Scalable Low-Rank Semidefinite Programming for Certifiably Correct Machine Perception

DOI10.1007/978-3-030-66723-8_33zbMath1469.68133OpenAlexW3128885578MaRDI QIDQ3382007

David M. Rosen

Publication date: 20 September 2021

Published in: Algorithmic Foundations of Robotics XIV (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-030-66723-8_33

Mathematics Subject Classification ID

Semidefinite programming (90C22) Computer graphics; computational geometry (digital and algorithmic aspects) (68U05) Artificial intelligence for robotics (68T40)

Related Items (2)

Time-Varying Semidefinite Programming: Path Following a Burer–Monteiro Factorization ⋮ An inexact projected gradient method with rounding and lifting by nonlinear programming for solving rank-one semidefinite relaxation of polynomial optimization

Uses Software

• SDPLR
• Ipopt
• g2o
• iSAM2
• SE-Sync

Cites Work

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• On block diagonal and Schur complement preconditioning
• A nonlinear programming algorithm for solving semidefinite programs via low-rank factorization
• A note on probably certifiably correct algorithms
• On LICQ and the uniqueness of Lagrange multipliers
• Local minima and convergence in low-rank semidefinite programming
• On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming
• Low-Rank Optimization on the Cone of Positive Semidefinite Matrices
• Semidefinite optimization
• Variational Analysis
• Trust Region Methods
• Semidefinite Programming

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