New Analysis on Sparse Solutions to Random Standard Quadratic Optimization Problems and Extensions
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Publication:3449456
DOI10.1287/moor.2014.0692zbMath1332.90187OpenAlexW2103765204MaRDI QIDQ3449456
Publication date: 4 November 2015
Published in: Mathematics of Operations Research (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1287/moor.2014.0692
random matricessparse solutionsstandard quadratic optimizationprobability analysiscopositive definite matrix
Related Items (5)
On random quadratic forms: supports of potential local maxima ⋮ On sparsity of the solution to a random quadratic optimization problem ⋮ Building a completely positive factorization ⋮ The Complexity of Simple Models—A Study of Worst and Typical Hard Cases for the Standard Quadratic Optimization Problem ⋮ Two-stage stochastic standard quadratic optimization
Cites Work
- Sparse solutions to random standard quadratic optimization problems
- New and old bounds for standard quadratic optimization: dominance, equivalence and incomparability
- A clique algorithm for standard quadratic programming
- Algorithms for determining the copositivity of a given symmetric matrix
- On standard quadratic optimization problems
- Robust principal component analysis?
- Extensions of Lipschitz mappings into a Hilbert space
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
- Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
- Continuous Characterizations of the Maximum Clique Problem
- Compressed sensing
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