Polytechnic University of Hong Kong

Related content

• Research output

• Evaluation complexity for nonlinear constrained optimization using unscaled KKT conditions and high-order models

  Research output: Contribution to journal › Article › peer-review

• How much patience do you have? A worst-case perspective on smooth nonconvex optimization

  Research output: Contribution to journal › Article › peer-review

• 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

• Updating the regularization parameter in the adaptive cubic regularization algorithm

  Research output: Contribution to journal › Article › peer-review

• On the oracle complexity of first-order and derivative-free algorithms for smooth nonconvex minimization

  Research output: Contribution to journal › Article › peer-review

• Erratum: Nonlinear programming without a penalty function or a filter (Mathematical Programming (2010) 122 (155-196)

  Research output: Contribution to journal › Article › peer-review

• Worst-case evaluation complexity for unconstrained nonlinear optimization using high-order regularized models

  Research output: Working paper

• On the evaluation complexity of composite function minimization with applications to nonconvex nonlinear programming

  Research output: Contribution to journal › Article › peer-review

• On the complexity of the steepest-descent with exact linesearches

  Research output: Book/Report/Journal › Other report

• 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

• 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

• Improved worst-case evaluation complexity for potentially rank-deficient nonlinear least-Euclidean-norm problems using higher-order regularized models

  Research output: Working paper

• Adaptive cubic regularisation methods for unconstrained optimization. Part II: Worst-case function- and derivative-evaluation complexity

  Research output: Contribution to journal › Article › peer-review

• Worst-case evaluation complexity of non-monotone gradient-related algorithms for unconstrained optimization

  Research output: Contribution to journal › Article › peer-review

• On the complexity of finding first-order critical points in constrained nonlinear optimization

  Research output: Contribution to journal › Article › peer-review

• Evaluation complexity of adaptive cubic regularization methods for convex unconstrained optimization

  Research output: Contribution to journal › Article › peer-review

• An adaptive cubic regularization algorithm for nonconvex optimization with convex constraints and its function-evaluation complexity

  Research output: Contribution to journal › Article › peer-review

• A note about the complexity of minimizing Nesterov's smooth Chebyshev-Rosenbrock function

  Research output: Contribution to journal › Article › peer-review

• Complexity bounds for second-order optimality in unconstrained optimization

  Research output: Contribution to journal › Article › peer-review

• An example of slow convergence for Newton's method on a function with globally Lipschitz continuous Hessian

  Research output: Working paper

• Projects

• Complexity in nonlinear optimization

  Project: Research

• ADALGOPT - Advanced algorithms in nonlinear optimization

  Project: Research Axis

• Student theses

• A trust-region method for constrained derivative-free optimization and worst-case evaluation complexity of non-monotone gradient-related algorithms for unconstrained optimization.

  Student thesis: Doc types › Doctor of Sciences