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SIAM REVIEW, ISSN 0036-1445, 12/2019, Volume 61, Issue 4, pp. 839 - 859
Semidefinite programs (SDPs)-some of the most useful and versatile optimization problems of the last few decades-are often pathological: the optimal values of... 
semidefinite programming | MATHEMATICS, APPLIED | closedness of the linear image of the semidefinite cone | pathological semidefinite programs | duality gap | CONES | EXACT DUALITY-THEORY | duality | CLOSEDNESS
Journal Article
IEEE Transactions on Signal Processing, ISSN 1053-587X, 03/2011, Volume 59, Issue 3, pp. 1202 - 1216
Journal Article
Linear Algebra and Its Applications, ISSN 0024-3795, 2010, Volume 433, Issue 2, pp. 401 - 411
The zero forcing number , which is the minimum number of vertices in a zero forcing set of a graph , is used to study the maximum nullity/minimum rank of the... 
Ordered set number | Positive semidefinite minimum rank | Minimum rank | Positive semidefinite maximum nullity | Maximum nullity | Zero forcing number | Positive semidefinite zero forcing number | MATHEMATICS | MATHEMATICS, APPLIED | NULLITY | POSITIVE SEMIDEFINITE MATRICES | SYMMETRIC-MATRICES
Journal Article
SIAM Journal on Matrix Analysis and Applications, ISSN 0895-4798, 2009, Volume 31, Issue 3, pp. 1235 - 1256
Journal Article
Journal Article
IEEE Transactions on Wireless Communications, ISSN 1536-1276, 05/2011, Volume 10, Issue 5, pp. 1389 - 1395
Journal Article
SIAM JOURNAL ON OPTIMIZATION, ISSN 1052-6234, 2019, Volume 29, Issue 2, pp. 1211 - 1239
Plain vanilla K-means clustering has proven to be successful in practice, yet it suffers from outlier sensitivity and may produce highly unbalanced clusters.... 
semidefinite programming | MATHEMATICS, APPLIED | optimality guarantee | outlier detection | K-means clustering | NP-HARDNESS | SEMIDEFINITE
Journal Article
Journal of Guidance, Control, and Dynamics, ISSN 0731-5090, 07/2010, Volume 33, Issue 4, pp. 1161 - 1171
To increase the science return of future missions to Mars and to enable sample return missions, the accuracy with which a lander can be delivered to the... 
INSTRUMENTS & INSTRUMENTATION | ENGINEERING, AEROSPACE | SEMIDEFINITE | Mars landing | Optimization | Convexity
Journal Article
IEEE Transactions on Control of Network Systems, ISSN 2325-5870, 03/2014, Volume 1, Issue 1, pp. 15 - 27
This tutorial summarizes recent advances in the convex relaxation of the optimal power flow (OPF) problem, focusing on structural properties rather than... 
second-order cone program (SOCP) | Biological system modeling | optimal power flow | quadratically constrained quadratic program (QCQP) | Control systems | Vectors | Generators | Equations | semidefinite relaxation | Convex relaxation | Cost function | power systems | semidefinite program (SDP) | Mathematical model
Journal Article
SIAM Journal on Optimization, ISSN 1052-6234, 2018, Volume 28, Issue 4, pp. 3105 - 3126
In this paper, we show that the popular K-means clustering problem can equivalently be reformulated as a conic program of polynomial size. The arising convex... 
K-means | Semidefinite programming | Copositive programming | Conic programming | semidefinite programming | MATHEMATICS, APPLIED | copositive programming | conic programming | NONNEGATIVE MATRIX FACTORIZATION | SEMIDEFINITE
Journal Article
IEEE Transactions on Signal Processing, ISSN 1053-587X, 09/2013, Volume 61, Issue 17, pp. 4230 - 4245
Journal Article
Electric Power Systems Research, ISSN 0378-7796, 11/2018, Volume 164, p. 178
The paper presents a solution to economic dispatch (ED) problems with non-convex, non-smooth fuel cost functions, which characterize practical generating... 
Semidefinite programming | Cost function | Mathematical programming
Journal Article
Proceedings of the forty-seventh annual ACM symposium on theory of computing, ISSN 0737-8017, 06/2015, Volume 14-17-, pp. 567 - 576
We introduce a method for proving lower bounds on the efficacy of semidefinite programming (SDP) relaxations for combinatorial problems. In particular, we show... 
semidefinite programming | polynomial optimization | sum-of-squares method | lower bounds on positive-semidefinite rank | approximation complexity | quantum learning | Sum-of-squares method | Approximation complexity | Quantum learning | Semidefinite programming | Polynomial optimization | Lower bounds on positive-semidefinite rank
Conference Proceeding
Mathematical Programming, ISSN 0025-5610, 2015, Volume 150, Issue 2, pp. 423 - 457
Journal Article
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