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Illustration of three piecewise convex functions.

Illustration of three piecewise convex functions.

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Nonconvex and nonsmooth optimization problems are important and challenging for statistics and machine learning. In this paper, we propose Projected Proximal Gradient Descent (PPGD) which solves a class of nonconvex and nonsmooth optimization problems, where the nonconvexity and nonsmoothness come from a nonsmooth regularization term which is nonco...

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... extension applies to the case when q = q m−1 is the left endpoint of Example 3.1 explains how to build surrogate functions for two popular piecewise convex functions in the optimization literature. It can be observed from the definition of surrogate functions and Figure 2 that surrogate functions always have a simpler geometric structure than the original f , so it is usually the case that proximal mapping associated with the surrogate functions have closedform expressions, given that proximal mapping associated with f has a closed-form solution. The proximal mappings associated with the surrogate functions are also given in Example 3.1. ...
Context 2
... extension applies to the case when q = q m−1 is the left endpoint of Example 3.1 explains how to build surrogate functions for two popular piecewise convex functions in the optimization literature. It can be observed from the definition of surrogate functions and Figure 2 that surrogate functions always have a simpler geometric structure than the original f , so it is usually the case that proximal mapping associated with the surrogate functions have closedform expressions, given that proximal mapping associated with f has a closed-form solution. The proximal mappings associated with the surrogate functions are also given in Example 3.1. ...

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