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.

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  1. Niazadeh, Rad; Hartline, Jason; Immorlica, Nicole; Khani, Mohammad Reza; Lucier, Brendan: Fast core pricing for rich advertising auctions (2022)
  2. Dai, Yu-Hong; Wang, Zhouhong; Xu, Fengmin: A primal-dual algorithm for unfolding neutron energy spectrum from multiple activation foils (2021)
  3. Dai, Yu-Hong; Liu, Xin-Wei; Sun, Jie: A primal-dual interior-point method capable of rapidly detecting infeasibility for nonlinear programs (2020)
  4. Haghgoo, Mojtaba; Ansari, Reza; Hassanzadeh-Aghdam, Mohammad Kazem: The effect of nanoparticle conglomeration on the overall conductivity of nanocomposites (2020)
  5. Cui, Yiran; Morikuni, Keiichi; Tsuchiya, Takashi; Hayami, Ken: Implementation of interior-point methods for LP based on Krylov subspace iterative solvers with inner-iteration preconditioning (2019)
  6. Petra, Cosmin G.; Potra, Florian A.: A homogeneous model for monotone mixed horizontal linear complementarity problems (2019)
  7. Wang, Guoqiang; Yu, Bo: PAL-Hom method for QP and an application to LP (2019)
  8. Zhou, Yiyuan; Zhang, Mingwang; Huang, Zhengwei: On complexity of a new Mehrotra-type interior point algorithm for (P_\ast(\kappa)) linear complementarity problems (2019)
  9. Yang, Y.: Two computationally efficient polynomial-iteration infeasible interior-point algorithms for linear programming (2018)
  10. Ma, Xiaojue; Liu, Hongwei: A superlinearly convergent wide-neighborhood predictor-corrector interior-point algorithm for linear programming (2017)
  11. Yang, Yaguang: CurveLP-A MATLAB implementation of an infeasible interior-point algorithm for linear programming (2017)
  12. Asadi, Alireza; Roos, Cornelis: Infeasible interior-point methods for linear optimization based on large neighborhood (2016)
  13. Cartis, Coralia; Yan, Yiming: Active-set prediction for interior point methods using controlled perturbations (2016)
  14. Ramírez-Gil, Francisco Javier; Nelli Silva, Emilío Carlos; Montealegre-Rubio, Wilfredo: Topology optimization design of 3D electrothermomechanical actuators by using GPU as a co-processor (2016)
  15. Barbara, Abdessamad: Strict quasi-concavity and the differential barrier property of gauges in linear programming (2015)
  16. Chubanov, Sergei: A polynomial projection algorithm for linear feasibility problems (2015)
  17. Hirata, Yoshito; Shiro, Masanori; Takahashi, Nozomu; Aihara, Kazuyuki; Suzuki, Hideyuki; Mas, Paloma: Approximating high-dimensional dynamics by barycentric coordinates with linear programming (2015)
  18. Ploskas, Nikolaos; Samaras, Nikolaos: Efficient GPU-based implementations of simplex type algorithms (2015)
  19. Bocanegra, Silvana; Castro, Jordi; Oliveira, Aurelio R. L.: Improving an interior-point approach for large block-angular problems by hybrid preconditioners (2013)
  20. Castro, Jordi; Cuesta, Jordi: Solving ( L_1)-CTA in 3D tables by an interior-point method for primal block-angular problems (2013)

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