# Changeset 1219

Ignore:
Timestamp:
Apr 25, 2008 2:14:35 PM (6 years ago)
Message:

updated options documentation, fixed tickets #57 #69 #70

Location:
trunk
Files:
25 edited

Unmodified
Removed
• ## trunk/Ipopt/configure

 r1215 #! /bin/sh # Guess values for system-dependent variables and create Makefiles. # Generated by GNU Autoconf 2.59 for Ipopt 3.3dev. # Generated by GNU Autoconf 2.59 for Ipopt 3.4trunk. # # Report bugs to . # gives unlimited permission to copy, distribute and modify it. # # Copyright 2004, 2007 International Business Machines and others. # Copyright 2004, 2008 International Business Machines and others. # All Rights Reserved. # This file is part of the open source package IPOPT which is distributed PACKAGE_NAME='Ipopt' PACKAGE_TARNAME='ipopt' PACKAGE_VERSION='3.3dev' PACKAGE_STRING='Ipopt 3.3dev' PACKAGE_VERSION='3.4trunk' PACKAGE_STRING='Ipopt 3.4trunk' PACKAGE_BUGREPORT='http://projects.coin-or.org/Ipopt/newticket' # This message is too long to be a string in the A/UX 3.1 sh. cat <<_ACEOF \configure' configures Ipopt 3.3dev to adapt to many kinds of systems. \configure' configures Ipopt 3.4trunk to adapt to many kinds of systems. Usage: $0 [OPTION]... [VAR=VALUE]... if test -n "$ac_init_help"; then case $ac_init_help in short | recursive ) echo "Configuration of Ipopt 3.3dev:";; short | recursive ) echo "Configuration of Ipopt 3.4trunk:";; esac cat <<\_ACEOF if$ac_init_version; then cat <<\_ACEOF Ipopt configure 3.3dev Ipopt configure 3.4trunk generated by GNU Autoconf 2.59 gives unlimited permission to copy, distribute and modify it. Copyright 2004, 2007 International Business Machines and others. Copyright 2004, 2008 International Business Machines and others. All Rights Reserved. This file is part of the open source package IPOPT which is distributed running configure, to aid debugging if configure makes a mistake. It was created by Ipopt $as_me 3.3dev, which was It was created by Ipopt$as_me 3.4trunk, which was generated by GNU Autoconf 2.59.  Invocation command line was # Define the identity of the package. PACKAGE='ipopt' VERSION='3.3dev' VERSION='3.4trunk' cat >&5 <<_CSEOF This file was extended by Ipopt $as_me 3.3dev, which was This file was extended by Ipopt$as_me 3.4trunk, which was generated by GNU Autoconf 2.59.  Invocation command line was cat >>$CONFIG_STATUS <<_ACEOF ac_cs_version="\\ Ipopt config.status 3.3dev Ipopt config.status 3.4trunk configured by$0, generated by GNU Autoconf 2.59, with options \\"echo "$ac_configure_args" | sed 's/[\\""\\$]/\\\\&/g'\\"

• ## trunk/Ipopt/doc/documentation.tex

 r1215 acceptable-*} options. This may happen if the desired tolerances are too small for the current problem. \item[{\tt Feasible\_Point\_Found}:]  $\;$ \\ Console Message: {\tt EXIT: Feasible point for square problem found.} \\ This message is printed if the problem is square'' (i.e., it has as many equality constraints as free variables) and \Ipopt\ found a feasible point. \item[{\tt Infeasible\_Problem\_Detected}:]  $\;$ \\ Console Message: {\tt EXIT: Converged to a point of
• ## trunk/Ipopt/doc/options.tex

 r1000 \subsection{Output} \paragraph{print\_level:} Output verbosity level. $\;$ \\ \paragraph{print\_level:}\label{sec:print_level} Output verbosity level. $\;$ \\ Sets the default verbosity level for console output. The larger this value the more detailed \paragraph{print\_user\_options:} Print all options set by the user. $\;$ \\ \paragraph{print\_user\_options:}\label{sec:print_user_options} Print all options set by the user. $\;$ \\ If selected, the algorithm will print the list of all options set by the user including their \end{itemize} \paragraph{print\_options\_documentation:} Switch to print all algorithmic options. $\;$ \\ \paragraph{print\_options\_documentation:}\label{sec:print_options_documentation} Switch to print all algorithmic options. $\;$ \\ If selected, the algorithm will print the list of all available algorithmic options with some \end{itemize} \paragraph{output\_file:} File name of desired output file (leave unset for no file output). $\;$ \\ \paragraph{output\_file:}\label{sec:output_file} File name of desired output file (leave unset for no file output). $\;$ \\ NOTE: This option only works when read from the ipopt.opt options file! An output file with this \end{itemize} \paragraph{file\_print\_level:} Verbosity level for output file. $\;$ \\ \paragraph{file\_print\_level:}\label{sec:file_print_level} Verbosity level for output file. $\;$ \\ NOTE: This option only works when read from the ipopt.opt options file! Determines the verbosity \subsection{Termination} \paragraph{tol:} Desired convergence tolerance (relative). $\;$ \\ \paragraph{tol:}\label{sec:tol} Desired convergence tolerance (relative). $\;$ \\ Determines the convergence tolerance for the algorithm.  The algorithm terminates \paragraph{max\_iter:} Maximum number of iterations. $\;$ \\ \paragraph{max\_iter:}\label{sec:max_iter} Maximum number of iterations. $\;$ \\ The algorithm terminates with an error message if the number of iterations exceeded this number. The valid range for this integer option is \paragraph{dual\_inf\_tol:} Desired threshold for the dual infeasibility. $\;$ \\ \paragraph{dual\_inf\_tol:}\label{sec:dual_inf_tol} Desired threshold for the dual infeasibility. $\;$ \\ Absolute tolerance on the dual infeasibility. Successful termination requires that the max-norm this threshold. The valid range for this real option is $0 < {\tt dual\_inf\_tol } < {\tt +inf}$ and its default value is $0.0001$. \paragraph{constr\_viol\_tol:} Desired threshold for the constraint violation. $\;$ \\ and its default value is $1$. \paragraph{constr\_viol\_tol:}\label{sec:constr_viol_tol} Desired threshold for the constraint violation. $\;$ \\ Absolute tolerance on the constraint violation. Successful termination requires that the max-norm \paragraph{compl\_inf\_tol:} Desired threshold for the complementarity conditions. $\;$ \\ \paragraph{compl\_inf\_tol:}\label{sec:compl_inf_tol} Desired threshold for the complementarity conditions. $\;$ \\ Absolute tolerance on the complementarity. Successful termination requires that the max-norm \paragraph{acceptable\_tol:} "Acceptable" convergence tolerance (relative). $\;$ \\ \paragraph{acceptable\_tol:}\label{sec:acceptable_tol} "Acceptable" convergence tolerance (relative). $\;$ \\ Determines which (scaled) overall optimality error is considered to be "acceptable." There are \paragraph{acceptable\_constr\_viol\_tol:} "Acceptance" threshold for the constraint violation. $\;$ \\ \paragraph{acceptable\_constr\_viol\_tol:}\label{sec:acceptable_constr_viol_tol} "Acceptance" threshold for the constraint violation. $\;$ \\ Absolute tolerance on the constraint violation. "Acceptable" termination requires that the \paragraph{acceptable\_dual\_inf\_tol:} "Acceptance" threshold for the dual infeasibility. $\;$ \\ \paragraph{acceptable\_dual\_inf\_tol:}\label{sec:acceptable_dual_inf_tol} "Acceptance" threshold for the dual infeasibility. $\;$ \\ Absolute tolerance on the dual infeasibility. "Acceptable" termination requires that the \paragraph{acceptable\_compl\_inf\_tol:} "Acceptance" threshold for the complementarity conditions. $\;$ \\ \paragraph{acceptable\_compl\_inf\_tol:}\label{sec:acceptable_compl_inf_tol} "Acceptance" threshold for the complementarity conditions. $\;$ \\ Absolute tolerance on the complementarity. "Acceptable" termination requires that the \paragraph{diverging\_iterates\_tol:} Threshold for maximal value of primal iterates. $\;$ \\ \paragraph{diverging\_iterates\_tol:}\label{sec:diverging_iterates_tol} Threshold for maximal value of primal iterates. $\;$ \\ If any component of the primal iterates exceeded this value (in absolute terms), the optimization \subsection{NLP Scaling} \paragraph{obj\_scaling\_factor:} Scaling factor for the objective function. $\;$ \\ \paragraph{obj\_scaling\_factor:}\label{sec:obj_scaling_factor} Scaling