Lasso, on the other hand, is somewhat indifferent to very correlated predictors, and will tend to pick one and ignore the rest. In the extreme case above, the lasso problem breaks down. The Lasso penalty corresponds to a Laplace prior, which expects many coefficients to be close to zero, and a small subset to be larger and nonzero.
