,1; n,2), \ldots, T(n,1; n,n)) \qquad \textrm{(line to line last passage percolation)} \end{align*}

A theorem: equidistribution of point-to-line and line-to-line

Theorem. $ \mathbf{U}^n \stackrel{D}{=} \mathbf{W}^n $

Proof. add this

A conjecture: equidistribution to the vector of finishing times

Conjecture. $ \mathbf{V}^n \stackrel{D}{=} \mathbf{W}^n $

Recurrence relation for the point-to-line joint distribution

Proposition. Let $f_n(x_1,\ldots,x_n)$ denote the joint density function of the random vector $\mathbf{U}^n$. Then we have the recurrence relation $$ f_{n+1}(x_1,\ldots,x_{n+1}) = \exp\left( - \sum_{k=1}^{n+1} x_k \right) \int_0^{x_1 \wedge x_2} \int_0^{x_2 \wedge x_3} \ldots \int_0^{x_n \wedge x_{n+1}} f_n(y_1,\ldots,y_n) \exp\left( \sum_{k=1}^{n+1} y_{k-1} \vee y_k \right) dy_n \, dy_{n-1} \ldots dy_2 \, dy_1 $$

Numerical results: the joint distribution for $n=4, 5$

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A formula for the joint density of the finishing times

add an explanation of the algorithm for computing the joint density of the finishing times

Jeu de taquin and the joint distribution of finishing time pairs $V_n(k), V_n(k+1)$

Weak version of the main conjecture. For $n\ge2$ and $1\le k< n$, we have the equality in distribution $$ (V^n(k), V^n(k+1)) \stackrel{D}{=} (U^n_k, U^n_{k+1}). $$

Consider as before an array $(X_{i,j)})_{i,j\ge 1}$ of iid $\operatorname{Exp}(1)$ random variables. Denote $$ X_{i,j}' = \begin{cases} 0 & \textrm{if }(i,j)=(1,1) \\ X_{i,j} & \textrm{otherwise.} \end{cases} $$ Let $T(a,b;c,d)$ denote as before the last passage percolation times associated with the array $(X_{i,j})_{i,j}$. We have \begin{align*} U^n_k &= T(1,1; k, n+1-k), \\ U^n_{k+1} &= T(1,1; k+1, n-k). \end{align*} Let $T'(a,b;c,d)$ denote the last passage percolation times associated with the modified array $(X_{i,j}')_{i,j}$. Now consider the rectangular $(k+1)\times (n-k)$ subarray $$ \mathbf{\tau}_{n,k} = (T'(1,1; i,j))_{1\le i\le k+1, 1\le j\le n-k} $$