Dropping Convexity for Faster Semi-definite Optimization

14 September 2015

Srinadh Bhojanapalli

Anastasios Kyrillidis

Sujay Sanghavi

Papers citing "Dropping Convexity for Faster Semi-definite Optimization"

50 / 88 papers shown

Title
Preconditioned Gradient Descent for Over-Parameterized Nonconvex Matrix Factorization G. Zhang S. Fattahi Richard Y. Zhang 45 36 0 13 Apr 2025
When Can We Solve the Weighted Low Rank Approximation Problem in Truly Subquadratic Time? Chenyang Li Yingyu Liang Zhenmei Shi Zhao-quan Song 36 3 0 24 Feb 2025
Implicit Bias in Matrix Factorization and its Explicit Realization in a New Architecture Yikun Hou Suvrit Sra A. Yurtsever 34 0 0 28 Jan 2025
LASE: Learned Adjacency Spectral Embeddings Sofía Pérez Casulo Marcelo Fiori Federico Larroca Gonzalo Mateos AI4TS GNN 38 0 0 23 Dec 2024
Provable Acceleration of Nesterov's Accelerated Gradient for Rectangular Matrix Factorization and Linear Neural Networks Zhenghao Xu Yuqing Wang T. Zhao Rachel Ward Molei Tao 29 0 0 12 Oct 2024
Low-Tubal-Rank Tensor Recovery via Factorized Gradient Descent Zhiyu Liu Zhi Han Yandong Tang Xi-Le Zhao Yao Wang 50 1 0 22 Jan 2024
How Over-Parameterization Slows Down Gradient Descent in Matrix Sensing: The Curses of Symmetry and Initialization Nuoya Xiong Lijun Ding Simon S. Du 35 11 0 03 Oct 2023
Conic Descent Redux for Memory-Efficient Optimization Bingcong Li G. Giannakis 21 0 0 13 Aug 2023
Gradient-Based Spectral Embeddings of Random Dot Product Graphs Marcelo Fiori Bernardo Marenco Federico Larroca P. Bermolen Gonzalo Mateos BDL 27 3 0 25 Jul 2023
Convergence of Alternating Gradient Descent for Matrix Factorization R. Ward T. Kolda 22 6 0 11 May 2023
Symphony in the Latent Space: Provably Integrating High-dimensional Techniques with Non-linear Machine Learning Models Qiong Wu Jian Li Zhenming Liu Yanhua Li Mihai Cucuringu 34 4 0 01 Dec 2022
Nonconvex Matrix Factorization is Geodesically Convex: Global Landscape Analysis for Fixed-rank Matrix Optimization From a Riemannian Perspective Yuetian Luo Nicolas García Trillos 24 6 0 29 Sep 2022
Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix Completion G. Zhang Hong-Ming Chiu Richard Y. Zhang 24 10 0 24 Aug 2022
Low-Rank Mirror-Prox for Nonsmooth and Low-Rank Matrix Optimization Problems Dan Garber Atara Kaplan 21 0 0 23 Jun 2022
Preconditioned Gradient Descent for Overparameterized Nonconvex Burer--Monteiro Factorization with Global Optimality Certification G. Zhang S. Fattahi Richard Y. Zhang 50 23 0 07 Jun 2022
Local Stochastic Factored Gradient Descent for Distributed Quantum State Tomography J. Kim Taha Toghani César A. Uribe Anastasios Kyrillidis 32 3 0 22 Mar 2022
Noisy Low-rank Matrix Optimization: Geometry of Local Minima and Convergence Rate Ziye Ma Somayeh Sojoudi 38 6 0 08 Mar 2022
Local Linear Convergence of Gradient Methods for Subspace Optimization via Strict Complementarity Dan Garber Ron Fisher 23 1 0 08 Feb 2022
Low-Rank Extragradient Method for Nonsmooth and Low-Rank Matrix Optimization Problems Dan Garber Atara Kaplan 21 5 0 08 Feb 2022
On Geometric Connections of Embedded and Quotient Geometries in Riemannian Fixed-rank Matrix Optimization Yuetian Luo Xudong Li Xinmiao Zhang 19 6 0 23 Oct 2021
Factorization Approach for Low-complexity Matrix Completion Problems: Exponential Number of Spurious Solutions and Failure of Gradient Methods Baturalp Yalcin Haixiang Zhang Javad Lavaei Somayeh Sojoudi 23 13 0 19 Oct 2021
Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect Yuqing Wang Minshuo Chen T. Zhao Molei Tao AI4CE 57 40 0 07 Oct 2021
Nonconvex Factorization and Manifold Formulations are Almost Equivalent in Low-rank Matrix Optimization Yuetian Luo Xudong Li Anru R. Zhang 30 9 0 03 Aug 2021
Global Convergence of Gradient Descent for Asymmetric Low-Rank Matrix Factorization Tian-Chun Ye S. Du 21 46 0 27 Jun 2021
