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Earlier this year Aug 25, 2009 r41 (Changed some files ) committed by xu.mathena - Changed some files Changed some files Aug 25, 2009 issue 8 (undefined symbol: _gfortran_st_write_done) Status changed by xu.mathena - Thanks for reporting this:) Since it's fixed, issue closed. I will add this to the doc Status: Fixed Thanks for reporting this:) Since it's fixed, issue closed. I will add this to the doc Status: Fixed Aug 25, 2009 issue 6 (Using Python 2.6 triggers depreciation warnings) Labels changed by xu.mathena - Labels: Type-Task −Type-Defect Labels: Type-Task −Type-Defect Aug 25, 2009 issue 5 ( undefined symbol: dsyev_) Status changed by xu.mathena - Status: Invalid Status: Invalid Jun 04, 2009 issue 8 (undefined symbol: _gfortran_st_write_done) commented on by Andrew.B.Lambe - I solved it! Similar to - again - issue 5 , you need to add a link to gfortran in the list of linking flags in pyipopt's makefile. For me that particular line in the makefile looks like this: LDFLAGS = -lipopt -lg2c -lm -lblas -llapack -lgfortran I also get a couple of deprecation warnings with the output, but this is a separate issue. I solved it! Similar to - again - issue 5 , you need to add a link to gfortran in the list of linking flags in pyipopt's makefile. For me that particular line in the makefile looks like this: LDFLAGS = -lipopt -lg2c -lm -lblas -llapack -lgfortran I also get a couple of deprecation warnings with the output, but this is a separate issue. Jun 03, 2009 issue 8 (undefined symbol: _gfortran_st_write_done) reported by Andrew.B.Lambe - What steps will reproduce the problem? 1. Compile ipopt from source 2. Compile pyipopt from source 3. Run example I basically followed the same procedure as issue 5 , including adding the links to blas and lapack on my system. Currently using the MA27 library for the linear system solve. What is the expected output? What do you see instead? Expected: the standard ipopt output file, through the Python interface (again, same as issue 5 ) Actual: Traceback (most recent call last): File "/home/andrew/pyipopt/example.py", line 6, in <module> import pyipopt ImportError: /usr/lib/libipopt.so.0: undefined symbol: _gfortran_st_write_done What version of the product are you using? On what operating system? pyipopt version 0.7 Ubuntu 8.04 x86_64 operating system Please provide any additional information below. I know this error is mentioned specifically in your documentation. I tried compiling the test problem packaged with ipopt and the problem runs correctly on all 3 interfaces. Thus, I'm pretty certain ipopt runs correctly on its own. What steps will reproduce the problem? 1. Compile ipopt from source 2. Compile pyipopt from source 3. Run example I basically followed the same procedure as issue 5 , including adding the links to blas and lapack on my system. Currently using the MA27 library for the linear system solve. What is the expected output? What do you see instead? Expected: the standard ipopt output file, through the Python interface (again, same as issue 5 ) Actual: Traceback (most recent call last): File "/home/andrew/pyipopt/example.py", line 6, in <module> import pyipopt ImportError: /usr/lib/libipopt.so.0: undefined symbol: _gfortran_st_write_done What version of the product are you using? On what operating system? pyipopt version 0.7 Ubuntu 8.04 x86_64 operating system Please provide any additional information below. I know this error is mentioned specifically in your documentation. I tried compiling the test problem packaged with ipopt and the problem runs correctly on all 3 interfaces. Thus, I'm pretty certain ipopt runs correctly on its own. May 01, 2009 issue 5 ( undefined symbol: dsyev_) commented on by xu.mathena - Hi, pyipopt is just an interface, I didn't touch any internal thing of