Interior-point methods for symmetric optimization based on a class of non-coercive kernel functions
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Publication:5299914
DOI10.1080/10556788.2011.651083zbMath1267.90068OpenAlexW2005083989MaRDI QIDQ5299914
Publication date: 24 June 2013
Published in: Optimization Methods and Software (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/10556788.2011.651083
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- Monotone functions on formally real Jordan algebras
- Convexity and differentiability properties of spectral functions and spectral mappings on Euclidean Jordan algebras
- A polynomial-time algorithm for linear optimization based on a new class of kernel functions
- A new class of polynomial primal-dual methods for linear and semidefinite optimization
- Interior-point methods based on kernel functions for symmetric optimization
- A New Class of Large Neighborhood Path-Following Interior Point Algorithms for Semidefinite Optimization with $O(\sqrt{n}\log\frac{\mathrm{Tr}(X^0S^0)}{\epsilon})$ Iteration Complexity
- A generic primal–dual interior-point method for semidefinite optimization based on a new class of kernel functions
- Self-Scaled Barriers and Interior-Point Methods for Convex Programming
- A New Efficient Large-Update Primal-Dual Interior-Point Method Based on a Finite Barrier
- Conic convex programming and self-dual embedding
- A Comparative Study of Kernel Functions for Primal-Dual Interior-Point Algorithms in Linear Optimization
- An O$(\sqrtn L)$ Iteration Primal-dual Path-following Method, Based on Wide Neighborhoods and Large Updates, for Monotone LCP
- A Jordan-algebraic approach to potential-reduction algorithms
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