Abstract LetXbe a compact normal complex space of dimensionnandLbe a holomorphic line bundle onX. Suppose that$$\Sigma =(\Sigma _1,\ldots ,\Sigma _\ell )$$ is an$$\ell $$ -tuple of distinct irreducible proper analytic subsets ofX,$$\tau =(\tau _1,\ldots ,\tau _\ell )$$ is an$$\ell $$ -tuple of positive real numbers, and let$$H^0_0(X,L^p)$$ be the space of holomorphic sections of$$L^p:=L^{\otimes p}$$ that vanish to order at least$$\tau _jp$$ along$$\Sigma _j$$ ,$$1\le j\le \ell $$ . If$$Y\subset X$$ is an irreducible analytic subset of dimensionm, we consider the space$$H^0_0 (X|Y, L^p)$$ of holomorphic sections of$$L^p|_Y$$ that extend to global holomorphic sections in$$H^0_0(X,L^p)$$ . Assuming that the triplet$$(L,\Sigma ,\tau )$$ is big in the sense that$$\dim H^0_0(X,L^p)\sim p^n$$ , we give a general condition onYto ensure that$$\dim H^0_0(X|Y,L^p)\sim p^m$$ . WhenLis endowed with a continuous Hermitian metric, we show that the Fubini-Study currents of the spaces$$H^0_0(X|Y,L^p)$$ converge to a certain equilibrium current onY. We apply this to the study of the equidistribution of zeros inYof random holomorphic sections in$$H^0_0(X|Y,L^p)$$ as$$p\rightarrow \infty $$ .
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Sparse Recovery: The Square of $$\ell _1/\ell _2$$ Norms
Abstract This paper introduces a nonconvex approach for sparse signal recovery, proposing a novel model termed the$$\tau _2$$ -model, which utilizes the squared$$\ell _1/\ell _2$$ norms for this purpose. Our model offers an advancement over the$$\ell _0$$ norm, which is often computationally intractable and less effective in practical scenarios. Grounded in the concept of effective sparsity, our approach robustly measures the number of significant coordinates in a signal, making it a powerful alternative for sparse signal estimation. The$$\tau _2$$ -model is particularly advantageous due to its computational efficiency and practical applicability. We detail two accompanying algorithms based on Dinkelbach’s procedure and a difference of convex functions strategy. The first algorithm views the model as a linear-constrained quadratic programming problem in noiseless scenarios and as a quadratic-constrained quadratic programming problem in noisy scenarios. The second algorithm, capable of handling both noiseless and noisy cases, is based on the alternating direction linearized proximal method of multipliers. We also explore the model’s properties, including the existence of solutions under certain conditions, and discuss the convergence properties of the algorithms. Numerical experiments with various sensing matrices validate the effectiveness of our proposed model.
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- Award ID(s):
- 2208385
- PAR ID:
- 10558173
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Journal of Scientific Computing
- Volume:
- 102
- Issue:
- 1
- ISSN:
- 0885-7474
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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