Complexity Analysis of the Lasso Regularization Path

05/01/2012
by   Julien Mairal, et al.
0

The regularization path of the Lasso can be shown to be piecewise linear, making it possible to "follow" and explicitly compute the entire path. We analyze in this paper this popular strategy, and prove that its worst case complexity is exponential in the number of variables. We then oppose this pessimistic result to an (optimistic) approximate analysis: We show that an approximate path with at most O(1/sqrt(epsilon)) linear segments can always be obtained, where every point on the path is guaranteed to be optimal up to a relative epsilon-duality gap. We complete our theoretical analysis with a practical algorithm to compute these approximate paths.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/08/2018

The Well Tempered Lasso

We study the complexity of the entire regularization path for least squa...
research
03/27/2009

An Exponential Lower Bound on the Complexity of Regularization Paths

For a variety of regularized optimization problems in machine learning, ...
research
10/02/2017

Lasso Regularization Paths for NARMAX Models via Coordinate Descent

We propose a new algorithm for estimating NARMAX models with L1 regulari...
research
03/27/2009

A Combinatorial Algorithm to Compute Regularization Paths

For a wide variety of regularization methods, algorithms computing the e...
research
05/13/2020

Structure and Algorithm for Path of Solutions to a Class of Fused Lasso Problems

We study a class of fused lasso problems where the estimated parameters ...
research
08/16/2007

Piecewise linear regularized solution paths

We consider the generic regularized optimization problem β̂(λ)=_βL(y,Xβ)...
research
09/08/2015

On the complexity of piecewise affine system identification

The paper provides results regarding the computational complexity of hyb...

Please sign up or login with your details

Forgot password? Click here to reset