Subroutines for nondifferentiable optimization

• PMIN.FOR - SQP variable metric methods for unconstrained and linearly constrained minimax optimization.
• TMIN.FOR - Examples of the subroutine PMIN.FOR application.
• PMIN.TXT - A short description of the subroutine PMIN.FOR.
• PBUN.FOR - Proximal bundle method for unconstrained and linearly constrained nonsmooth optimization.
• TBUN.FOR - Examples of the subroutine PBUN.FOR application.
• PBUN.TXT - A short description of the subroutine PBUN.FOR.
• PNEW.FOR - Bundle-Newton method for unconstrained and linearly constrained nonsmooth optimization.
• TNEW.FOR - Examples of the subroutine PNEW.FOR application.
• PNEW.TXT - A short description of the subroutine PNEW.FOR.
• PVAR.FOR - Variable metric methods for unconstrained and linearly constrained nonsmooth optimization.
• TVAR.FOR - Examples of the subroutine PVAR.FOR application.
• PVAR.TXT - A short description of the subroutine PVAR.FOR.
• PBSUBS.FOR - Basic modules for the above subroutines.
• MBSUBS.FOR - Matrix modules for the above subroutines.
• TBSUBS.FOR - Test modules for the above subroutines.
• TEST19.DAT - Data for test modules.

Subroutines for large-scale optimization

• PLIS.FOR - Limited-memory BFGS method based on vector recurrences for large-scale unconstrained and box constrained minimization.
• TLIS.FOR - An example of the subroutine PLIS.FOR application.
• PLIS.TXT - A short description of the subroutine PLIS.FOR.
• PLIP.FOR - Limited-memory variable metric methods based on product-form updates for large-scale unconstrained and box constrained minimization.
• TLIP.FOR - An example of the subroutine PLIP.FOR application.
• PLIP.TXT - A short description of the subroutine PLIP.FOR.
• PLIC.FOR - Limited-memory variable metric methods based on conjugate direction updates for large-scale unconstrained and box constrained minimization.
• TLIC.FOR - An example of the subroutine PLIC.FOR application.
• PLIC.TXT - A short description of the subroutine PLIC.FOR.
• PLIV.FOR - Limited-memory variable metric methods based on multiple correction updates for large-scale unconstrained and box constrained minimization.
• TLIV.FOR - An example of the subroutine PLIV.FOR application.
• PLIV.TXT - A short description of the subroutine PLIV.FOR.
• PNET.FOR - Truncated Newton method with an iterative computation of the direction vector for large-scale unconstrained and box constrained minimization.
• TNET.FOR - An example of the subroutine PNET.FOR application.
• PNET.TXT - A short description of the subroutine PNET.FOR.
• PNED.FOR - Discrete Newton method with a direct solution of the trust-region subproblem for large-scale sparse unconstrained and box constrained minimization.
• TNED.FOR - An example of the subroutine PNED.FOR application.
• PNED.TXT - A short description of the subroutine PNED.FOR.
• PNEC.FOR - Discrete Newton method with an iterative solution of the trust-region subproblem for large-scale sparse unconstrained and box constrained minimization.
• TNEC.FOR - An example of the subroutine PNEC.FOR application.
• PNEC.TXT - A short description of the subroutine PNEC.FOR.
• PSED.FOR - Variable metric methods with a direct computation of the direction vector for large-scale partially separable unconstrained and box constrained minimization.
• TSED.FOR - An example of the subroutine PSED.FOR application.
• PSED.TXT - A short description of the subroutine PSED.FOR.
• PSEC.FOR - Variable metric methods with an iterative computation of the direction vector for large-scale partially separable unconstrained and box constrained minimization.
• TSEC.FOR - An example of the subroutine PSEC.FOR application.
• PSEC.TXT - A short description of the subroutine PSEC.FOR.
• PSEN.FOR - Bundle variable metric method for large-scale nonsmooth partially separable minimization.
• TSEN.FOR - An example of the subroutine PSEN.FOR application.
• PSEN.TXT - A short description of the subroutine PSEN.FOR.
• PGAD.FOR - Hybrid methods with a direct solution of the trust-region subproblem for large-scale unconstrained and box constrained nonlinear least squares.
• TGAD.FOR - An example of the subroutine PGAD.FOR application.
• PGAD.TXT - A short description of the subroutine PGAD.FOR.
• PGAC.FOR - Hybrid methods with an iterative solution of the trust-region subproblem for large-scale unconstrained and box constrained nonlinear least squares.
• TGAC.FOR - An example of the subroutine PGAC.FOR application.
• PGAC.TXT - A short description of the subroutine PGAC.FOR.
• PMAX.FOR - Primal interior-point methods for large-scale nonlinear minimax optimization.
• TMAX.FOR - An example of the subroutine PMAX.FOR application.
• PMAX.TXT - A short description of the subroutine PMAX.FOR.
• PSUM.FOR - Primal interior-point methods for large-scale nonlinear sum of absolute values.
• TSUM.FOR - An example of the subroutine PSUM.FOR application.
• PSUM.TXT - A short description of the subroutine PSUM.FOR.
• PEQN.FOR - Discrete Newton method for large-scale systems of nonlinear equations.
• TEQN.FOR - An example of the subroutine PEQN.FOR application.
• PEQN.TXT - A short description of the subroutine PEQN.FOR.
• PEQL.FOR - Inverse column update method for large-scale systems of nonlinear equations.
• TEQL.FOR - An example of the subroutine PEQL.FOR application.
• PEQL.TXT - A short description of the subroutine PEQL.FOR.
• PSSUBS.FOR - Basic modules for the above subroutines.
• MSSUBS.FOR - Matrix modules for the above subroutines.
• TSSUBS.FOR - Test modules for the above subroutines.

