DOC Update linear_model.rst : min should be \min (scikit-learn#11374)

Author: josephsalmon

doc/modules/linear_model.rst

@@ -31,7 +31,7 @@ of squares between the observed responses in the dataset, and the
 responses predicted by the linear approximation. Mathematically it
 solves a problem of the form:
 
-.. math:: \underset{w}{min\,} {|| X w - y||_2}^2
+.. math:: \underset{w}{\min\,} {|| X w - y||_2}^2
 
 .. figure:: ../auto_examples/linear_model/images/sphx_glr_plot_ols_001.png
    :target: ../auto_examples/linear_model/plot_ols.html
@@ -83,7 +83,7 @@ of squares,
 
 .. math::
 
-   \underset{w}{min\,} {{|| X w - y||_2}^2 + \alpha {||w||_2}^2}
+   \underset{w}{\min\,} {{|| X w - y||_2}^2 + \alpha {||w||_2}^2}
 
 
 Here, :math:`\alpha \geq 0` is a complexity parameter that controls the amount
@@ -170,7 +170,7 @@ weights (see
 Mathematically, it consists of a linear model trained with :math:`\ell_1` prior
 as regularizer. The objective function to minimize is:
 
-.. math::  \underset{w}{min\,} { \frac{1}{2n_{samples}} ||X w - y||_2 ^ 2 + \alpha ||w||_1}
+.. math::  \underset{w}{\min\,} { \frac{1}{2n_{samples}} ||X w - y||_2 ^ 2 + \alpha ||w||_1}
 
 The lasso estimate thus solves the minimization of the
 least-squares penalty with :math:`\alpha ||w||_1` added, where
@@ -319,7 +319,7 @@ Mathematically, it consists of a linear model trained with a mixed
 :math:`\ell_1` :math:`\ell_2` prior as regularizer.
 The objective function to minimize is:
 
-.. math::  \underset{w}{min\,} { \frac{1}{2n_{samples}} ||X W - Y||_{Fro} ^ 2 + \alpha ||W||_{21}}
+.. math::  \underset{w}{\min\,} { \frac{1}{2n_{samples}} ||X W - Y||_{Fro} ^ 2 + \alpha ||W||_{21}}
 
 where :math:`Fro` indicates the Frobenius norm:
 
@@ -355,7 +355,7 @@ The objective function to minimize is in this case
 
 .. math::
 
-    \underset{w}{min\,} { \frac{1}{2n_{samples}} ||X w - y||_2 ^ 2 + \alpha \rho ||w||_1 +
+    \underset{w}{\min\,} { \frac{1}{2n_{samples}} ||X w - y||_2 ^ 2 + \alpha \rho ||w||_1 +
     \frac{\alpha(1-\rho)}{2} ||w||_2 ^ 2}
 
 
@@ -402,7 +402,7 @@ The objective function to minimize is:
 
 .. math::
 
-    \underset{W}{min\,} { \frac{1}{2n_{samples}} ||X W - Y||_{Fro}^2 + \alpha \rho ||W||_{2 1} +
+    \underset{W}{\min\,} { \frac{1}{2n_{samples}} ||X W - Y||_{Fro}^2 + \alpha \rho ||W||_{2 1} +
     \frac{\alpha(1-\rho)}{2} ||W||_{Fro}^2}
 
 The implementation in the class :class:`MultiTaskElasticNet` uses coordinate descent as
@@ -734,12 +734,12 @@ regularization.
 As an optimization problem, binary class L2 penalized logistic regression
 minimizes the following cost function:
 
-.. math:: \underset{w, c}{min\,} \frac{1}{2}w^T w + C \sum_{i=1}^n \log(\exp(- y_i (X_i^T w + c)) + 1) .
+.. math:: \underset{w, c}{\min\,} \frac{1}{2}w^T w + C \sum_{i=1}^n \log(\exp(- y_i (X_i^T w + c)) + 1) .
 
 Similarly, L1 regularized logistic regression solves the following
 optimization problem
 
-.. math:: \underset{w, c}{min\,} \|w\|_1 + C \sum_{i=1}^n \log(\exp(- y_i (X_i^T w + c)) + 1).
+.. math:: \underset{w, c}{\min\,} \|w\|_1 + C \sum_{i=1}^n \log(\exp(- y_i (X_i^T w + c)) + 1).
 
 Note that, in this notation, it's assumed that the observation :math:`y_i` takes values in the set
 :math:`{-1, 1}` at trial :math:`i`.
@@ -1137,7 +1137,7 @@ The loss function that :class:`HuberRegressor` minimizes is given by
 
 .. math::
 
-  \underset{w, \sigma}{min\,} {\sum_{i=1}^n\left(\sigma + H_m\left(\frac{X_{i}w - y_{i}}{\sigma}\right)\sigma\right) + \alpha {||w||_2}^2}
+  \underset{w, \sigma}{\min\,} {\sum_{i=1}^n\left(\sigma + H_m\left(\frac{X_{i}w - y_{i}}{\sigma}\right)\sigma\right) + \alpha {||w||_2}^2}
 
 where