On the convergence of projected gradient processes to singular critical points
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Publication:1821692
DOI10.1007/BF00939081zbMath0616.90060OpenAlexW2090243370MaRDI QIDQ1821692
Publication date: 1987
Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/bf00939081
asymptotic stabilityprojected gradient methodsconvex feasible setssingular minimizersconvergence rate theoremsnonconvex objective functions
Nonlinear programming (90C30) Sensitivity, stability, parametric optimization (90C31) Numerical methods based on nonlinear programming (49M37)
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Cites Work
- A class of superlinearly convergent projection algorithms with relaxed stepsizes
- Asymptotic decay rates from the growth properties of Lyapunov functions near singular attractors
- Newton-Goldstein convergence rates for convex constrained minimization problems with singular solutions
- Two-Metric Projection Methods for Constrained Optimization
- Newton’s Method and the Goldstein Step-Length Rule for Constrained Minimization Problems
- On the Goldstein-Levitin-Polyak gradient projection method
- Projected Newton Methods for Optimization Problems with Simple Constraints
- Convex programming in Hilbert space
- On Steepest Descent
