Abstract: The application of the lasso is espoused in high-dimensional settings where only a small number of the regression coefficients are believed to be nonzero. Moreover, statistical properties of high-dimensional lasso estimators are often proved under the assumption that the correlation between the predictors is bounded. In this vein, coordinatewise methods, the most common means of computing the lasso solution, work well in the presence of low to moderate multicollinearity. Motivated by these limitations, we propose the novel "Deterministic Bayesian Lasso" algorithm for computing the lasso solution. This algorithm is developed by considering a limiting version of the Bayesian lasso.