ipopt. So I don't know how ipopt defines those symbols/calls third-party libraries. So I don't know how to switch from numps to other solvers. Maybe you can go to ipopt mailing list for more information. Hi, pyipopt is just an interface, I didn't touch any internal thing of ipopt. So I don't know how ipopt defines those symbols/calls third-party libraries. So I don't know how to switch from numps to other solvers. Maybe you can go to ipopt mailing list for more information. May 01, 2009 issue 6 (Using Python 2.6 triggers depreciation warnings) commented on by xu.mathena - yes, it doesnot support py2.6 very well yes, it doesnot support py2.6 very well May 01, 2009 issue 7 (Crash when using object methods as callbacks) commented on by xu.mathena - thank you for reporting this, I will test it. thank you for reporting this, I will test it. Apr 01, 2009 issue 7 (Crash when using object methods as callbacks) reported by jorn.baayen - Pyipopt.create does not like being passing object methods as eval_f, etc. Something like class Test: def eval_f(self, x): return x[0] 2 + x[1] 2 ... def run(self): nlp = pyipopt.create( ... ) opt, zl, zu, obj = nlp.solve(x_0) nlp.close() will illustrate the problem. Pyipopt.create does not like being passing object methods as eval_f, etc. Something like class Test: def eval_f(self, x): return x[0] 2 + x[1] 2 ... def run(self): nlp = pyipopt.create( ... ) opt, zl, zu, obj = nlp.solve(x_0) nlp.close() will illustrate the problem. Apr 01, 2009 issue 6 (Using Python 2.6 triggers depreciation warnings) reported by jorn.baayen - optimize.py:62: DeprecationWarning: PyArray_FromDims: use PyArray_SimpleNew. opt, zl, zu, obj = nlp.solve(x_0) optimize.py:62: DeprecationWarning: PyArray_FromDimsAndDataAndDescr: use PyArray_NewFromDescr. optimize.py:62: DeprecationWarning: PyArray_FromDims: use PyArray_SimpleNew. opt, zl, zu, obj = nlp.solve(x_0) optimize.py:62: DeprecationWarning: PyArray_FromDimsAndDataAndDescr: use PyArray_NewFromDescr. Feb 21, 2009 issue 5 ( undefined symbol: dsyev_) reported by nils106 - What steps will reproduce the problem? 1. Install Ipopt from source. svn co https://projects.coin-or.org/svn/Ipopt/trunk CoinIpopt ./configure make make test make install 2. Install pyipopt from source. svn checkout http://pyipopt.googlecode.com/svn/trunk/ pyipopt-read-only make pyipopt make install 3. Run the test example shipped with pyipopt. python -i example.py Traceback (most recent call last): File "example.py", line 6, in <module> import pyipopt ImportError: /home/nwagner/svn/CoinIpopt/lib/libipopt.so.0: undefined symbol: dsyev_ nm /home/nwagner/svn/CoinIpopt/lib/libipopt.so.0 \| grep dsyev U dsyev_ If I add -llapack -lblas to the line LDFLAGS = -L/home/nwagner/svn/CoinIpopt/lib -lipopt -llapack -lblas -lgfortran -lm in the makefile it works fine for me. What is the expected output? python -i example.py [PyIPOPT] Ipopt will use Hessian approximation. [PyIPOPT] nele_hess is 10 example.py:110: DeprecationWarning: PyArray_FromDims: use PyArray_SimpleNew. x, zl, zu, obj = nlp.solve(x0) example.py:110: DeprecationWarning: PyArray_FromDimsAndDataAndDescr: use PyArray_NewFromDescr. x, zl, zu, obj = nlp.solve(x0) **************************************************************************** This program contains Ipopt, a library for large-scale nonlinear optimization. Ipopt is released as open source code under the Common Public License (CPL). For more information visit http://projects.coin-or.org/Ipopt **************************************************************************** NOTE: You are using Ipopt by default with the MUMPS linear solver. Other linear solvers might be more efficient (see Ipopt documentation). This is Ipopt version 3.5trunk, running with linear solver mumps. Number of nonzeros in equality constraint Jacobian...: 4 Number of nonzeros in inequality