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First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper investigate fast convergence properties of proximal gradient method and proximal Newton method under the assumption of Constant Nullspace Strong Convexity (CNSC). The problem of interest is to minimize the sum of two convex functions f(x)+h(x), where f is twice differentiable (smooth) and h can be non-smooth but admits a simple proximal mapping. Under the CNSC assumption on f and assuming h has the form of decomposable norm, this paper showed global geometric convergence of the proximal gradient method, and local quadratic convergence of the proximal Newton method. Writing of this paper is very clear.
Neural Information Processing Systems
Oct-3-2025, 04:36:22 GMT
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- North America > Canada > Quebec > Montreal (0.04)
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- Overview (0.90)
- Research Report (0.93)
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