UG

UG - Ubiquity Generator framework: UG is a generic framework to parallelize branch-and-bound based solvers (e.g., MIP, MINLP, ExactIP) in a distributed or shared memory computing environment: Exploits powerful performance of state-of-the-art ”base solvers”, such as SCIP, CPLEX, etc. Without the need for base solver parallelization. Base solvers and communication libraries are abstracted within UG. A parallel solver instantiated by UG framework is named: ug[Base solver, Communication libaray]


References in zbMATH (referenced in 16 articles , 1 standard article )

Showing results 1 to 16 of 16.
Sorted by year (citations)

  1. Berthold, Timo: A computational study of primal heuristics inside an MI(NL)P solver (2018)
  2. Kimura, Keiji; Waki, Hayato: Minimization of Akaike’s information criterion in linear regression analysis via mixed integer nonlinear program (2018)
  3. Munguía, Lluís-Miquel; Ahmed, Shabbir; Bader, David A.; Nemhauser, George L.; Shao, Yufen: Alternating criteria search: a parallel large neighborhood search algorithm for mixed integer programs (2018)
  4. Shinano, Yuji: The ubiquity generator framework: 7 years of progress in parallelizing branch-and-bound (2018)
  5. Shinano, Yuji; Berthold, Timo; Heinz, Stefan: ParaXpress: an experimental extension of the FICO Xpress-Optimizer to solve hard MIPs on supercomputers (2018)
  6. Zhou, Kai; Kılınç, Mustafa R.; Chen, Xi; Sahinidis, Nikolaos V.: An efficient strategy for the activation of MIP relaxations in a multicore global MINLP solver (2018)
  7. Berthold, Timo; Farmer, James; Heinz, Stefan; Perregaard, Michael: Parallelization of the FICO Xpress-Optimizer (2016)
  8. Kimura, Keiji; Waki, Hayato: Mixed integer nonlinear program for minimization of Akaike’s information criterion (2016)
  9. Maher, Stephen; Miltenberger, Matthias; Pedroso, João Pedro; Rehfeldt, Daniel; Schwarz, Robert; Serrano, Felipe: PySCIPOpt: mathematical programming in python with the SCIP optimization suite (2016)
  10. Shinano, Yuji; Berthold, Timo; Heinz, Stefan: A first implementation of paraxpress: combining internal and external parallelization to solve MIPs on supercomputers (2016)
  11. Eckstein, Jonathan; Hart, William E.; Phillips, Cynthia A.: PEBBL: an object-oriented framework for scalable parallel branch and bound (2015)
  12. Mason, Luke R.; Mak-Hau, Vicky H.; Ernst, Andreas T.: A parallel optimisation approach for the realisation problem in intensity modulated radiotherapy treatment planning (2015)
  13. Carvajal, R.; Ahmed, S.; Nemhauser, G.; Furman, K.; Goel, V.; Shao, Y.: Using diversification, communication and parallelism to solve mixed-integer linear programs (2014)
  14. Gamrath, Gerald: Improving strong branching by domain propagation (2014)
  15. Koch, Thorsten; Ralphs, Ted; Shinano, Yuji: Could we use a million cores to solve an integer program? (2012)
  16. Koch, Thorsten; Achterberg, Tobias; Andersen, Erling; Bastert, Oliver; Berthold, Timo; Bixby, Robert E.; Danna, Emilie; Gamrath, Gerald; Gleixner, Ambros M.; Heinz, Stefan; Lodi, Andrea; Mittelmann, Hans; Ralphs, Ted; Salvagnin, Domenico; Steffy, Daniel E.; Wolter, Kati: MIPLIB 2010. Mixed integer programming library version 5 (2011) ioport


Further publications can be found at: http://ug.zib.de/#reference