uniquenessLARSThe lasso is a popular tool for sparse linear regression, especially for problems in which the number of variables p exceeds the number of observations n . But when p > n , the lasso criterion is not strictly convex, and hence it may not have a unique minimizer. An important...
The Group-Lasso method for finding important explanatory factors suffers from the potential non-uniqueness of solutions and also from high computational costs. We formulate conditions for the uniqueness of Group-Lasso solutions which lead to an easily implementable test procedure that allows us to ident...
On the Q-linear convergence of forward-backward splitting method and uniqueness of optimal solution to Lasso We first establish that this method exhibits global convergence to an optimal solution of the problem (if it exists) without the usual assumption that the gradient of the differentiable functi...
On the Q-linear convergence of forward-backward splitting method and uniqueness of optimal solution to Lasso In this paper, by using tools of second-order variational analysis, we study the popular forward-backward splitting method with Beck-Teboulle's line-search for solving convex optimization pro...
of transference: The case of monosyllabic salience in Hong Kong Cantonese Uniquenessand grammatical relations in Upper Necaxa Totonac When terminologymatters: The imperative as a comparative concept Using distributionalsemantics to study syntactic productivity in diachrony: A case study Dogon ...
“You owe me a favor,” I told him. “You’re going to have some student who is going to communicate to you from a place of emotion and stress. You owe them the same grace I gave you. You need to pay it forward.” There’s a quote from Ted Lasso that I thought of after Chri...
Under common and standard assumptions, we show that the proposed method weakly converges to a solution of the SFP. Numerical examples illustrating our method's efficiency are presented for solving the LASSO problem in which the goal is to recover a sparse signal from a limited number of ...
We found that LASSO regression and Bayesian ridge regression performed nearly as well as ridge regression, and gradient boosted trees performed nearly as well as random forest, so we omitted them from the table. Two key observations can be made. The first is that the sum over bonds ...
This type of asymptotic framework reflects the complexity of the problem and the computation demand associated with a large number of candidate moments. Our method achieves consistent moment selection via an information-based adaptive GMM shrinkage estimation. Assuming there exists a conservative set of ...
EN [42] could be represented as a combination of lasso regression [43] and ridge regression [44]. It is a linear model with the penalty being a mix of L1 and L2 terms. Similar to modeling with KRR, EN hyperparameters were tuned up in a way it has been described in the article of...