Selected work

Representative Papers

Compressed Newton-direction-based Thresholding Methods for Sparse Optimization Problems

Oct 05, 2025

To address the high computational cost and difficulty in balancing convergence efficiency of thresholding-based Newton-type methods for sparse optimization, this paper proposes a Compressed Newton Thresholding Optimization framework. It compresses the Newton direction onto a low-dimensional subspace and incorporates diagonal regularization, yielding two novel algorithms: Compressed Newton Hard Thresholding Pursuit (CNHTP) and Compressed Newton Optimal Thresholding Pursuit (CNOTP). Under the Restricted Isometry Property (RIP), we establish rigorous theoretical guarantees of global convergence. The algorithms integrate compressed direction computation, adaptive thresholding selection, and regularization, preserving Newton-type convergence rates while significantly reducing per-iteration complexity. Experiments demonstrate that the proposed methods match state-of-the-art algorithms in recovery success rate and solution accuracy, while achieving superior computational efficiency and robustness.

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Splitting Alternating Algorithms for Sparse Solutions of Linear Systems with Concatenated Orthogonal Matrices

Sep 29, 2025

This work addresses the sparse solution recovery problem for linear systems involving concatenated orthogonal matrices. To exploit their structural properties, we propose a splitting-based alternating optimization framework—supporting both two-block and multi-block decompositions—that relies solely on matrix-vector products and low-dimensional orthogonal projections, thereby avoiding explicit matrix inversion or storage. By decomposing the large-scale system into coupled subsystems and solving them cooperatively via iterative updates, the algorithm is proven to converge globally to the sparse solution under a coherence constraint. Compared to mainstream iterative methods—including Orthogonal Matching Pursuit (OMP) and Iterative Shrinkage-Thresholding Algorithm (ISTA)—the proposed approach significantly reduces iteration counts while achieving faster convergence and enhanced numerical stability. This yields an efficient, scalable, and structurally aware paradigm for high-dimensional sparse signal recovery.

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Recent publications

Latest Papers

Compressed Newton-direction-based Thresholding Methods for Sparse Optimization Problems

Oct 05, 2025

To address the high computational cost and difficulty in balancing convergence efficiency of thresholding-based Newton-type methods for sparse optimization, this paper proposes a Compressed Newton Thresholding Optimization framework. It compresses the Newton direction onto a low-dimensional subspace and incorporates diagonal regularization, yielding two novel algorithms: Compressed Newton Hard Thresholding Pursuit (CNHTP) and Compressed Newton Optimal Thresholding Pursuit (CNOTP). Under the Restricted Isometry Property (RIP), we establish rigorous theoretical guarantees of global convergence. The algorithms integrate compressed direction computation, adaptive thresholding selection, and regularization, preserving Newton-type convergence rates while significantly reducing per-iteration complexity. Experiments demonstrate that the proposed methods match state-of-the-art algorithms in recovery success rate and solution accuracy, while achieving superior computational efficiency and robustness.

0 citationsRead paper

Splitting Alternating Algorithms for Sparse Solutions of Linear Systems with Concatenated Orthogonal Matrices

Sep 29, 2025

This work addresses the sparse solution recovery problem for linear systems involving concatenated orthogonal matrices. To exploit their structural properties, we propose a splitting-based alternating optimization framework—supporting both two-block and multi-block decompositions—that relies solely on matrix-vector products and low-dimensional orthogonal projections, thereby avoiding explicit matrix inversion or storage. By decomposing the large-scale system into coupled subsystems and solving them cooperatively via iterative updates, the algorithm is proven to converge globally to the sparse solution under a coherence constraint. Compared to mainstream iterative methods—including Orthogonal Matching Pursuit (OMP) and Iterative Shrinkage-Thresholding Algorithm (ISTA)—the proposed approach significantly reduces iteration counts while achieving faster convergence and enhanced numerical stability. This yields an efficient, scalable, and structurally aware paradigm for high-dimensional sparse signal recovery.

0 citationsRead paper