factor for the objective function. $\;$ \\ This option sets a scaling factor for the objective function. The scaling is seen \paragraph{nlp\_scaling\_method:} Select the technique used for scaling the NLP. $\;$ \\ \paragraph{nlp\_scaling\_method:}\label{sec:nlp_scaling_method} Select the technique used for scaling the NLP. $\;$ \\ Selects the technique used for scaling the problem internally before it is solved. For \end{itemize} \paragraph{nlp\_scaling\_max\_gradient:} Maximum gradient after NLP scaling. $\;$ \\ \paragraph{nlp\_scaling\_max\_gradient:}\label{sec:nlp_scaling_max_gradient} Maximum gradient after NLP scaling. $\;$ \\ This is the gradient scaling cut-off. If the maximum gradient is above this value, then \subsection{NLP} \paragraph{bound\_relax\_factor:} Factor for initial relaxation of the bounds. $\;$ \\ \paragraph{bound\_relax\_factor:}\label{sec:bound_relax_factor} Factor for initial relaxation of the bounds. $\;$ \\ Before start of the optimization, the bounds given by the user are relaxed.  This option sets \paragraph{honor\_original\_bounds:} Indicates whether final points should be projected into original bounds. $\;$ \\ \paragraph{honor\_original\_bounds:}\label{sec:honor_original_bounds} Indicates whether final points should be projected into original bounds. $\;$ \\ Ipopt might relax the bounds during the optimization (see, e.g., option \end{itemize} \paragraph{check\_derivatives\_for\_naninf:} Indicates whether it is desired to check for Nan/Inf in derivative matrices $\;$ \\ \paragraph{check\_derivatives\_for\_naninf:}\label{sec:check_derivatives_for_naninf} Indicates whether it is desired to check for Nan/Inf in derivative matrices $\;$ \\ Activating this option will cause an error if an invalid number is detected in the constraint \end{itemize} \paragraph{nlp\_lower\_bound\_inf:} any bound less or equal this value will be considered -inf (i.e. not lower bounded). $\;$ \\ \paragraph{nlp\_lower\_bound\_inf:}\label{sec:nlp_lower_bound_inf} any bound less or equal this value will be considered -inf (i.e. not lower bounded). $\;$ \\ The valid range for this real option is ${\tt -inf} < {\tt nlp\_lower\_bound\_inf } < {\tt +inf}$ \paragraph{nlp\_upper\_bound\_inf:} any bound greater or this value will be considered +inf (i.e. not upper bounded). $\;$ \\ \paragraph{nlp\_upper\_bound\_inf:}\label{sec:nlp_upper_bound_inf} any bound greater or this value will be considered +inf (i.e. not upper bounded). $\;$ \\ The valid range for this real option is ${\tt -inf} < {\tt nlp\_upper\_bound\_inf } < {\tt +inf}$ \paragraph{fixed\_variable\_treatment:} Determines how fixed variables should be handled. $\;$ \\ \paragraph{fixed\_variable\_treatment:}\label{sec:fixed_variable_treatment} Determines how fixed variables should be handled. $\;$ \\ The main difference between those options is that the starting point in the "make\_constraint" case still has the fixed variables at their given values, whereas in the case "make\_parameter" the functions are always evaluated with the fixed values for those variables.  Also, for "relax\_bounds", the fixing bound constraints are relaxed (according to" bound\_relax\_factor"). For both "make\_constraints" and "relax\_bounds", bound multipliers are computed for the fixed variables. The default value for this string option is "make\_parameter". \\ variables \item make\_constraint: Add equality constraints fixing variables \item relax\_bounds: Relax fixing bound constraintsThe main difference between those options is that the starting point in the "make\_constraint" case still has the fixed variables at their given values, whereas in the case "make\_parameter" the functions are always evaluated with the fixed values for those variables.  