# Why cant Ridge Regression benift from negative lamda? [duplicate]

in Rigid regression, we generally set a positive Lambda for regularization to get a less Residual. Why cant we have a negative Lambda in a regularization if we can benefit from it? • We do not set a positive $\lambda$ to get smaller residuals; any positive $\lambda$ will produce larger residuals than setting $\lambda = 0$. – jbowman Nov 17 '19 at 3:05
• – user20160 Nov 17 '19 at 3:14

There are two (equivalent) formulations of ridge regression (I mention both because I'm not sure which version you're referring to). If $$\mathbf{X} \in \mathbb{R}^{n \times p}$$ is the design matrix and $$\mathbf{y} \in \mathbb{R}^n$$ is the target vector, then the two formulations are the following ($$\|\cdot\|_2$$ denotes the $$L^2$$ norm).
1. The constrained optimization form: $$\hat{\boldsymbol{\theta}}_{\text{ridge}} = \operatorname*{arg\,min}_{\substack{\boldsymbol{\theta} \in \mathbb{R}^p \\ \|\boldsymbol{\theta}\|_2 \leq \lambda}} \left\|\mathbf{X}\boldsymbol{\theta} - \mathbf{y}\right\|_2^2.$$ Here, if $$\lambda < 0$$, then the constraint $$\|\boldsymbol{\theta}\|_2 \leq \lambda$$ never holds, so the optimization problem is ill-defined.
2. The Lagrangian form: $$\hat{\boldsymbol{\theta}}_{\text{ridge}} = \operatorname*{arg\,min}_{\boldsymbol{\theta} \in \mathbb{R}^p} \left( \left\|\mathbf{X}\boldsymbol{\theta} - \mathbf{y}\right\|_2^2 + \lambda \|\boldsymbol{\theta}\|_2^2\right)$$ (this $$\lambda$$ is not the same as the $$\lambda$$ in the first formulation, but they are related). Here, if $$\lambda < 0$$, then the optimization problem encourages the $$L^2$$ norm of the vector $$\boldsymbol{\theta}$$ to get as large as possible, which goes against the point of regularization.
Thus, in both formulations of ridge regressions, choosing a negative $$\lambda$$ leads to undesirable results.
• In the lagrangian form with negative $\lambda$, there is no minimum because the norm of $\theta$ can grow without bound. But, apparently people have used variants of 'negative ridge regression' (e.g. the paper referenced in this thread) – user20160 Nov 17 '19 at 3:32