Solving Euclidean Distance Matrix Completion Problems Via Semidefinite Programming
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
Exploiting Sparsity in Semidefinite Programming via Matrix Completion I: General Framework
SIAM Journal on Optimization
Semidefinite programming for ad hoc wireless sensor network localization
Proceedings of the 3rd international symposium on Information processing in sensor networks
SIAM Journal on Optimization
Semidefinite programming based algorithms for sensor network localization
ACM Transactions on Sensor Networks (TOSN)
Second-Order Cone Programming Relaxation of Sensor Network Localization
SIAM Journal on Optimization
SpaseLoc: An Adaptive Subproblem Algorithm for Scalable Wireless Sensor Network Localization
SIAM Journal on Optimization
SIAM Journal on Scientific Computing
Further Relaxations of the Semidefinite Programming Approach to Sensor Network Localization
SIAM Journal on Optimization
Sum of squares method for sensor network localization
Computational Optimization and Applications
Exploiting Sparsity in SDP Relaxation for Sensor Network Localization
SIAM Journal on Optimization
An SDP-Based Divide-and-Conquer Algorithm for Large-Scale Noisy Anchor-Free Graph Realization
SIAM Journal on Scientific Computing
Mathematical Programming: Series A and B - Special Issue on Cone Programming and its Applications
(Robust) Edge-based semidefinite programming relaxation of sensor network localization
Mathematical Programming: Series A and B
ACM Transactions on Mathematical Software (TOMS)
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SFSDP is a Matlab package for solving sensor network localization (SNL) problems. These types of problems arise in monitoring and controlling applications using wireless sensor networks. SFSDP implements the semidefinite programming (SDP) relaxation proposed in Kim et al. [2009] for sensor network localization problems, as a sparse version of the full semidefinite programming relaxation (FSDP) by Biswas and Ye [2004]. To improve the efficiency of FSDP, SFSDP exploits the aggregated and correlative sparsity of a sensor network localization problem. As a result, SFSDP can handle much larger problems than other software as well as three-dimensional anchor-free problems. SFSDP analyzes the input data of a sensor network localization problem, solves the problem, and displays the computed locations of sensors. SFSDP also includes the features of generating test problems for numerical experiments.