removed some now-spurious math symbol explanations

Author: pavelkomarov

examples/2_optimizing_hyperparameters.ipynb

@@ -51,7 +51,7 @@
     "In the real world, option (1) is not possible, so for (2) we use a loss function that balances the faithfulness and smoothness of the derivative estimate, balanced by a single hyperparameter, $\\gamma$, or `tvgamma` in the code:\n",
     "\n",
     "$$L = \\sigma \\sqrt{\\frac{2}{N}\\sum\\text{Huber}\\bigg( \\frac{\\mathbf{\\hat{x}}(\\Phi) - \\mathbf{y}}{\\sigma}, M \\bigg)} + \\gamma \\bigg({TV}\\big(\\mathbf{\\hat{\\dot{x}}}(\\Phi)\\big)\\bigg),$$\n",
-    "where the left term is a generalization of RMSE that uses the Huber loss, $\\sigma$ is a robust scatter measure, $M$ is a robustness parameter in units of $\\sigma$ (often set to 2 or 3 so the vast majority of inliers are measured with RMSE), $\\mathbf{\\hat{x}}$ is the estimate of the true underlying signal, $\\mathbf{y}$ are the noisy measurements, $\\mathbf{\\hat{\\dot{x}}}$ is the estimate of the derivative, $\\text{trapz}(\\cdot)$ is the discrete-time trapezoidal numerical integral, $\\mu$ resolves the unknown integration constant, $\\gamma$ is a hyper-parameter, and $TV$ is the total variation,\n",
+    "where the left term is a generalization of RMSE that uses the Huber loss, $\\sigma$ is a robust scatter measure, $M$ is a robustness parameter in units of $\\sigma$ (often set to 2 or 3 so the vast majority of inliers are measured with RMSE), $\\mathbf{\\hat{x}}$ is the estimate of the true underlying signal, $\\mathbf{y}$ are the noisy measurements, $\\mathbf{\\hat{\\dot{x}}}$ is the estimate of the derivative, $\\gamma$ is a hyper-parameter, and $TV$ is the total variation,\n",
     "\n",
     "$$TV(\\mathbf{\\hat{\\dot{x}}}) = \\frac{1}{m}\\left\\lVert\\mathbf{\\hat{\\dot{x}}}_{0:m-1}-\\mathbf{\\hat{\\dot{x}}}_{1:m}\\right\\rVert_{1}.$$\n",
     "\n",