arXiv subjects

Zhuorui Li

Publications and source records attributed to Zhuorui Li.

6 recordsLinked to original sources

arXiv2026

Solving Sparsity Constrained PCA, Regression, and QCQP via the Spartrahedron

Sparsity is a fundamental modeling principle in statistics, signal processing, and data science. However, optimization with sparsity constraints is notoriously difficult. We introduce a new convex relaxation framework for {sparse quadratically constrained quadratic programs} (QCQPs), a class that subsumes sparse regression, sparse principal component analysis (PCA), and related problems. Our approach is based on a novel convex cone, the spartrahedron, which exactly characterizes sparsity at the matrix level. This leads to a semidefinite programming (SDP) relaxation that is tight whenever its solution is rank-one, providing a simple certificate of global optimality. We establish theoretical guarantees, including approximation bounds and exactness regions for sparse PCA and sparse ridge regression, as well as a general stability result under perturbations. Numerical experiments on sparse PCA, sparse regression, RIP constant estimation, and sparse canonical correlation analysis (CCA) demonstrate the practical success of our methods.

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arXiv2025

Solving exact and noisy rank-one tensor completion with semidefinite programming

Consider recovering a rank-one tensor of size $n_1 \times \cdots \times n_d$ from exact or noisy observations of a few of its entries. We tackle this problem via semidefinite programming (SDP). We derive deterministic combinatorial conditions on the observation mask $\Omega$ (the set of observed indices) under which our SDPs solve the exact completion and achieve robust recovery in the noisy regime. These conditions can be met with as few as $\bigl(\sum_{i=1}^d n_i\bigr) - d + 1$ observations for special $\Omega$. When $\Omega$ is uniformly random, our conditions hold with $O\!\bigl((\prod_{i=1}^d n_i)^{1/2}\,\mathrm{polylog}(\prod_{i=1}^d n_i)\bigr)$ observations. Prior works mostly focus on the uniformly random case, ignoring the practical relevance of structured masks. For $d=2$ (matrix completion), our propagation condition holds if and only if the completion problem admits a unique solution. Our results apply to tensors of arbitrary order and cover both exact and noisy settings. In contrast to much of the literature, our guarantees rely solely on the combinatorial structure of the observation mask, without incoherence assumptions on the ground-truth tensor or uniform randomness of the samples. Preliminary computational experiments show that our SDP methods solve tensor completion problems using significantly fewer observations than alternative methods.

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arXiv2024

A Robust Compressed Push-Pull Method for Decentralized Nonconvex Optimization

In the modern paradigm of multi-agent networks, communication has become one of the main bottlenecks for decentralized optimization, where a large number of agents are involved in minimizing the average of the local cost functions. In this paper, we propose a robust compressed push-pull algorithm (RCPP) that combines gradient tracking with communication compression. In particular, RCPP is robust under a much more general class of compression operators that allow both relative and absolute compression errors, in contrast to the existing works which can handle either one of them or assume convex problems. We show that RCPP enjoys sublinear convergence rate for smooth and possibly nonconvex objective functions over general directed networks. Moreover, under the additional Polyak-Łojasiewicz condition, linear convergence rate can be achieved for RCPP. Numerical examples verify the theoretical findings and demonstrate the efficiency, flexibility, and robustness of the proposed algorithm.

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arXiv2023

A Linearly Convergent Robust Compressed Push-Pull Method for Decentralized Optimization

In the modern paradigm of multi-agent networks, communication has become one of the main bottlenecks for decentralized optimization, where a large number of agents are involved in minimizing the average of the local cost functions. In this paper, we propose a robust compressed push-pull algorithm (RCPP) that combines gradient tracking with communication compression. In particular, RCPP is compatible with a much more general class of compression operators that allow both relative and absolute compression errors. We show that RCPP achieves linear convergence rate for smooth objective functions satisfying the Polyak-Łojasiewicz condition over general directed networks. Numerical examples verify the theoretical findings and demonstrate the efficiency, flexibility, and robustness of the proposed algorithm.

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arXiv2022

A Compressed Gradient Tracking Method for Decentralized Optimization with Linear Convergence

Communication compression techniques are of growing interests for solving the decentralized optimization problem under limited communication, where the global objective is to minimize the average of local cost functions over a multi-agent network using only local computation and peer-to-peer communication. In this paper, we propose a novel compressed gradient tracking algorithm (C-GT) that combines gradient tracking technique with communication compression. In particular, C-GT is compatible with a general class of compression operators that unifies both unbiased and biased compressors. We show that C-GT inherits the advantages of gradient tracking-based algorithms and achieves linear convergence rate for strongly convex and smooth objective functions. Numerical examples complement the theoretical findings and demonstrate the efficiency and flexibility of the proposed algorithm.

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arXiv2021

Compressed Gradient Tracking Methods for Decentralized Optimization with Linear Convergence

Communication compression techniques are of growing interests for solving the decentralized optimization problem under limited communication, where the global objective is to minimize the average of local cost functions over a multi-agent network using only local computation and peer-to-peer communication. In this paper, we first propose a novel compressed gradient tracking algorithm (C-GT) that combines gradient tracking technique with communication compression. In particular, C-GT is compatible with a general class of compression operators that unifies both unbiased and biased compressors. We show that C-GT inherits the advantages of gradient tracking-based algorithms and achieves linear convergence rate for strongly convex and smooth objective functions. In the second part of this paper, we propose an error feedback based compressed gradient tracking algorithm (EF-C-GT) to further improve the algorithm efficiency for biased compression operators. Numerical examples complement the theoretical findings and demonstrate the efficiency and flexibility of the proposed algorithms.

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