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@ -14,18 +14,21 @@ The Social (Fleet) Planner's problem can be written in the belman form as:
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+ \Gamma \sum^N_{i=1} x^i_t
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+ \Gamma \sum^N_{i=1} x^i_t
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\end{align}
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\end{align}
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The resulting $N$ optimality conditions are:
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\subsubsection{Euler Equation}
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First find the $N$ optimality conditions:
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\begin{align}
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\begin{align}
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0 =& -\der{F(x^i_t)}{s^i_t}{}
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0 =& -\der{F(x^i_t)}{x^i_t}{}
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+ \beta \left[
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+ \beta \left[
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\nabla_{\vec s_{t+1}, D_{t+1}} W(\vec s_{t+1}, D_{t+1})
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\nabla_{\vec s_{t+1}, D_{t+1}} W(\vec s_{t+1}, D_{t+1})
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\cdot
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\cdot
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\nabla_{\vec s_{t}, D_{t}}[\vec s_{t+1} ~ D_{t+1}]
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\parder{}{x^I_t}{}[\vec s_{t+1} ~ D_{t+1}]
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\right]
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\right]
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~~\forall~~i \\
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~~\forall~~i
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\end{align}
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Which in vector form is:
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\begin{align}
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0 =& -\vec f +\beta \left[B\cdot \nabla W_{t+1} \right]
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0 =& -\vec f +\beta \left[B\cdot \nabla W_{t+1} \right]
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\end{align}
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\end{align}
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And the $N+1$ envelope conditions are:
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And the $N+1$ envelope conditions are:
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\begin{align}
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\begin{align}
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\nabla_{\vec s_{t}, D_{t}} W(\vec s_t, D_t) =&
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\nabla_{\vec s_{t}, D_{t}} W(\vec s_t, D_t) =&
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@ -37,5 +40,10 @@ And the $N+1$ envelope conditions are:
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\right] \\
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\right] \\
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\nabla W_t =& \vec U + \beta \left[C \cdot \nabla W_{t+1} \right]
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\nabla W_t =& \vec U + \beta \left[C \cdot \nabla W_{t+1} \right]
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\end{align}
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\end{align}
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Which gives us the iteration format
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\begin{align}
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\nabla W_{t+1} =& (\beta C)^{-1} \cdot \left(\nabla W_t - \vec U \right)
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\end{align}
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\end{document}
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\end{document}
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