In Hastie et al's Elements of Statistical Learning, it says
Least angle regression (LAR) ... can be viewed as a kind of “democratic” version of forward stepwise regression (Section 3.3.2). As we will see, LAR is intimately connected with the lasso, and in fact provides an extremely eﬃcient algorithm for computing the entire lasso path as in Figure 3.10.
Forward stepwise regression tries to solve the following optimization problem for selection of best subset of features: $$ \min_x \|Ax-b\|_2 $$ s.t. $$ \|x\|_0 \leq M. $$
LASSO tries to solve the following optimization problem $$ \min_x \|Ax-b\|_2 $$ s.t. $$ \|x\|_1 \leq \epsilon. $$
I was wondering what optimization problem does least angle regression try to solve?