SNLSDP

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: http://plato.asu.edu)


References in zbMATH (referenced in 37 articles )

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  1. Lu, Si-Tong; Zhang, Miao; Li, Qing-Na: Feasibility and a fast algorithm for Euclidean distance matrix optimization with ordinal constraints (2020)
  2. Zhai, Fengzhen; Li, Qingna: A Euclidean distance matrix model for protein molecular conformation (2020)
  3. Aravkin, Aleksandr Y.; Burke, James V.; Drusvyatskiy, Dmitry; Friedlander, Michael P.; Roy, Scott: Level-set methods for convex optimization (2019)
  4. Sun, Chuangchuang; Dai, Ran: An iterative rank penalty method for nonconvex quadratically constrained quadratic programs (2019)
  5. Fang, Ethan X.; Liu, Han; Toh, Kim-Chuan; Zhou, Wen-Xin: Max-norm optimization for robust matrix recovery (2018)
  6. Li, Xudong; Sun, Defeng; Toh, Kim-Chuan: QSDPNAL: a two-phase augmented Lagrangian method for convex quadratic semidefinite programming (2018)
  7. Park, Dohyung; Kyrillidis, Anastasios; Caramanis, Constantine; Sanghavi, Sujay: Finding low-rank solutions via nonconvex matrix factorization, efficiently and provably (2018)
  8. Yang, Lei; Pong, Ting Kei; Chen, Xiaojun: A nonmonotone alternating updating method for a class of matrix factorization problems (2018)
  9. D’Ambrosio, Claudia; Vu, Ky; Lavor, Carlile; Liberti, Leo; Maculan, Nelson: New error measures and methods for realizing protein graphs from distance data (2017)
  10. Ding, Chao; Qi, Hou-Duo: Convex optimization learning of faithful Euclidean distance representations in nonlinear dimensionality reduction (2017)
  11. Drusvyatskiy, D.; Krislock, N.; Voronin, Yuen-Lam; Wolkowicz, H.: Noisy Euclidean distance realization: robust facial reduction and the Pareto frontier (2017)
  12. Lai, Rongjie; Li, Jia: Solving partial differential equations on manifolds from incomplete interpoint distance (2017)
  13. Luke, D. Russell; Sabach, Shoham; Teboulle, Marc; Zatlawey, Kobi: A simple globally convergent algorithm for the nonsmooth nonconvex single source localization problem (2017)
  14. Hu, Yaohua; Li, Chong; Yang, Xiaoqi: On convergence rates of linearized proximal algorithms for convex composite optimization with applications (2016)
  15. Chaudhury, K. N.; Khoo, Y.; Singer, A.: Global registration of multiple point clouds using semidefinite programming (2015)
  16. Cheong, Seunggyun; Manchester, Ian R.: Input design for discrimination between classes of LTI models (2015)
  17. Qi, Hou-Duo; Yuan, Xiaoming: Computing the nearest Euclidean distance matrix with low embedding dimensions (2014)
  18. Wu, Changzhi; Li, Chaojie; Long, Qiang: A DC programming approach for sensor network localization with uncertainties in anchor positions (2014)
  19. Cucuringu, Mihai: ASAP: an eigenvector synchronization algorithm for the graph realization problem (2013)
  20. Fang, Xingyuan; Toh, Kim-Chuan: Using a distributed SDP approach to solve simulated protein molecular conformation problems (2013)

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