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Convex optimization
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391	Potential and Challenges of Two-VariablePer-Inequality Sub-Polyhedral Compilation Ramakrishna Upadrasta Albert Cohen INRIA and LRI, Paris-Sud 11 University Add to Reading List Source URL: perso.ens-lyon.fr Language: English - Date: 2011-03-29 07:21:24 Operations research Computational complexity theory Convex optimization Linear programming Polyhedron Mathematical optimization Approximation algorithm Simplex algorithm Algorithm Theoretical computer science Applied mathematics Mathematics
392	Solving online feasibility problem in constant amortized time per update Lilian Buzer∗† global shape of the pixels by minimizing an approximation error. But, using digital primitives we can build an exact approximati Add to Reading List Source URL: www.cccg.ca Language: English - Date: 2005-07-31 13:55:46 Applied mathematics Convex optimization Linear programming Constraint Candidate solution Algorithm Linear programming relaxation LP-type problem Mathematical optimization Operations research Mathematics
393	Computational Experience with Generic Decomposition using the DIP Framework M.V. Galati∗ T.K. Ralphs† Add to Reading List Source URL: coral.ie.lehigh.edu Language: English - Date: 2012-09-15 17:55:19 Relaxation Linear programming Decomposition method Dantzig–Wolfe decomposition Cutting-plane method Lagrangian relaxation Mathematical optimization Operations research Convex optimization
394	Conic geometric optimisation on the manifold of positive definite matrices Suvrit Sra∗ Reshad Hosseini† Add to Reading List Source URL: suvrit.de Language: English - Date: 2014-07-29 23:24:31 Linear algebra Operator theory Convex analysis Inequalities Convex function Convex optimization Tangent space Cauchy–Schwarz inequality Hilbert space Algebra Mathematics Mathematical analysis
395	Convex Modeling with Priors by Benjamin Recht B.S., University of ChicagoM.S., Massachusetts Institute of Technology (2002) Add to Reading List Source URL: www.cba.mit.edu Language: English - Date: 2011-12-13 18:32:07 Mathematical optimization Regression analysis Image processing Nonlinear dimensionality reduction Supervised learning Neil Gershenfeld Segmentation Cluster analysis Spectral clustering Statistics Machine learning Multivariate statistics
396	Top-k Aggregation Using Intersections of Ranked Inputs Ravi Kumar Kunal Punera Torsten Suel Add to Reading List Source URL: www.wsdm2009.org Language: English - Date: 2009-04-14 09:42:15 Operations research Mathematical optimization Search algorithms Theoretical computer science Convex optimization Algorithm Nearest neighbor search Linear programming Approximation algorithm Mathematics Information science Applied mathematics
397	Modelling Transit Paths for Military Vehicles Add to Reading List Source URL: www.mssanz.org.au Language: English - Date: 2013-01-15 17:46:12 Operations research Convex optimization Optimal control Equations Lagrangian relaxation Dynamic programming Shortest path problem Linear programming Optimization problem Mathematics Mathematical optimization Applied mathematics
398	Quality Guarantees on k-Optimal Solutions for Distributed Constraint Optimization Problems Jonathan P. Pearce and Milind Tambe∗ University of Southern California Computer Science Department {, tambe@usc Add to Reading List Source URL: teamcore.usc.edu Language: English - Date: 2008-10-14 04:00:37 Applied mathematics Operations research Distributed constraint optimization Convex optimization Computational complexity theory Constraint satisfaction problem Linear programming Constraint optimization Combinatorial optimization Constraint programming Mathematical optimization Theoretical computer science
399	Journal of Machine Learning Research–57 Submitted 11/08; Revised 11/09; Published -/10 A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning Add to Reading List Source URL: www.stat.purdue.edu Language: English - Date: 2010-03-07 22:04:30 Limited-memory BFGS BFGS method Quasi-Newton method Gradient descent Orthant-wise limited-memory quasi-Newton Wolfe conditions Convex optimization Hessian matrix Subderivative Numerical analysis Mathematical analysis Mathematical optimization
400	Numerical Example for Fundamental Theorem of Linear Programming T Given a feasible solution · x = (1, 2,¸0, 1) µ(i.e.¶ Ax = b, x ≥ 0), with p = 3 pos entries Add to Reading List Source URL: web.hku.hk Language: English - Date: 2008-08-27 04:46:42 Linear algebra Convex optimization Linear programming Vector space Line Linear differential equation Algebra Mathematics Operations research

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