# What is the difference between multi-task lasso regression and ridge regression? The optimization fu

What is the difference between multi-task lasso regression and ridge regression? The optimization function of multi-task lasso regression is
$mi{n}_{w}\sum _{l=1}^{L}1/{N}_{t}\sum _{i=1}^{{N}_{t}}{J}^{l}\left(w,x,y\right)+\gamma \sum _{l=1}^{L}||{w}^{l}|{|}_{2}$
while ridge regression is
$mi{n}_{w}\sum _{l=1}^{L}1/{N}_{t}{J}^{l}\left(w,x,y\right)+\gamma ||{w}^{l}|{|}_{2}$
which looks the same as the ridge regression. As for me, the problem of multi-task lasso regression is equivalent to solve global ridge regression. So what is the difference between these two regression methods? Both of them use ${L}_{2}$ function. Or does it mean that in multi-task lasso regression, the shape of $W$ is (1,n)?
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iskakanjulc
Least Absolute Shrinkage and Selection Operator uses ${L}^{1}$ regularization
$\underset{x}{min}f\left(x\right)+\lambda ‖x{‖}_{1}.$