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Convex approximations to sparse PCA via Lagrangian duality
Luss, Ronny; Teboulle, Marc - In: Operations research letters 39 (2011) 1, pp. 57-61
Persistent link: https://www.econbiz.de/10008857505
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A new semidefinite programming relaxation scheme for a class of quadratic matrix problems
Beck, Amir; Drori, Yoel; Teboulle, Marc - In: Operations research letters 40 (2012) 4, pp. 298-302
Persistent link: https://www.econbiz.de/10009558921
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A simple algorithm for a class of nonsmooth convex-concave saddle-point problems
Drori, Yoel; Sabach, Shoham; Teboulle, Marc - In: Operations research letters 43 (2015) 2, pp. 209-214
Persistent link: https://www.econbiz.de/10010515775
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A fast dual proximal gradient algorithm for convex minimization and applications
Beck, Amir; Teboulle, Marc - In: Operations research letters 42 (2014) 1, pp. 1-6
Persistent link: https://www.econbiz.de/10010259285
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A non-Euclidean gradient descent method with sketching for unconstrained matrix minimization
Hallak, Nadav; Teboulle, Marc - In: Operations research letters 47 (2019) 5, pp. 421-426
Persistent link: https://www.econbiz.de/10012110606
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A probabilistic result for the max-cut problem on random graphs
Beck, Amir; Teboulle, Marc - In: Operations research letters 27 (2000) 5, pp. 209-214
Persistent link: https://www.econbiz.de/10006835382
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Convex approximations to sparse PCA via Lagrangian duality
Luss, Ronny; Teboulle, Marc - In: Operations research letters 39 (2011) 1, pp. 57-62
Persistent link: https://www.econbiz.de/10008780824
Cover Image
A new semidefinite programming relaxation scheme for a class of quadratic matrix problems
Beck, Amir; Drori, Yoel; Teboulle, Marc - In: Operations research letters 40 (2012) 4, pp. 298-303
Persistent link: https://www.econbiz.de/10009977025
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Mirror descent and nonlinear projected subgradient methods for convex optimization
Beck, Amir; Teboulle, Marc - In: Operations research letters 31 (2003) 3, pp. 167-175
Persistent link: https://www.econbiz.de/10006823399