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Global Convergence of an Augmented Lagrangian Method for Nonlinear Programming via Riemannian Optimization

Roberto Andreani et al.

SIAM Journal on Optimization2026https://doi.org/10.1137/24m1692319article
AJG 3
Weight
0.37

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https://doi.org/https://doi.org/10.1137/24m1692319

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@article{roberto2026,
  title        = {{Global Convergence of an Augmented Lagrangian Method for Nonlinear Programming via Riemannian Optimization}},
  author       = {Roberto Andreani et al.},
  journal      = {SIAM Journal on Optimization},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1137/24m1692319},
}

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Global Convergence of an Augmented Lagrangian Method for Nonlinear Programming via Riemannian Optimization

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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