constraint Jacobian.: 4 Number of nonzeros in Lagrangian Hessian.............: 0 Total number of variables............................: 4 variables with only lower bounds: 0 variables with lower and upper bounds: 4 variables with only upper bounds: 0 Total number of equality constraints.................: 1 Total number of inequality constraints...............: 1 inequality constraints with only lower bounds: 1 inequality constraints with lower and upper bounds: 0 inequality constraints with only upper bounds: 0 iter objective inf_pr inf_du lg(mu) \|\|d\|\| lg(rg) alpha_du alpha_pr ls 0 1.6109693e+01 1.12e+01 5.28e-01 0.0 0.00e+00 - 0.00e+00 0.00e+00 0 1 1.7420709e+01 8.40e-01 2.40e+01 -0.3 8.13e-01 - 3.20e-01 1.00e+00f 1 2 1.7120453e+01 2.14e-01 2.00e-01 -0.6 1.02e+00 - 9.97e-01 1.00e+00h 1 3 1.6979032e+01 2.15e-01 8.49e-02 -1.6 1.18e-01 - 9.72e-01 1.00e+00h 1 4 1.6972554e+01 7.81e-02 1.20e-02 -3.2 8.01e-02 - 1.00e+00 1.00e+00h 1 5 1.7013661e+01 6.94e-04 9.50e-03 -4.9 1.00e-02 - 1.00e+00 1.00e+00h 1 6 1.7014018e+01 1.96e-06 3.49e-04 -6.1 1.09e-03 - 1.00e+00 1.00e+00h 1 7 1.7014017e+01 1.47e-09 2.15e-07 -8.2 3.02e-05 - 1.00e+00 1.00e+00h 1 8 1.7014017e+01 0.00e+00 1.69e-09 -11.0 1.17e-08 - 1.00e+00 1.00e+00h 1 Number of Iterations....: 8 (scaled) (unscaled) Objective...............: 1.7014017140224176e+01 1.7014017140224176e+01 Dual infeasibility......: 1.6853158598871439e-09 1.6853158598871439e-09 Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00 Complementarity.........: 1.0002952000760553e-11 1.0002952000760553e-11 Overall NLP error.......: 1.6853158598871439e-09 1.6853158598871439e-09 Number of objective function evaluations = 9 Number of objective gradient evaluations = 9 Number of equality constraint evaluations = 9 Number of inequality constraint evaluations = 9 Number of equality constraint Jacobian evaluations = 9 Number of inequality constraint Jacobian evaluations = 9 Number of Lagrangian Hessian evaluations = 0 Total CPU secs in IPOPT (w/o function evaluations) = 0.008 Total CPU secs in NLP function evaluations = 0.004 EXIT: Optimal Solution Found. Problem solved Solution of the primal variables, x [ 1. 4.74299964 3.82114998 1.37940829] Solution of the bound multipliers, z_L and z_U [ 1.08787121e+00 2.67165388e-12 3.54465533e-12 2.63564701e-11] [ 2.49999763e-12 3.89104702e-11 8.48283408e-12 2.76198339e-12] Objective value f(x) = 17.0140171402 What do you see instead? What version of the product are you using? On what operating system? svn versions on opensuse11.1 x86_64. Please provide any additional information below. How can I switch from MUMPS to another linear solver ? What steps will reproduce the problem? 1. Install Ipopt from source. svn co https://projects.coin-or.org/svn/Ipopt/trunk CoinIpopt ./configure make make test make install 2. Install pyipopt from source. svn checkout http://pyipopt.googlecode.com/svn/trunk/ pyipopt-read-only make pyipopt make install 3. Run the test example shipped with pyipopt. python -i example.py Traceback (most recent call last): File "example.py", line 6, in <module> import pyipopt ImportError: /home/nwagner/svn/CoinIpopt/lib/libipopt.so.0: undefined symbol: dsyev_ nm /home/nwagner/svn/CoinIpopt/lib/libipopt.so.0 \| grep dsyev U dsyev_ If I add -llapack -lblas to the line LDFLAGS = -L/home/nwagner/svn/CoinIpopt/lib -lipopt -llapack -lblas -lgfortran -lm in the makefile it works fine for me. What is the expected output? python -i example.py [PyIPOPT] Ipopt will use Hessian approximation. [PyIPOPT] nele_hess is 10 example.py:110: DeprecationWarning: PyArray_FromDims: use PyArray_SimpleNew. x, zl, zu, obj = nlp.solve(x0) example.py:110: DeprecationWarning: PyArray_FromDimsAndDataAndDescr: use PyArray_NewFromDescr. x, zl, zu, obj = nlp.solve(x0) *************************************************************************** This