Algorithm 920: SFSDP: A Sparse Version of Full Semidefinite Programming Relaxation for Sensor Network Localization Problems 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

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  1. Burachik, Regina S.; Kaya, C. Yalçın: Steklov convexification and a trajectory method for global optimization of multivariate quartic polynomials (2021)
  2. Zhou, Shenglong; Xiu, Naihua; Qi, Hou-Duo: Robust Euclidean embedding via EDM optimization (2020)
  3. Chieu, N. H.; Jeyakumar, V.; Li, G.: A convergent hierarchy of SDP relaxations for a class of hard robust global polynomial optimization problems (2017)
  4. Drusvyatskiy, D.; Krislock, N.; Voronin, Yuen-Lam; Wolkowicz, H.: Noisy Euclidean distance realization: robust facial reduction and the Pareto frontier (2017)
  5. Long, Qiang; Wu, Changzhi; Wang, Xiangyu; Wu, Zhiyou: A modified quasisecant method for global optimization (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. Jeyakumar, V.; Kim, S.; Lee, G. M.; Li, G.: Semidefinite programming relaxation methods for global optimization problems with sparse polynomials and unbounded semialgebraic feasible sets (2016)
  8. Jeyakumar, V.; Lasserre, J. B.; Li, G.; Phạm, T. S.: Convergent semidefinite programming relaxations for global bilevel polynomial optimization problems (2016)
  9. Qi, Hou-Duo; Yuan, Xiaoming: Computing the nearest Euclidean distance matrix with low embedding dimensions (2014)
  10. Wu, Changzhi; Li, Chaojie; Long, Qiang: A DC programming approach for sensor network localization with uncertainties in anchor positions (2014)
  11. Zhou, Xiaojun; Gao, David Yang; Yang, Chunhua: Canonical primal-dual algorithm for solving fourth-order polynomial minimization problems (2014)
  12. Bezdek, Károly; Deza, Antoine; Ye, Yinyu: Selected open problems in discrete geometry and optimization (2013)
  13. Kim, Sunyoung; Kojima, Masakazu: A continuation method for large-sized sensor network localization problems (2013)
  14. Kojima, Masakazu; Yamashita, Makoto: Enclosing ellipsoids and elliptic cylinders of semialgebraic sets and their application to error bounds in polynomial optimization (2013)
  15. Malick, Jérôme; Roupin, Frédéric: On the bridge between combinatorial optimization and nonlinear optimization: a family of semidefinite bounds for 0--1 quadratic problems leading to quasi-Newton methods (2013)
  16. Gouveia, João; Pong, Ting Kei: Comparing SOS and SDP relaxations of sensor network localization (2012)
  17. Kim, Sunyoung; Kojima, Masakazu: Exploiting sparsity in SDP relaxation of polynomial optimization problems (2012)
  18. Kim, Sunyoung; Kojima, Masakazu; Waki, Hayato; Yamashita, Makato: Algorithm 920: SFSDP: a sparse version of full semidefinite programming relaxation for sensor network localization problems (2012)
  19. Krislock, Nathan; Wolkowicz, Henry: Euclidean distance matrices and applications (2012)
  20. Lakhbab, Halima; El Bernoussi, Souad: A hybrid method based on particle swarm optimization and nonmonotone spectral gradient method for unconstrained optimization problem (2012)

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