Efficient Multi-Stage Conjugate Gradient for Trust Region Step
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Summary
An efficient Multi-Stage Conjugate Gradient algorithm to compute the trust region step in a multi-stage manner and can generate a solution in any prescribed precision controlled by a tolerance parameter which is the only parameter the authors need.
- Type
- article
- Published
- 2012-07-22
- Cited by
- 1
- References
- 31
- Access
- Open access
- OpenAlex
- https://openalex.org/W7439820
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:11188680
Keywords
Conjugate gradient method, Gradient descent, Trust region, Convergence (economics), Derivation of the conjugate gradient method
References
- SLEP: Sparse Learning with Efficient Projections
- A Comparison of Optimization Methods and Software for Large-scale L1-regularized Linear Classification
- Trust Region Newton Method for Logistic Regression
- Multi-Task Feature Learning Via Efficient l2, 1-Norm Minimization
- A Subspace Minimization Method for the Trust-Region Step
- Minimizing a Quadratic Over a Sphere
- Efficient projections onto the l1-ball for learning in high dimensions
- Newton's Method for Large Bound-Constrained Optimization Problems
- A semidefinite framework for trust region subproblems with applications to large scale minimization
- The Conjugate Gradient Method and Trust Regions in Large Scale Optimization
- Efficient Methods for Overlapping Group Lasso
- Sparse Reconstruction by Separable Approximation
- The trust region subproblem and semidefinite programming
- A New Matrix-Free Algorithm for the Large-Scale Trust-Region Subproblem
- Quadratically constrained least squares and quadratic problems
- Minimizing quadratic functions with separable quadratic constraints
- Solving the Trust-Region Subproblem using the Lanczos Method
- Efficient Euclidean projections via Piecewise Root Finding and its application in gradient projection
- Computing a Trust Region Step
- Optimization with Sparsity-Inducing Penalties
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