program contains Ipopt, a library for large-scale nonlinear optimization. Ipopt is released as open source code under the Common Public License (CPL). For more information visit http://projects.coin-or.org/Ipopt **************************************************************************** NOTE: You are using Ipopt by default with the MUMPS linear solver. Other linear solvers might be more efficient (see Ipopt documentation). This is Ipopt version 3.5trunk, running with linear solver mumps. Number of nonzeros in equality constraint Jacobian...: 4 Number of nonzeros in inequality constraint Jacobian.: 4 Number of nonzeros in Lagrangian Hessian.............: 0 Total number of variables............................: 4 variables with only lower bounds: 0 variables with lower and upper bounds: 4 variables with only upper bounds: 0 Total number of equality constraints.................: 1 Total number of inequality constraints...............: 1 inequality constraints with only lower bounds: 1 inequality constraints with lower and upper bounds: 0 inequality constraints with only upper bounds: 0 iter objective inf_pr inf_du lg(mu) \|\|d\|\| lg(rg) alpha_du alpha_pr ls 0 1.6109693e+01 1.12e+01 5.28e-01 0.0 0.00e+00 - 0.00e+00 0.00e+00 0 1 1.7420709e+01 8.40e-01 2.40e+01 -0.3 8.13e-01 - 3.20e-01 1.00e+00f 1 2 1.7120453e+01 2.14e-01 2.00e-01 -0.6 1.02e+00 - 9.97e-01 1.00e+00h 1 3 1.6979032e+01 2.15e-01 8.49e-02 -1.6 1.18e-01 - 9.72e-01 1.00e+00h 1 4 1.6972554e+01 7.81e-02 1.20e-02 -3.2 8.01e-02 - 1.00e+00 1.00e+00h 1 5 1.7013661e+01 6.94e-04 9.50e-03 -4.9 1.00e-02 - 1.00e+00 1.00e+00h 1 6 1.7014018e+01 1.96e-06 3.49e-04 -6.1 1.09e-03 - 1.00e+00 1.00e+00h 1 7 1.7014017e+01 1.47e-09 2.15e-07 -8.2 3.02e-05 - 1.00e+00 1.00e+00h 1 8 1.7014017e+01 0.00e+00 1.69e-09 -11.0 1.17e-08 - 1.00e+00 1.00e+00h 1 Number of Iterations....: 8 (scaled) (unscaled) Objective...............: 1.7014017140224176e+01 1.7014017140224176e+01 Dual infeasibility......: 1.6853158598871439e-09 1.6853158598871439e-09 Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00 Complementarity.........: 1.0002952000760553e-11 1.0002952000760553e-11 Overall NLP error.......: 1.6853158598871439e-09 1.6853158598871439e-09 Number of objective function evaluations = 9 Number of objective gradient evaluations = 9 Number of equality constraint evaluations = 9 Number of inequality constraint evaluations = 9 Number of equality constraint Jacobian evaluations = 9 Number of inequality constraint Jacobian evaluations = 9 Number of Lagrangian Hessian evaluations = 0 Total CPU secs in IPOPT (w/o function evaluations) = 0.008 Total CPU secs in NLP function evaluations = 0.004 EXIT: Optimal Solution Found. Problem solved Solution of the primal variables, x [ 1. 4.74299964 3.82114998 1.37940829] Solution of the bound multipliers, z_L and z_U [ 1.08787121e+00 2.67165388e-12 3.54465533e-12 2.63564701e-11] [ 2.49999763e-12 3.89104702e-11 8.48283408e-12 2.76198339e-12] Objective value f(x*) = 17.0140171402 What do you see instead? What version of the product are you using? On what operating system? svn versions on opensuse11.1 x86_64. Please provide any additional information below. How can I switch from MUMPS to another linear solver ?

issue 5 ( undefined symbol: dsyev_) reported by nils106 - What steps will reproduce the problem? 