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A unified analysis of convex and non-convex \(\ell _p\)-ball projection problems

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Abstract

The task of projecting onto \(\ell _p\) norm balls is ubiquitous in statistics and machine learning, yet the availability of actionable algorithms for doing so is largely limited to the special cases of \(p \in \left\{ 0, 1,2, \infty \right\}\). In this paper, we introduce novel, scalable methods for projecting onto the \(\ell _p\)-ball for general \(p>0\). For \(p \ge 1\), we solve the univariate Lagrangian dual via a dual Newton method. We then carefully design a bisection approach for \(p<1\), presenting theoretical and empirical evidence of zero or a small duality gap in the non-convex case. The success of our contributions is thoroughly assessed empirically, and applied to large-scale regularized multi-task learning and compressed sensing. The code implementing our methods is publicly available on Github.

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Notes

  1. These properties have emerged in the context of studying theoretical properties of projected gradient descent for \(\ell _p\)-norm constrained least squares (problem (1) with \(\phi (\varvec{x},\varvec{y})=\frac{1}{2}\Vert \varvec{y}- \varvec{A}\varvec{x}\Vert _2^2\)). However, no actual algorithm for \(\ell _p\)-ball projection is provided in [2].

  2. Refer to Theorem 1 in https://planetmath.org/newtonsmethodworksforconvexrealfunctions (retrieved 2022-05-08).

  3. For a direct correspondence between Proposition 2.1 and [25, Theorem 1], \(q \leftarrow p\), \(\beta \leftarrow x\), \(z \leftarrow y\), \(\lambda \leftarrow \mu /p\), \(h_a \leftarrow r_p\), \(\beta _a \leftarrow \kappa _p\), and \(\beta _* = z_p(y)\) when \(\mu = 1\).

  4. Available at https://github.com/albarji/proxTV/blob/master/src/LPopt.cpp.

  5. Available at https://github.com/Optimizater/Lp-ball-Projection.

  6. Available publicly at https://github.com/won-j/ LpBallProjection.

  7. Available at https://github.com/JuliaPy/PyCall.jl.

  8. This optimal tuning parameter as required by the theory of [27] can be relaxed. However, we closely follow the experiment setup of [27] here.

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Acknowledgements

We thank the associate editor and anonymous referees for providing constructive comments, especially for pointing out references [12, 13, 15, 24, 35]. JW was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2019R1A2C1007126). KL was supported by the United States Public Health Service (USPHS) grants GM53275 and HG006139.

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Authors and Affiliations

  1. Department of Statistics, Seoul National University, Seoul, Republic of Korea

    Joong-Ho Won

  2. Departments of Computational Medicine, Human Genetics and Statistics, University of California, Los Angeles, California, USA

    Kenneth Lange

  3. Department of Statistical Science, Duke University, Durham, North Carolina, USA

    Jason Xu

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  1. Joong-Ho Won
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Won, JH., Lange, K. & Xu, J. A unified analysis of convex and non-convex \(\ell _p\)-ball projection problems. Optim Lett 17, 1133–1159 (2023). https://doi.org/10.1007/s11590-022-01919-0

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