Fix typos in least_squares notebook.
PiperOrigin-RevId: 635696410 Change-Id: Iba9a2c8f121ca4994678ba933e19dce5323da27b
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@@ -2179,7 +2179,7 @@
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"source": [
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"### Non-quadratic norms\n",
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"\n",
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"As explained in the background section, Least Squares can be generalized to norms other than the quadratic. We are now in a position to show how to define a non-quadratic norm, which is important in the estimation and system-identification contexts, where long-taled, disturbance-rejecting distributions are proportional to the exponent of a non-quadratic function.\n",
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"As explained in the background section, Least Squares can be generalized to norms other than the quadratic. We are now in a position to show how to define a non-quadratic norm, which is important in the estimation and system-identification contexts, where long-tailed, disturbance-rejecting distributions are proportional to the exponent of a non-quadratic function.\n",
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"\n",
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"Let's say that we wish the task residual i.e., the vector from the hand to the target, to be evaluated with the \"Smooth L2\" function $c(r)$ which, for a given smoothing radius $d \\gt 0$ is\n",
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"$$\n",
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@@ -2193,7 +2193,7 @@
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"H &=\\tfrac{\\partial^2 c}{\\partial r^2} = \\frac{I_{n_r} - g\\cdot g^T}{s}\n",
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"\\end{align}\n",
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"$$\n",
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"There is no particularly good reason to use this norm for this optimization task, it is meerly an example.\n",
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"There is no particularly good reason to use this norm for this optimization task, it is merely an example.\n",
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"\n",
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"Let's read the documentation of the `minimize.Norm` class:\n"
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]
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@@ -2215,7 +2215,7 @@
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"id": "3JGpR47DQ9S2"
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},
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"source": [
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"Our sensors are 3 `r_pos` residual values for the hand-to-object vector followed by 21 `r_torque` actuator torques, for a total of `ns = 24` sensors. These are concatented for the entire trajectory, leading to a residual of size `24*N`, where `N` is the number of timesteps in a trajectory. After reshaping and slicing appropriately, the norm implementation looks like"
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"Our sensors are 3 `r_pos` residual values for the hand-to-object vector followed by 21 `r_torque` actuator torques, for a total of `ns = 24` sensors. These are concatenated for the entire trajectory, leading to a residual of size `24*N`, where `N` is the number of timesteps in a trajectory. After reshaping and slicing appropriately, the norm implementation looks as follows:"
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]
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},
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{
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@@ -2291,7 +2291,7 @@
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"id": "J0brs9pG0-8Y"
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},
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"source": [
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"Now that we are confident of our implemetation, we can see what the solution looks like:"
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"Now that we are confident in our implemetation, we can see what the solution looks like:"
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]
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},
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{
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