- arg max or argmax) and
arguments of the
minima (abbreviated arg min or
argmin) are the
input points at
which a
function output value is
maximized and...
-
Mahalanobis length of this
residual vector: β ^ =
argmin b ( y − X b ) T Ω − 1 ( y − X b ) =
argmin b y T Ω − 1 y + ( X b ) T Ω − 1 X b − y T Ω − 1 X...
- f\rVert \right).} Then, any
minimizer of the
empirical risk f ∗ =
argmin f ∈ H k { E ( ( x 1 , y 1 , f ( x 1 ) ) , … , ( x n , y n , f ( x n ) )...
- {1}{n}}(x_{1}+\cdots +x_{n})}
argmin x ∈ R ∑ i = 1 n ( x − x i ) 2 {\displaystyle {\underset {x\in \mathbb {R} }{\operatorname {
argmin} }}\,\sum _{i=1}^{n}(x-x_{i})^{2}}...
- {\displaystyle C} we have that prox ι C ( x ) =
argmin y { 1 2 ‖ x − y ‖ 2 2 if y ∈ C + ∞ if y ∉ C =
argmin y ∈ C 1 2 ‖ x − y ‖ 2 2 {\displaystyle...
-
least squares):
quantile ( τ ) ∈
argmin t ∈ R E [ | X − t | | τ − H ( t − X ) | ]
expectile ( τ ) ∈
argmin t ∈ R E [ | X − t | 2 | τ − H (...
-
maximizing distribution. β ^ G M M =
argmin m ( x , β ) ′ W m ( x , β ) {\displaystyle {\hat {\beta }}_{GMM}=\operatorname {
argmin} \,m(x,\beta )'Wm(x,\beta )}...
- {\displaystyle {\mathcal {H}}} by
minimizing the
regularized empirical risk: f ∗ =
argmin f ( ∑ i = 1 l ( 1 − y i f ( x i ) ) + + λ 1 ‖ h ‖ H 2 + λ 2 ∑ i = l + 1...
- ^ ∈
argmin β S ( β ) ≡
argmin β ∑ i = 1 m [ y i − f ( x i , β ) ] 2 , {\displaystyle {\hat {\boldsymbol {\beta }}}\in \operatorname {
argmin} \limits...
- β ^ ≡
argmin β ( ‖ y − X β ‖ 2 + λ 2 ‖ β ‖ 2 + λ 1 ‖ β ‖ 1 ) . {\displaystyle {\hat {\beta }}\equiv {\underset {\beta }{\operatorname {
argmin} }}(\|y-X\beta...
