Finding second-order stationary points in constrained minimization: a feasible direction approach
From MaRDI portal
Publication:2194125
DOI10.1007/s10957-020-01713-xzbMath1450.90034OpenAlexW3042090410MaRDI QIDQ2194125
Publication date: 25 August 2020
Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10957-020-01713-x
constrained optimizationsecond-order methodsfeasible direction methodssecond-order necessary optimality conditions
Numerical mathematical programming methods (65K05) Nonconvex programming, global optimization (90C26) Nonlinear programming (90C30) Optimality conditions and duality in mathematical programming (90C46) Sensitivity, stability, parametric optimization (90C31)
Related Items
Proximal methods avoid active strict saddles of weakly convex functions, Escaping Strict Saddle Points of the Moreau Envelope in Nonsmooth Optimization, Complexity analysis of interior-point methods for second-order stationary points of nonlinear semidefinite optimization problems, The regularized feasible directions method for nonconvex optimization
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Trust-region problems with linear inequality constraints: exact SDP relaxation, global optimality and robust optimization
- Adaptive cubic regularisation methods for unconstrained optimization. I: Motivation, convergence and numerical results
- Extended trust-region problems with one or two balls: exact copositive and Lagrangian relaxations
- Introduction to sensitivity and stability analysis in nonlinear programming
- Global optimization using interval analysis: The one-dimensional case
- Approximation algorithms for quadratic programming
- A computational study of global optimization solvers on two trust region subproblems
- A branch and bound algorithm for nonconvex quadratic optimization with ball and linear constraints
- Second-order optimality and beyond: characterization and evaluation complexity in convexly constrained nonlinear optimization
- The landscape of empirical risk for nonconvex losses
- Recent advances in trust region algorithms
- Cubic regularization of Newton method and its global performance
- The trust region subproblem with non-intersecting linear constraints
- Convergence to Second Order Stationary Points in Inequality Constrained Optimization
- A Note on Polynomial Solvability of the CDT Problem
- Solving Generalized CDT Problems via Two-Parameter Eigenvalues
- Introduction to Nonlinear Optimization
- Computing Points that Satisfy Second Order Necessary Optimality Conditions for Unconstrained Minimization
- Newton Methods For Large-Scale Linear Inequality-Constrained Minimization
- Newton-type methods for unconstrained and linearly constrained optimization
- Optimization algorithms and point-to-set-maps
- On the use of directions of negative curvature in a modified newton method
- Trust Region Methods
- Exploiting negative curvature directions in linesearch methods for unconstrained optimization
- First-Order Methods in Optimization
- An adaptive cubic regularization algorithm for nonconvex optimization with convex constraints and its function-evaluation complexity
- Second-Order-Cone Constraints for Extended Trust-Region Subproblems
- A Second-Order Cone Based Approach for Solving the Trust-Region Subproblem and Its Variants
- Polynomial Solvability of Variants of the Trust-Region Subproblem
- Convergence to Second-Order Stationary Points of a Primal-Dual Algorithm Model for Nonlinear Programming
- Nonlinear Programming
- Point-to-Set Maps in Mathematical Programming
