In our first blog post, we introduced CVXR, an R package for disciplined convex optimization, and showed how to model and solve a non-negative least squares problem using its interface. This time, we will tackle a non-parametric estimation example, which features new atoms as well as more complex constraints.
Direct Standardization Consider a set of observations ((x_i,y_i)) drawn non-uniformly from an unknown distribution. We know the expected value of the columns of (X), denoted by (b \in {\mathbf R}^n), and want to estimate the true distribution of (y).

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