# Fractional deviance for lambda in cv.glmnet for non-negative Lasso

 Hi, I want to solve the following optimisation problem: [image: \hat{\beta} = \arg \min_{\beta \geq 0} \| y-A\beta \|_2^2 + \lambda \|\beta\|_1] For that, I am using glmnet package (cv.glmnet for finding 𝜆 and lower.limits = 0 to impose non-negativity). I would like to modify the fdev parameter (minimum fractional change in deviance for stopping path) in glmnet function. This modification seems impossible when lower.limits=0 (non-negative coefficients) is specified. Here is a minimal working example # MWE # Generate data P = 100 # number of sensors R = 10 # number of sources beta = runif(R,0,1) %>% matrix beta[1:7] = 0 # optimal solution is sparse A = replicate(R,runif(P,0,1)) y = A %*% beta # set all control parameters of glmnet to factory parameters (fdev = 1e-5) glmnet.control(factory = T) # Now set the stopping criterion for the lambda path (fdev) to a bigger value, say 1e-1 glmnet.control(fdev = 1e-1) # Without any constraint cvfit = glmnet::cv.glmnet(A, y, type.measure = "mse", nfolds = 10,intercept=T,nlambda=100,parallel = F) cvfit_fdev = diff(cvfit$glmnet.fit$dev.ratio)/cvfit$glmnet.fit$dev.ratio[-1] print(cvfit_fdev) # fdev = 0.1 is respected # With non-negativity constraint cvfit = glmnet::cv.glmnet(A, y, type.measure = "mse", nfolds = 10,intercept=T,lower.limits=0,nlambda=100,parallel = F) cvfit_fdev = diff(cvfit$glmnet.fit$dev.ratio)/cvfit$glmnet.fit$dev.ratio[-1] print(cvfit_fdev) # fdev = 0.1 is not respected I would like to know if it is a known bug (I couldn't find it on Google) or if I am simply doing something wrong. Thanks a lot         [[alternative HTML version deleted]] ______________________________________________ [hidden email] mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code.