From 8b49f99f1ce5940d3527e75941e927e13bf22885 Mon Sep 17 00:00:00 2001 From: Frederik Baymler Mathiesen Date: Thu, 21 Nov 2024 15:23:32 +0100 Subject: [PATCH] Fix description of bounds on initial conditions for Optimization under Uncertainty showcase. --- docs/src/showcase/optimization_under_uncertainty.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/src/showcase/optimization_under_uncertainty.md b/docs/src/showcase/optimization_under_uncertainty.md index 116a2901373..a0a4a04f58c 100644 --- a/docs/src/showcase/optimization_under_uncertainty.md +++ b/docs/src/showcase/optimization_under_uncertainty.md @@ -113,7 +113,7 @@ sol.u ## Optimization Under Uncertainty -We now wish to optimize the initial position ($x_0,y_0$) and horizontal velocity ($\dot{x}_0$) of the system to minimize the expected squared miss distance from the star, where $x_0\in\left[-100,0\right]$, $y_0\in\left[1,3\right]$, and $\dot{x}_0\in\left[10,50\right]$. We will demonstrate this using a gradient-based optimization approach from NLopt.jl using `ForwardDiff.jl` AD through the expectation calculation. +We now wish to optimize the initial position ($x_0,y_0$) and horizontal velocity ($\dot{x}_0$) of the system to minimize the expected squared miss distance from the star, where $x_0\in\left[-100,0\right]$, $\dot{x}_0\in\left[1,3\right]$, and $y_0\in\left[10,50\right]$. We will demonstrate this using a gradient-based optimization approach from NLopt.jl using `ForwardDiff.jl` AD through the expectation calculation. ```@example control using Optimization, OptimizationNLopt, OptimizationMOI