1. Install Ipopt from source. svn co https://projects.coin-or.org/svn/Ipopt/trunk CoinIpopt ./configure make make test make install 2. Install pyipopt from source. svn checkout http://pyipopt.googlecode.com/svn/trunk/ pyipopt-read-only make pyipopt make install 3. Run the test example shipped with pyipopt. python -i example.py Traceback (most recent call last): File "example.py", line 6, in <module> import pyipopt ImportError: /home/nwagner/svn/CoinIpopt/lib/libipopt.so.0: undefined symbol: dsyev_ nm /home/nwagner/svn/CoinIpopt/lib/libipopt.so.0 | grep dsyev U dsyev_ If I add -llapack -lblas to the line LDFLAGS = -L/home/nwagner/svn/CoinIpopt/lib -lipopt -llapack -lblas -lgfortran -lm in the makefile it works fine for me. What is the expected output? python -i example.py [PyIPOPT] Ipopt will use Hessian approximation. [PyIPOPT] nele_hess is 10 example.py:110: DeprecationWarning: PyArray_FromDims: use PyArray_SimpleNew. x, zl, zu, obj = nlp.solve(x0) example.py:110: DeprecationWarning: PyArray_FromDimsAndDataAndDescr: use PyArray_NewFromDescr. x, zl, zu, obj = nlp.solve(x0) ****************************************************************************** This program contains Ipopt, a library for large-scale nonlinear optimization. Ipopt is released as open source code under the Common Public License (CPL). For more information visit http://projects.coin-or.org/Ipopt ****************************************************************************** NOTE: You are using Ipopt by default with the MUMPS linear solver. Other linear solvers might be more efficient (see Ipopt documentation). This is Ipopt version 3.5trunk, running with linear solver mumps. Number of nonzeros in equality constraint Jacobian...: 4 Number of nonzeros in inequality constraint Jacobian.: 4 Number of nonzeros in Lagrangian Hessian.............: 0 Total number of variables............................: 4 variables with only lower bounds: 0 variables with lower and upper bounds: 4 variables with only upper bounds: 0 Total number of equality constraints.................: 1 Total number of inequality constraints...............: 1 inequality constraints with only lower bounds: 1 inequality constraints with lower and upper bounds: 0 inequality constraints with only upper bounds: 0 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 0 1.6109693e+01 1.12e+01 5.28e-01 0.0 0.00e+00 - 0.00e+00 0.00e+00 0 1 1.7420709e+01 8.40e-01 2.40e+01 -0.3 8.13e-01 - 3.20e-01 1.00e+00f 1 2 1.7120453e+01 2.14e-01 2.00e-01 -0.6 1.02e+00 - 9.97e-01 1.00e+00h 1 3 1.6979032e+01 2.15e-01 8.49e-02 -1.6 1.18e-01 - 9.72e-01 1.00e+00h 1 4 1.6972554e+01 7.81e-02 1.20e-02 -3.2 8.01e-02 - 1.00e+00 1.00e+00h 1 5 1.7013661e+01 6.94e-04 9.50e-03 -4.9 1.00e-02 - 1.00e+00 1.00e+00h 1 6 1.7014018e+01 1.96e-06 3.49e-04 -6.1 1.09e-03 - 1.00e+00 1.00e+00h 1 7 1.7014017e+01 1.47e-09 2.15e-07 -8.2 3.02e-05 - 1.00e+00 1.00e+00h 1 8 1.7014017e+01 0.00e+00 1.69e-09 -11.0 1.17e-08 - 1.00e+00 1.00e+00h 1 Number of Iterations....: 8 (scaled) (unscaled) Objective...............: 1.7014017140224176e+01 1.7014017140224176e+01 Dual infeasibility......: 1.6853158598871439e-09 1.6853158598871439e-09 Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00 Complementarity.........: 1.0002952000760553e-11 1.0002952000760553e-11 Overall NLP error.......: 1.6853158598871439e-09 1.6853158598871439e-09 Number of objective function evaluations = 9 Number of objective gradient evaluations = 9 Number of equality constraint evaluations = 9 Number of inequality constraint evaluations = 9 Number of equality constraint Jacobian evaluations = 9 Number of inequality constraint Jacobian evaluations = 9 Number of Lagrangian Hessian evaluations = 0 Total CPU secs in IPOPT (w/o function evaluations) = 0.008 Total CPU secs in NLP function evaluations = 0.004 EXIT: Optimal Solution Found. Problem solved Solution of the primal variables, x [ 1. 4.74299964 3.82114998 1.37940829] Solution of the bound multipliers, z_L and z_U [ 1.08787121e+00 2.67165388e-12 3.54465533e-12 2.63564701e-11] [ 2.49999763e-12 3.89104702e-11 8.48283408e-12 2.76198339e-12] Objective value f(x*) = 17.0140171402 What do you see instead? What version of the product are you using? On what operating system? svn versions on opensuse11.1 x86_64. Please provide any additional information below. How can I switch from MUMPS to another linear solver ?

