Research map / Learning theory and algorithms / Matrix and tensor methods

Polynomial Optimization Methods for Matrix Factorization

AAAI 2017 · Learning theory and algorithms › Matrix and tensor methods · approach: Nonconvex low-rank and sparse optimization · 0 citations (OpenAlex)

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Closest papers by meaning

• Convergence of Alternating Gradient Descent for Matrix Factorization (NeurIPS 2023 · similarity 0.90)
• Global Convergence of Gradient Descent for Asymmetric Low-Rank Matrix Factorization (NeurIPS 2021 · similarity 0.88)
• Provable Acceleration of Nesterov's Accelerated Gradient for Asymmetric Matrix Factorization and Linear Neural Networks (NeurIPS 2024 · similarity 0.88)
• Towards Resolving the Implicit Bias of Gradient Descent for Matrix Factorization: Greedy Low-Rank Learning (ICLR 2021 · similarity 0.88)
• In-depth Analysis of Low-rank Matrix Factorisation in a Federated Setting (AAAI 2025 · similarity 0.87)
• On the Crucial Role of Initialization for Matrix Factorization (ICLR 2025 · similarity 0.86)