The TOMLAB NLPLIB toolbox for nonlinear programming The paper presents the toolbox NLPLIB TB 1.0 (NonLinear Programming LIBrary); a set of Matlab solvers, test problems, graphical and computational utilities for unconstrained and constrained optimization, quadratic programming, unconstrained and constrained nonlinear least squares, boxbounded global optimization, global mixed- integer nonlinear programming, and exponential sum model fitting. NLPLIB TB, like the toolbox OPERA TB for linear and discrete optimization, is a part of TOMLAB; an environment in Matlab for research and teaching in optimization. TOMLAB currently solves small and medium size dense problems. Presently, NLPLIB TB implements more than 25 solver algorithms, and it is possible to call solvers in the Matlab Optimization Toolbox. MEX- file interfaces are prepared for seven Fortran and C solvers, and others are easily added using the same type of interface routines. Currently, MEXfile interfaces have been developed for MINOS, NPSOL, NPOPT, NLSSOL, LPOPT, QPOPT and LSSOL. There are four ways to solve a problem: by a direct call to the solver routine or a call to a multi- solver driver routine, or interactively, using the Graphical User Interface (GUI) or a menu system. The GUI may also be used as a preprocessor to generate Matlab code for standalone runs. If analytical derivatives are not available, automatic differentiation is easy using an interface to ADMAT/ADMIT TB. Furthermore, five types of numerical differentiation methods are included in NLPLIB TB. NLPLIB TB implements a large set of standard test problems. Using MEXfile interfaces, problems in the CUTE test problem data base and problems defined in the AMPL modeling language can be solved. TOMLAB and NLPLIB TB have been used to solve several applied optimization problems. New types of algorithms are implemented for the nonlinear least squares problem to approxi mate sums of exponential functions to empirical data and for global optimization. We present some preliminary test results, which show very good performance for the NLPLIB TB solvers.

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  1. Manno, Andrea; Amaldi, Edoardo; Casella, Francesco; Martelli, Emanuele: A local search method for costly black-box problems and its application to CSP plant start-up optimization refinement (2020)
  2. Sun, Chuangchuang; Dai, Ran: An iterative rank penalty method for nonconvex quadratically constrained quadratic programs (2019)
  3. Torrisi, Giampaolo; Grammatico, Sergio; Smith, Roy S.; Morari, Manfred: A projected gradient and constraint linearization method for nonlinear model predictive control (2018)
  4. Tan, Tunzi; Gao, Suixiang; Mesa, Juan A.: An exact algorithm for min-max hyperstructure equipartition with a connected constraint (2017)
  5. Zhao, Xiao; Noack, Stephan; Wiechert, Wolfgang; von Lieres, Eric: Dynamic flux balance analysis with nonlinear objective function (2017)
  6. Ma, Ding; Saunders, Michael A.: Solving multiscale linear programs using the simplex method in quadruple precision (2015)
  7. Conde, Eduardo: A MIP formulation for the minmax regret total completion time in scheduling with unrelated parallel machines (2014)
  8. Ahn, Mihye; Zhang, Hao Helen; Lu, Wenbin: Moment-based method for random effects selection in linear mixed models (2012)
  9. Berend, Daniel; Korach, Ephraim; Zucker, Shira: Tabu search for the BWC problem (2012)
  10. Ingolfsson, Armann; Campello, Fernanda; Wu, Xudong; Cabral, Edgar: Combining integer programming and the randomization method to schedule employees (2010)
  11. Jakobsson, Stefan; Saif-Ul-Hasnain, Muhammad; Rundqvist, Robert; Edelvik, Fredrik; Andersson, Björn; Patriksson, Michael; Ljungqvist, Mattias; Lortet, Dimitri; Wallesten, Johan: Combustion engine optimization: a multiobjective approach (2010)
  12. Pisică, Ioana; Postolache, Petru; Edvall, Marcus M.: Optimal planning of distributed generation via nonlinear optimization and genetic algorithms (2010)
  13. Chiu, Nan-Chieh; Fang, Shu-Cherng; Lavery, John E.; Lin, Jen-Yen; Wang, Yong: Approximating term structure of interest rates using cubic (L_1) splines (2008)
  14. Holmström, Kenneth; Quttineh, Nils-Hassan; Edvall, Marcus M.: An adaptive radial basis algorithm (ARBF) for expensive black-box mixed-integer constrained global optimization (2008)
  15. Regis, Rommel G.; Shoemaker, Christine A.: Improved strategies for radial basis function methods for global optimization (2007)
  16. Murray, Walter; Shanbhag, Uday V.: A local relaxation approach for the siting of electrical substations (2006)
  17. Regis, Rommel G.; Shoemaker, Christine A.: Constrained global optimization of expensive black box functions using radial basis functions (2005)
  18. Banga, Julio R.; Moles, Carmen G.; Alonso, Antonio A.: Global optimization of bioprocesses using stochastic and hybrid methods (2004)
  19. Berbyuk, V. E.: Control and optimization of semi-passively actuated multibody systems (2003)
  20. Golub, Gene; Pereyra, Victor: Separable nonlinear least squares: The variable projection method and its applications (2003)

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