The Proxy Step-size Technique for Regularized Optimization on the Sphere Manifold.
- Publisher:
- Institute of Electrical and Electronics Engineers (IEEE)
- Publication Type:
- Journal Article
- Citation:
- IEEE Trans Pattern Anal Mach Intell, 2022, PP, (99), pp. 1-17
- Issue Date:
- 2022-10-19
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| The_Proxy_Step-size_Technique_for_Regularized_Optimization_on_the_Sphere_Manifold.pdf | Published version | 2.25 MB |
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We give an effective solution to the regularized optimization problem g (x) + h (x), where x is constrained on the unit sphere ||x ||2 = 1. Here g (·) is a smooth cost with Lipschitz continuous gradient within the unit ball {x : ||x ||2 ≤ 1 } whereas h (·) is typically non-smooth but convex and absolutely homogeneous, e.g., norm regularizers and their combinations. Our solution is based on the Riemannian proximal gradient, using an idea we call proxy step-size - a scalar variable which we prove is monotone with respect to the actual step-size within an interval. The proxy step-size exists ubiquitously for convex and absolutely homogeneous h(·), and decides the actual step-size and the tangent update in closed-form, thus the complete proximal gradient iteration. Based on these insights, we design a Riemannian proximal gradient method using the proxy step-size. We prove that our method converges to a critical point, guided by a line-search technique based on the g(·) cost only. The proposed method can be implemented in a couple of lines of code. We show its usefulness by applying nuclear norm, l1 norm, and nuclear-spectral norm regularization to three classical computer vision problems. The improvements are consistent and backed by numerical experiments.
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