Subroutines for large-scale nonlinear programming

• PIND.FOR - Indefinitely preconditioned full-system inexact Newton method for large-scale equality constrained optimization.
• TIND.FOR - An example of the subroutine PIND.FOR application.
• PIND.TXT - A short description of the subroutine PIND.FOR.
• PNUL.FOR - Preconditioned null-space inexact Newton method for large-scale equality constrained optimization.
• TNUL.FOR - An example of the subroutine PNUL.FOR application.
• PNUL.TXT - A short description of the subroutine PNUL.FOR.
• PNSUBS.FOR - Basic modules for the above subroutines.
• MNSUBS.FOR - Matrix modules for the above subroutines.
• TNSUBS.FOR - Test modules for the above subroutines.

Subroutines for dense nonlinear optimization

• PVMM.FOR - Variable metric methods for unconstrained and linearly constrained optimization.
• TVMM.FOR - Examples of the subroutine PVMM.FOR application.
• PVMM.TXT - A short description of the subroutine PVMM.FOR.
• PHYB.FOR - Hybrid methods for unconstrained and linearly constrained nonlinear least squares.
• THYB.FOR - Examples of the subroutine PHYB.FOR application.
• PHYB.TXT - A short description of the subroutine PHYB.FOR.
• PSQP.FOR - SQP variable metric methods for general nonlinear programming problems.
• TSQP.FOR - An example of the subroutine PSQP.FOR application.
• PSQP.TXT - A short description of the subroutine PSQP.FOR.
• PNEQ.FOR - Modified Newton method for systems of nonlinear equations.
• TNEQ.FOR - Examples of the subroutine PNEQ.FOR application.
• PNEQ.TXT - A short description of the subroutine PNEQ.FOR.
• PBEQ.FOR - Broyden's quasi-Newton method for systems of nonlinear equations.
• TBEQ.FOR - Examples of the subroutine PBEQ.FOR application.
• PBEQ.TXT - A short description of the subroutine PBEQ.FOR.
• PQSUBS.FOR - Basic modules for the above subroutines.
• MQSUBS.FOR - Matrix modules for the above subroutines.
• TQSUBS.FOR - Test modules for the above subroutines.

Research Reports

•  V 797-00 - Description of subroutines for nondifferentiable optimization.
•  V 999-07 - Description of subroutines for large-scale optimization.
• V1000-07 - Description of subroutines for large-scale nonlinear programming.

License

• Subroutines PMIN, PBUN, PNEW, PVAR, published as Algorithm 811 in ACM Transactions on Mathematical Software, Vol.27, No.2, 2001, and subroutines PLIS, PLIP, PNET, PNED, PNEC, PSED, PSEC, PSEN, PGAD, PGAC, PMAX, PSUM, PEQN, PEQL, published as Algorithm 896 in ACM Transactions on Mathematical Softwareare, Vol.36, No.3, 2009, are licensed by the ACM Software License Agreement.
• The other subroutines are licensed by the GNU Lesser General Public License (LGPL); see the copyright information for explicit details, including the author list and acknowledgements.