What steps will reproduce the problem? 1. Install Ipopt from source. svn co https://projects.coin-or.org/svn/Ipopt/trunk CoinIpopt ./configure make make test make install 2. Install pyipopt from source. svn checkout http://pyipopt.googlecode.com/svn/trunk/ pyipopt-read-only make pyipopt make install 3. Run the test example shipped with pyipopt. python -i example.py Traceback (most recent call last): File "example.py", line 6, in <module> import pyipopt ImportError: /home/nwagner/svn/CoinIpopt/lib/libipopt.so.0: undefined symbol: dsyev_ nm /home/nwagner/svn/CoinIpopt/lib/libipopt.so.0 | grep dsyev U dsyev_ If I add -llapack -lblas to the line LDFLAGS = -L/home/nwagner/svn/CoinIpopt/lib -lipopt -llapack -lblas -lgfortran -lm in the makefile it works fine for me. What is the expected output? python -i example.py [PyIPOPT] Ipopt will use Hessian approximation. [PyIPOPT] nele_hess is 10 example.py:110: DeprecationWarning: PyArray_FromDims: use PyArray_SimpleNew. x, zl, zu, obj = nlp.solve(x0) example.py:110: DeprecationWarning: PyArray_FromDimsAndDataAndDescr: use PyArray_NewFromDescr. x, zl, zu, obj = nlp.solve(x0) ****************************************************************************** This program contains Ipopt, a library for large-scale nonlinear optimization. Ipopt is released as open source code under the Common Public License (CPL). For more information visit http://projects.coin-or.org/Ipopt ****************************************************************************** NOTE: You are using Ipopt by default with the MUMPS linear solver. Other linear solvers might be more efficient (see Ipopt documentation). This is Ipopt version 3.5trunk, running with linear solver mumps. Number of nonzeros in equality constraint Jacobian...: 4 Number of nonzeros in inequality constraint Jacobian.: 4 Number of nonzeros in Lagrangian Hessian.............: 0 Total number of variables............................: 4 variables with only lower bounds: 0 variables with lower and upper bounds: 4 variables with only upper bounds: 0 Total number of equality constraints.................: 1 Total number of inequality constraints...............: 1 inequality constraints with only lower bounds: 1 inequality constraints with lower and upper bounds: 0 inequality constraints with only upper bounds: 0 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 0 1.6109693e+01 1.12e+01 5.28e-01 0.0 0.00e+00 - 0.00e+00 0.00e+00 0 1 1.7420709e+01 8.40e-01 2.40e+01 -0.3 8.13e-01 - 3.20e-01 1.00e+00f 1 2 1.7120453e+01 2.14e-01 2.00e-01 -0.6 1.02e+00 - 9.97e-01 1.00e+00h 1 3 1.6979032e+01 2.15e-01 8.49e-02 -1.6 1.18e-01 - 9.72e-01 1.00e+00h 1 4 1.6972554e+01 7.81e-02 1.20e-02 -3.2 8.01e-02 - 1.00e+00 1.00e+00h 1 5 1.7013661e+01 6.94e-04 9.50e-03 -4.9 1.00e-02 - 1.00e+00 1.00e+00h 1 6 1.7014018e+01 1.96e-06 3.49e-04 -6.1 1.09e-03 - 1.00e+00 1.00e+00h 1 7 1.7014017e+01 1.47e-09 2.15e-07 -8.2 3.02e-05 - 1.00e+00 1.00e+00h 1 8 1.7014017e+01 0.00e+00 1.69e-09 -11.0 1.17e-08 - 1.00e+00 1.00e+00h 1 Number of Iterations....: 8 (scaled) (unscaled) Objective...............: 1.7014017140224176e+01 1.7014017140224176e+01 Dual infeasibility......: 1.6853158598871439e-09 1.6853158598871439e-09 Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00 Complementarity.........: 1.0002952000760553e-11 1.0002952000760553e-11 Overall NLP error.......: 1.6853158598871439e-09 1.6853158598871439e-09 Number of objective function evaluations = 9 Number of objective gradient evaluations = 9 Number of equality constraint evaluations = 9 Number of inequality constraint evaluations = 9 Number of equality constraint Jacobian evaluations = 9 Number of inequality constraint Jacobian evaluations = 9 Number of Lagrangian Hessian evaluations = 0 Total CPU secs in IPOPT (w/o function evaluations) = 0.008 Total CPU secs in NLP function evaluations = 0.004 EXIT: Optimal Solution Found. Problem solved Solution of the primal variables, x [ 1. 4.74299964 3.82114998 1.37940829] Solution of the bound multipliers, z_L and z_U [ 1.08787121e+00 2.67165388e-12 3.54465533e-12 2.63564701e-11] [ 2.49999763e-12 3.89104702e-11 8.48283408e-12 2.76198339e-12] Objective value f(x*) = 17.0140171402 What do you see instead? What version of the product are you using? On what operating system? svn versions on opensuse11.1 x86_64. Please provide any additional information below. How can I switch from MUMPS to another linear solver ?

Earlier this year