LIPSOL stands for Linear programming Interior-Point SOLvers. It is a free, Matlab-based software package for solving linear programs by interior-Point methods. It requires Matlab version 4.0 or later to run. The current release of LIPSOL is for 32-bit UNIX platforms. LIPSOL is designed to solve relatively large problems. It utilizes Matlab’s sparse-matrix data-structure and Application Program Interface facility, and at the same time takes advantages of existing, efficient Fortran codes for solving large, sparse, symmetric positive definite linear systems. Specifically, LIPSOL constructs MEX-files from two Fortran packages: a sparse Cholesky factorization package developed by Esmond Ng and Barry Peyton at ORNL and a multiple minimum-degree ordering package by Joseph Liu at University of Waterloo. Built in the high-level programming environment of Matlab, LIPSOL enjoys a much greater degree of simplicity and versatility than codes in Fortran or C language. On the other hand, utilizing efficient Fortran codes for computationally intensive tasks, LIPSOL also has adequate speed for solving moderately large-scale problems even in the presence of overhead induced from Matlab. LIPSOL has been extensively tested on the Netlib set of linear programs and has effectively solved all 95 Netlib problems. LIPSOL is free software and comes with no warranty. All files written by this author are copyrighted under the terms of the GNU General Public License as published by the Free Software Foundation.

References in zbMATH (referenced in 69 articles )

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  1. Yang, Yaguang: CurveLP-A MATLAB implementation of an infeasible interior-point algorithm for linear programming (2017)
  2. Asadi, Alireza; Roos, Cornelis: Infeasible interior-point methods for linear optimization based on large neighborhood (2016)
  3. Cartis, Coralia; Yan, Yiming: Active-set prediction for interior point methods using controlled perturbations (2016)
  4. Chubanov, Sergei: A polynomial projection algorithm for linear feasibility problems (2015)
  5. Ploskas, Nikolaos; Samaras, Nikolaos: Efficient GPU-based implementations of simplex type algorithms (2015)
  6. Bocanegra, Silvana; Castro, Jordi; Oliveira, Aurelio R.L.: Improving an interior-point approach for large block-angular problems by hybrid preconditioners (2013)
  7. Castro, Jordi; Cuesta, Jordi: Solving $ L_1$-CTA in 3D tables by an interior-point method for primal block-angular problems (2013)
  8. Li, Xianhong; Yu, Haibin; Yuan, Mingzhe: Design of optimal PID controller with $\epsilon$-Routh stability for different processes (2013)
  9. Bandeira, Michael Martin A.S.; Scheinberg, K.; Vicente, L.N.: Computation of sparse low degree interpolating polynomials and their application to derivative-free optimization (2012)
  10. Carrasco, Miguel; Ivorra, Benjamin; Ramos, Angel Manuel: A variance-expected compliance model for structural optimization (2012)
  11. Jung, Jin Hyuk; O’Leary, Dianne P.; Tits, André L.: Adaptive constraint reduction for convex quadratic programming (2012)
  12. Li, Xianhong; Yu, Haibin; Yuan, Mingzhe: Modeling and optimization of cement raw materials blending process (2012)
  13. Petra, Cosmin G.; Anitescu, Mihai: A preconditioning technique for Schur complement systems arising in stochastic optimization (2012)
  14. Velazco Fontova, Marta I.; Oliveira, Aurelio R.L.; Lyra, Christiano: Hopfield neural networks in large-scale linear optimization problems (2012)
  15. Xu, Yinghong; Zhang, Lipu; Jin, Zhengjing: A predictor-corrector algorithm for linear optimization based on a modified Newton direction (2012)
  16. Choi, Sou-Cheng T.; Paige, Christopher C.; Saunders, Michael A.: MINRES-QLP: a Krylov subspace method for indefinite or singular symmetric systems (2011)
  17. Yang, Yaguang: A polynomial arc-search interior-point algorithm for convex quadratic programming (2011)
  18. Hofwing, Magnus; Strömberg, Niclas: D-optimality of non-regular design spaces by using a Bayesian modification and a hybrid method (2010)
  19. Parra-Guevara, D.; Skiba, Yu.N.; Pérez-Sesma, A.: A linear programming model for controlling air pollution (2010)
  20. Pólik, Imre; Terlaky, Tamás: Interior point methods for nonlinear optimization (2010)

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