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Solving Equations of Random Convex Functions via Anchored Regression

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We consider the question of estimating a solution to a system of equations that involve convex nonlinearities, a problem that is common in machine learning and signal processing. Because of these nonlinearities, conventional estimators based on empirical risk minimization generally involve solving a non-convex optimization program. We propose anchored regression, a new approach based on convex programming that amounts to maximizing a linear functional (perhaps augmented by a regularizer) over a convex set. The proposed convex program is formulated in the natural space of the problem, and avoids the introduction of auxiliary variables, making it computationally favorable. Working in the native space also provides great flexibility as structural priors (e.g., sparsity) can be seamlessly incorporated. For our analysis, we model the equations as being drawn from a fixed set according to a probability law. Our main results provide guarantees on the accuracy of the estimator in terms of the number of equations we are solving, the amount of noise present, a measure of statistical complexity of the random equations, and the geometry of the regularizer at the true solution. We also provide recipes for constructing the anchor vector (that determines the linear functional to maximize) directly from the observed data.

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  1. Of course, we need to be able to evaluate the \(f_m\) and some number of its derivatives to actually solve (1.6).

  2. Unlike conventional definition of Rademacher complexities, we use a normalization by square root of the number of samples.

  3. Because \(\psi _{\alpha }\left( \cdot \right) \) is bounded, we can treat \(t=0\) as \(t\rightarrow 0\) to avoid the issue of division by zero.


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Correspondence to Sohail Bahmani.

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Communicated by Francis Bach.

This work was supported in part by the Semiconductor Research Corporation (SRC) and DARPA.

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Bahmani, S., Romberg, J. Solving Equations of Random Convex Functions via Anchored Regression. Found Comput Math 19, 813–841 (2019).

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