SNLSDP version 0 -- a MATLAB software for sensor network localization It implemented an SDP based approach with regularization for solving sensor network localization problems. The algorithm first solves an SDP relaxation (with regularization) of the non-convex minimization problem (1), and use the SDP computed solution as the starting point for a gradient descent method with backtracking line search to solve the smooth unconstrained problem (2). This software package is designed for solving small size senor network localization problems with up to 200 sensors and a few thousands given distances. (Source:

References in zbMATH (referenced in 30 articles )

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  1. Fang, Ethan X.; Liu, Han; Toh, Kim-Chuan; Zhou, Wen-Xin: Max-norm optimization for robust matrix recovery (2018)
  2. D’Ambrosio, Claudia; Vu, Ky; Lavor, Carlile; Liberti, Leo; Maculan, Nelson: New error measures and methods for realizing protein graphs from distance data (2017)
  3. Ding, Chao; Qi, Hou-Duo: Convex optimization learning of faithful Euclidean distance representations in nonlinear dimensionality reduction (2017)
  4. Drusvyatskiy, D.; Krislock, N.; Voronin, Yuen-Lam; Wolkowicz, H.: Noisy Euclidean distance realization: robust facial reduction and the Pareto frontier (2017)
  5. Lai, Rongjie; Li, Jia: Solving partial differential equations on manifolds from incomplete interpoint distance (2017)
  6. Luke, D.Russell; Sabach, Shoham; Teboulle, Marc; Zatlawey, Kobi: A simple globally convergent algorithm for the nonsmooth nonconvex single source localization problem (2017)
  7. Hu, Yaohua; Li, Chong; Yang, Xiaoqi: On convergence rates of linearized proximal algorithms for convex composite optimization with applications (2016)
  8. Chaudhury, K.N.; Khoo, Y.; Singer, A.: Global registration of multiple point clouds using semidefinite programming (2015)
  9. Cheong, Seunggyun; Manchester, Ian R.: Input design for discrimination between classes of LTI models (2015)
  10. Qi, Hou-Duo; Yuan, Xiaoming: Computing the nearest Euclidean distance matrix with low embedding dimensions (2014)
  11. Wu, Changzhi; Li, Chaojie; Long, Qiang: A DC programming approach for sensor network localization with uncertainties in anchor positions (2014)
  12. Cucuringu, Mihai: ASAP: an eigenvector synchronization algorithm for the graph realization problem (2013)
  13. Fang, Xingyuan; Toh, Kim-Chuan: Using a distributed SDP approach to solve simulated protein molecular conformation problems (2013)
  14. Jia, Jie; Zhang, Guiyuan; Wang, Xingwei; Chen, Jian: On distributed localization for road sensor networks: a game theoretic approach (2013)
  15. Manjarres, Diana; Del Ser, Javier; Gil-Lopez, Sergio; Vecchio, Massimo; Landa-Torres, Itziar; Lopez-Valcarce, Roberto: A novel heuristic approach for distance- and connectivity-based multihop node localization in wireless sensor networks (2013) ioport
  16. Lavor, Carlile; Liberti, Leo; Maculan, Nelson; Mucherino, Antonio: The discretizable molecular distance geometry problem (2012)
  17. Pong, Ting Kei: Edge-based semidefinite programming relaxation of sensor network localization with lower bound constraints (2012)
  18. Alfakih, Abdo Y.; Anjos, Miguel F.; Piccialli, Veronica; Wolkowicz, Henry: Euclidean distance matrices, semidefinite programming and sensor network localization (2011)
  19. Pong, Ting Kei; Tseng, Paul: (Robust) edge-based semidefinite programming relaxation of sensor network localization (2011)
  20. Singer, A.: Angular synchronization by eigenvectors and semidefinite programming (2011)

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