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clean up the math
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lectures/match_transport.md

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@@ -53,7 +53,9 @@ We will refer to these two measures as *marginals*.
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We assume that
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$$\sum_{x \in X} n_x = \sum_{y \in Y} m_y =: N$$
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$$
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\sum_{x \in X} n_x = \sum_{y \in Y} m_y =: N
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$$
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so that the matching problem is *balanced*.
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@@ -700,7 +702,7 @@ example_off_diag.plot_layers()
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Recall that layer $L_\ell$ consists of a list of distinct types from $Y \sqcup X$
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$$
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z_1 < z_2\dots < z_{N_\ell-1} < z_{N_\ell},
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z_1 < z_2\dots < z_{N_\ell-1} < z_{N_\ell},
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$$
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which is alternating.
@@ -1387,7 +1389,9 @@ The following example shows that composite matching can feature both positive an
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Suppose that there are two agents per side and types
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$$ \textcolor{blue}{x_0} < \textcolor{red}{y_0} < \textcolor{blue}{x_1} < \textcolor{red}{y_1}$$
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$$
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\textcolor{blue}{x_0} < \textcolor{red}{y_0} < \textcolor{blue}{x_1} < \textcolor{red}{y_1}
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$$
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There are two feasible matchings, one corresponding to PAM, the other to NAM.
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@@ -1632,12 +1636,10 @@ The *dual problem* is
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$$
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\begin{aligned}
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V_D = \max_{\phi,\psi}& \sum_{x \in X }n_x \phi_x + \sum_{y \in Y} m_y \psi_y\\
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\text{s.t. }& \phi_x + \psi_y \leq c_{xy} \\
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\end{aligned}
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$$
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where $(\phi , \psi) $ are dual variables, which can be interpreted as shadow cost of agents in $X$ and $Y$, respectively.
@@ -1909,6 +1911,7 @@ Indeed, for any subpair $(x_1,y_1)$ of $(x_0,y_0)$, the dual variables of all th
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But dual feasibility is not satisfied globally in general, for instance it might not be satisfied for two subpairs $(x_1,y_1)$ and $(x_2,y_2)$ of $(x_0,y_0).$
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Therefore, letting $(x_1,y_1), \dots, (x_p,y_p)$ be the subpairs of $(x_0,y_0),$ we compute the solution $(\beta_2, \dots, \beta_p) $ of the linear system
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$$
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\max (c_{x_0 y_0} - c_{x_0 y_i} - c_{x_j y_0} , - c_{x_j y_i}) + c_{x_i y_i}
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\leq \sum_{k=i+1}^{j} \beta_k

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