Momentum-inspired Low-Rank Coordinate Descent for Diagonally Constrained SDPs J. Kim Jose Antonio Lara Benitez Taha Toghani Cameron R. Wolfe Zhiwei Zhang Anastasios Kyrillidis 8 0 0 16 Jun 2021
Sharp Global Guarantees for Nonconvex Low-rank Recovery in the Noisy Overparameterized Regime Richard Y. Zhang 44 25 0 21 Apr 2021
Fast quantum state reconstruction via accelerated non-convex programming J. Kim G. Kollias A. Kalev K. X. Wei Anastasios Kyrillidis 25 14 0 14 Apr 2021
Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization Tianyi Liu Yan Li S. Wei Enlu Zhou T. Zhao 21 13 0 24 Feb 2021
MARINA: Faster Non-Convex Distributed Learning with Compression Eduard A. Gorbunov Konstantin Burlachenko Zhize Li Peter Richtárik 39 109 0 15 Feb 2021
On the computational and statistical complexity of over-parameterized matrix sensing Jiacheng Zhuo Jeongyeol Kwon Nhat Ho C. Caramanis 24 28 0 27 Jan 2021
Beyond Procrustes: Balancing-Free Gradient Descent for Asymmetric Low-Rank Matrix Sensing Cong Ma Yuanxin Li Yuejie Chi 18 3 0 13 Jan 2021
On Stochastic Variance Reduced Gradient Method for Semidefinite Optimization Jinshan Zeng Yixuan Zha Ke Ma Yuan Yao 9 0 0 01 Jan 2021
Rank-One Measurements of Low-Rank PSD Matrices Have Small Feasible Sets T. Roddenberry Santiago Segarra Anastasios Kyrillidis 21 0 0 17 Dec 2020
Recent Theoretical Advances in Non-Convex Optimization Marina Danilova Pavel Dvurechensky Alexander Gasnikov Eduard A. Gorbunov Sergey Guminov Dmitry Kamzolov Innokentiy Shibaev 31 76 0 11 Dec 2020
A Nonconvex Framework for Structured Dynamic Covariance Recovery Katherine Tsai Mladen Kolar Oluwasanmi Koyejo 19 3 0 11 Nov 2020
Differentiable Programming for Hyperspectral Unmixing using a Physics-based Dispersion Model J. Janiczek Parth Thaker Gautam Dasarathy C. Edwards P. Christensen Suren Jayasuriya 21 3 0 12 Jul 2020
How Many Samples is a Good Initial Point Worth in Low-rank Matrix Recovery? G. Zhang Richard Y. Zhang 12 16 0 12 Jun 2020
Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent Tian Tong Cong Ma Yuejie Chi 27 113 0 18 May 2020
On the Sample Complexity and Optimization Landscape for Quadratic Feasibility Problems Parth Thaker Gautam Dasarathy Angelia Nedić 21 5 0 04 Feb 2020
On the Convergence of Stochastic Gradient Descent with Low-Rank Projections for Convex Low-Rank Matrix Problems Dan Garber 9 4 0 31 Jan 2020
Efficient Low-Rank Semidefinite Programming with Robust Loss Functions Quanming Yao Hansi Yang En-Liang Hu James T. Kwok 28 2 0 12 May 2019
On the Convergence of Projected-Gradient Methods with Low-Rank Projections for Smooth Convex Minimization over Trace-Norm Balls and Related Problems Dan Garber 14 15 0 05 Feb 2019
Sharp Restricted Isometry Bounds for the Inexistence of Spurious Local Minima in Nonconvex Matrix Recovery Richard Y. Zhang Somayeh Sojoudi Javad Lavaei 11 51 0 07 Jan 2019
A biconvex optimization for solving semidefinite programs via bilinear factorization En-Liang Hu 9 0 0 03 Nov 2018
Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview Yuejie Chi Yue M. Lu Yuxin Chen 31 416 0 25 Sep 2018
Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes C. J. Li Zhaoran Wang Han Liu DiffM 23 18 0 29 Aug 2018
Implicit regularization and solution uniqueness in over-parameterized matrix sensing Kelly Geyer Anastasios Kyrillidis A. Kalev 19 4 0 06 Jun 2018
Provably convergent acceleration in factored gradient descent with applications in matrix sensing Tayo Ajayi David Mildebrath Anastasios Kyrillidis Shashanka Ubaru Georgios Kollias K. Bouchard 13 1 0 01 Jun 2018
$Simple and practical algorithms for $\ell_p$-norm low-rank approximation$ Simple and practical algorithms for $\ell_p$ -norm low-rank approximation Anastasios Kyrillidis 10 5 0 24 May 2018
Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow Xiao Zhang S. Du Quanquan Gu 26 24 0 03 Mar 2018