A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements

Qinqing Zheng, John Lafferty

Oct-2-2025, 03:22:39 GMT–Neural Information Processing Systems

We propose a simple, scalable, and fast gradient descent algorithm to optimize a nonconvex objective for the rank minimization problem and a closely related family of semidefinite programs.

algorithm, minimization, optimization, (12 more...)

Neural Information Processing Systems

Oct-2-2025, 03:22:39 GMT

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Country:
- Asia > Middle East
  - Jordan (0.04)
- North America > United States
  - Illinois > Cook County > Chicago (0.04)

Industry:
- Education (0.46)

Technology:
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Gradient Descent (0.87)

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A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements
A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements John Lafferty University of Chicago

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