For Also, for "relax\_bounds", the fixing bound constraints are relaxed (according to" bound\_relax\_factor"). For both "make\_constraints" and "relax\_bounds", bound multipliers are computed for the fixed variables. \end{itemize} \paragraph{jac\_c\_constant:} Indicates whether all equality constraints are linear $\;$ \\ \item relax\_bounds: Relax fixing bound constraints \end{itemize} \paragraph{jac\_c\_constant:}\label{sec:jac_c_constant} Indicates whether all equality constraints are linear $\;$ \\ Activating this option will cause Ipopt to ask for the Jacobian of the equality constraints only \end{itemize} \paragraph{jac\_d\_constant:} Indicates whether all inequality constraints are linear $\;$ \\ \paragraph{jac\_d\_constant:}\label{sec:jac_d_constant} Indicates whether all inequality constraints are linear $\;$ \\ Activating this option will cause Ipopt to ask for the Jacobian of the inequality constraints \end{itemize} \paragraph{hessian\_constant:} Indicates whether the problem is a quadratic problem $\;$ \\ \paragraph{hessian\_constant:}\label{sec:hessian_constant} Indicates whether the problem is a quadratic problem $\;$ \\ Activating this option will cause Ipopt to ask for the Hessian of the Lagrangian function only \subsection{Initialization} \paragraph{bound\_frac:} Desired minimum relative distance from the initial point to bound. $\;$ \\ \paragraph{bound\_frac:}\label{sec:bound_frac} Desired minimum relative distance from the initial point to bound. $\;$ \\ Determines how much the initial point might have to be modified in order to be sufficiently inside \paragraph{bound\_push:} Desired minimum absolute distance from the initial point to bound. $\;$ \\ \paragraph{bound\_push:}\label{sec:bound_push} Desired minimum absolute distance from the initial point to bound. $\;$ \\ Determines how much the initial point might have to be modified in order to be sufficiently inside \paragraph{slack\_bound\_frac:} Desired minimum relative distance from the initial slack to bound. $\;$ \\ \paragraph{slack\_bound\_frac:}\label{sec:slack_bound_frac} Desired minimum relative distance from the initial slack to bound. $\;$ \\ Determines how much the initial slack variables might have to be modified in order to be \paragraph{slack\_bound\_push:} Desired minimum absolute distance from the initial slack to bound. $\;$ \\ \paragraph{slack\_bound\_push:}\label{sec:slack_bound_push} Desired minimum absolute distance from the initial slack to bound. $\;$ \\ Determines how much the initial slack variables might have to be modified in order to be \paragraph{bound\_mult\_init\_val:} Initial value for the bound multipliers. $\;$ \\ \paragraph{bound\_mult\_init\_val:}\label{sec:bound_mult_init_val} Initial value for the bound multipliers. $\;$ \\ All dual variables corresponding to bound constraints are initialized to this value. The valid range for this real option is \paragraph{constr\_mult\_init\_max:} Maximum allowed least-square guess of constraint multipliers. $\;$ \\ \paragraph{constr\_mult\_init\_max:}\label{sec:constr_mult_init_max} Maximum allowed least-square guess of constraint multipliers. $\;$ \\ Determines how large the initial least-square guesses of the constraint multipliers are allowed \paragraph{bound\_mult\_init\_val:} Initial value for the bound multipliers. $\;$ \\ \paragraph{bound\_mult\_init\_method:}\label{sec:bound_mult_init_method} Initialization method for bound multipliers $\;$ \\ This option defines how the iterates for the bound multipliers are initialized.  If "constant" is chosen, then all bound multipliers are initialized to the value of "bound\_mult\_init\_val".  If "mu-based" is chosen, the each value is initialized to the the value of "mu\_init" divided by the corresponding slack variable.  