On the complexity of approximating a KKT point of quadratic programming
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Publication:1380927
DOI10.1007/BF01581726zbMath0894.90117OpenAlexW2063676335MaRDI QIDQ1380927
Publication date: 7 September 1998
Published in: Mathematical Programming. Series A. Series B (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/bf01581726
quadratic programminglocal minimizerfully polynomial-time approximation schemeKKT pointpotential reduction algorithm
Abstract computational complexity for mathematical programming problems (90C60) Quadratic programming (90C20)
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Cites Work
- A continuous approach to inductive inference
- A new polynomial-time algorithm for linear programming
- Constrained global optimization: algorithms and applications
- Checking local optimality in constrained quadratic programming is NP- hard
- How easy is local search?
- On affine scaling algorithms for nonconvex quadratic programming
- Complexity of uniqueness and local search in quadratic 0-1 programming
- Combining binary search and Newton's method to compute real roots for a class of real functions
- A semidefinite framework for trust region subproblems with applications to large scale minimization
- Introduction to global optimization
- The complexity of approximating a nonlinear program
- Complementary pivot theory of mathematical programming
- Some NP-complete problems in quadratic and nonlinear programming
- An Algorithm for Least-Squares Estimation of Nonlinear Parameters
- Computing Optimal Locally Constrained Steps
- Newton’s Method with a Model Trust Region Modification
- A Fully Polynomial-Time Approximation Algorithm for Computing a Stationary Point of the General Linear Complementarity Problem
- A method for the solution of certain non-linear problems in least squares
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