| Period | 10 Dec 2015 |
|---|---|
| Held at | University of Edimburgh, United Kingdom |
Keywords
- non-linear optimization
- Complexity theory
Documents & Links
Related content
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Research output
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Optimal Newton-type methods for nonconvex smooth optimization problems
Research output: Book/Report/Journal › Other report
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Worst-case evaluation complexity of non-monotone gradient-related algorithms for unconstrained optimization
Research output: Contribution to journal › Article › peer-review
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Evaluation complexity bounds for smooth constrained nonlinear optimization using scaled KKT conditions and high-order models
Research output: Contribution in Book/Catalog/Report/Conference proceeding › Chapter
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Worst-case evaluation complexity for unconstrained nonlinear optimization using high-order regularized models
Research output: Working paper
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On the oracle complexity of first-order and derivative-free algorithms for smooth nonconvex minimization
Research output: Contribution to journal › Article › peer-review
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On the complexity of steepest descent, Newton's and regularized Newton's methods for nonconvex unconstrained optimization problems
Research output: Contribution to journal › Article › peer-review
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On the evaluation complexity of cubic regularization methods for potentially rank-deficient nonlinear least-squares problems and its relevance to constrained nonlinear optimization
Research output: Contribution to journal › Article › peer-review
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An adaptive cubic regularization algorithm for nonconvex optimization with convex constraints and its function-evaluation complexity
Research output: Contribution to journal › Article › peer-review
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On the evaluation complexity of composite function minimization with applications to nonconvex nonlinear programming
Research output: Contribution to journal › Article › peer-review
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Complexity bounds for second-order optimality in unconstrained optimization
Research output: Contribution to journal › Article › peer-review
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Adaptive cubic regularisation methods for unconstrained optimization. Part II: Worst-case function- and derivative-evaluation complexity
Research output: Contribution to journal › Article › peer-review
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Adaptive cubic regularisation methods for unconstrained optimization. Part I: Motivation, convergence and numerical results
Research output: Contribution to journal › Article › peer-review
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An example of slow convergence for Newton's method on a function with globally Lipschitz continuous Hessian
Research output: Working paper
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On the complexity of finding first-order critical points in constrained nonlinear optimization
Research output: Contribution to journal › Article › peer-review
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On the evaluation complexity of constrained nonlinear least-squares and general constrained nonlinear optimization using second-order methods
Research output: Contribution to journal › Article › peer-review
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A note about the complexity of minimizing Nesterov's smooth Chebyshev-Rosenbrock function
Research output: Contribution to journal › Article › peer-review
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Evaluation complexity for nonlinear constrained optimization using unscaled KKT conditions and high-order models
Research output: Contribution to journal › Article › peer-review
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How much patience do you have? A worst-case perspective on smooth nonconvex optimization
Research output: Contribution to journal › Article › peer-review
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Evaluation complexity of adaptive cubic regularization methods for convex unconstrained optimization
Research output: Contribution to journal › Article › peer-review
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Projects
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ADALGOPT - Advanced algorithms in nonlinear optimization
Project: Research Axis
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Complexity in nonlinear optimization
Project: Research