This latter option might be useful if the starting point is close to the optimal solution. The default value for this string option is "constant". \\ Possible values: \begin{itemize} \item constant: set all bound multipliers to the value of bound\_mult\_init\_val \item mu-based: initialize to mu\_init/x\_slack \end{itemize} \paragraph{bound\_mult\_init\_val:}\label{sec:bound_mult_init_val} Initial value for the bound multipliers. $\;$ \\ All dual variables corresponding to bound constraints are initialized to this value. The valid range for this real option is \subsection{Barrier Parameter} \paragraph{mehrotra\_algorithm:} Indicates if we want to do Mehrotra's algorithm. $\;$ \\ \paragraph{mehrotra\_algorithm:}\label{sec:mehrotra_algorithm} Indicates if we want to do Mehrotra's algorithm. $\;$ \\ If set to yes, Ipopt runs as Mehrotra's predictor-corrector algorithm. This works usually "corrector\_type=affine" without any safeguards; you should not set any of those options explicitly in addition.  Also, unlessotherwise explicitly in addition.  Also, unless otherwise specified, the values of "bound\_push", "bound\_frac", and "bound\_mult\_init\_val" are \end{itemize} \paragraph{mu\_strategy:} Update strategy for barrier parameter. $\;$ \\ \paragraph{mu\_strategy:}\label{sec:mu_strategy} Update strategy for barrier parameter. $\;$ \\ Determines which barrier parameter update strategy is to be used. \end{itemize} \paragraph{mu\_oracle:} Oracle for a new barrier parameter in the adaptive strategy. $\;$ \\ \paragraph{mu\_oracle:}\label{sec:mu_oracle} Oracle for a new barrier parameter in the adaptive strategy. $\;$ \\ Determines how a new barrier parameter is computed in each "free-mode" iteration of the \end{itemize} \paragraph{quality\_function\_max\_section\_steps:} Maximum number of search steps during direct search procedure determining the optimal centering parameter. $\;$ \\ \paragraph{quality\_function\_max\_section\_steps:}\label{sec:quality_function_max_section_steps} Maximum number of search steps during direct search procedure determining the optimal centering parameter. $\;$ \\ The golden section search is performed for the quality function based mu oracle. (Only used if \paragraph{fixed\_mu\_oracle:} Oracle for the barrier parameter when switching to fixed mode. $\;$ \\ \paragraph{fixed\_mu\_oracle:}\label{sec:fixed_mu_oracle} Oracle for the barrier parameter when switching to fixed mode. $\;$ \\ Determines how the first value of the barrier parameter should be computed when switching to \end{itemize} \paragraph{mu\_init:} Initial value for the barrier parameter. $\;$ \\ \paragraph{mu\_init:}\label{sec:mu_init} Initial value for the barrier parameter. $\;$ \\ This option determines the initial value for the barrier parameter (mu).  It is only relevant in \paragraph{mu\_max\_fact:} Factor for initialization of maximum value for barrier parameter. $\;$ \\ \paragraph{mu\_max\_fact:}\label{sec:mu_max_fact} Factor for initialization of maximum value for barrier parameter. $\;$ \\ This option determines the upper bound on the barrier parameter.  This upper bound is computed \paragraph{mu\_max:} Maximum value for barrier parameter. $\;$ \\ \paragraph{mu\_max:}\label{sec:mu_max} Maximum value for barrier parameter. $\;$ \\ This option specifies an upper bound on the barrier parameter in the adaptive mu selection \paragraph{mu\_min:} Minimum value for barrier parameter. $\;$ \\ \paragraph{mu\_min:}\label{sec:mu_min} Minimum value for barrier parameter. $\;$ \\ This option specifies the lower bound on the barrier parameter in the adaptive mu selection mode. By default, it is set to the minimum of 1e-12 and 1e-11 and min("tol","compl\_inf\_tol")/("barrier\_tol\_fact- or"+1), which should be a reasonable value. (Only "adaptive".) The valid range for this real option is $0 < {\tt mu\_min } < {\tt +inf}$ and its default value is $1 \cdot 10^{-12}$. \paragraph{barrier\_tol\_factor:} Factor for mu in barrier stop test. $\;$ \\ and its default value is $1 \cdot 10^{-11}$. \paragraph{barrier\_tol\_factor:}\label{sec:barrier_tol_factor} Factor for mu in barrier stop test. $\;$ \\ The convergence tolerance for each barrier problem in the monotone mode is the value of the \paragraph{mu\_linear\_decrease\_factor:} Determines linear decrease rate of barrier parameter. $\;$ \\ \paragraph{mu\_linear\_decrease\_factor:}\label{sec:mu_linear_decrease_factor} Determines linear decrease rate of barrier parameter. $\;$ \\ For the Fiacco-McCormick update procedure the new barrier parameter mu is obtained by taking the \paragraph{mu\_superlinear\_decrease\_power:} Determines superlinear decrease rate of barrier parameter. $\;$ \\ \paragraph{mu\_superlinear\_decrease\_power:}\label{sec:mu_superlinear_decrease_power} Determines superlinear decrease rate of barrier parameter. $\;$ \\ For the Fiacco-McCormick update procedure the new barrier parameter mu is obtained by taking the \subsection{Multiplier Updates} \paragraph{alpha\_for\_y:} Method to determine the step size for constraint multipliers. $\;$ \\ \paragraph{alpha\_for\_y:}\label{sec:alpha_for_y} Method to determine the step size for constraint multipliers. $\;$ \\ This option determines how the step size (alpha\_y) will be calculated when updating the \item safe\_min\_dual\_infeas: like "min\_dual\_infeas", but safeguarded by "min" and "max" \end{itemize} \paragraph{recalc\_y:} Tells the algorithm to recalculate the equality and inequality multipliers as least square estimates. $\;$ \\ \item primal-and-full: use the primal step size, and full step if delta\_x <= alpha\_for\_y\_tol \item dual-and-full: use the dual step size, and full step if delta\_x <= alpha\_for\_y\_tol \item acceptor: Call LSAcceptor to get step size for y \end{itemize} \paragraph{recalc\_y:}\label{sec:recalc_y} Tells the algorithm to recalculate the equality and inequality multipliers as least square estimates. $\;$ \\ This asks the algorithm to recompute the multipliers, whenever the current infeasibility \end{itemize} \paragraph{recalc\_y\_feas\_tol:} Feasibility threshold for recomputation of multipliers. $\;$ \\ \paragraph{recalc\_y\_feas\_tol:}\label{sec:recalc_y_feas_tol} Feasibility threshold for recomputation of multipliers. $\;$ \\ If recalc\_y is chosen and the current infeasibility is less than this value, then the \subsection{Line Search} \paragraph{max\_soc:} Maximum number of second order correction trial steps at each iteration. $\;$ \\ \paragraph{max\_soc:}\label{sec:max_soc} Maximum number of second order correction trial steps at each iteration. $\;$ \\ Choosing 0 disables the second order corrections. (This is p\^{max} of Step A-5.9 of Algorithm A in implementation paper.) The valid range for this integer option is the implementation paper.) The valid range for this integer option is $0 \le {\tt max\_soc } < {\tt +inf}$ and its default value is $4$. \paragraph{watchdog\_shortened\_iter\_trigger:} Number of shortened iterations that trigger the watchdog. $\;$ \\ \paragraph{watchdog\_shortened\_iter\_trigger:}\label{sec:watchdog_shortened_iter_trigger} Number of shortened iterations that trigger the watchdog. $\;$ \\ If the number of successive iterations in which the backtracking line search did not accept the \paragraph{watchdog\_trial\_iter\_max:} Maximum number of watchdog iterations. $\;$ \\ \paragraph{watchdog\_trial\_iter\_max:}\label{sec:watchdog_trial_iter_max} Maximum number of watchdog iterations. $\;$ \\ This option determines the number of trial iterations allowed before the watchdog procedure \paragraph{corrector\_type:} The type of corrector steps that should be taken (unsupported!). $\;$ \\ \paragraph{corrector\_type:}\label{sec:corrector_type} The type of corrector steps that should be taken (unsupported!). $\;$ \\ If "mu\_strategy" is "adaptive", this option determines what kind of corrector steps should be \subsection{Warm Start} \paragraph{warm\_start\_init\_point:} Warm-start for initial point $\;$ \\ \paragraph{warm\_start\_init\_point:}\label{sec:warm_start_init_point} Warm-start for initial point $\;$ \\ Indicates whether this optimization should use a warm start initialization, where values of primal \end{itemize} \paragraph{warm\_start\_bound\_push:} same as bound\_push for the regular initializer. $\;$ \\ \paragraph{warm\_start\_bound\_push:}\label{sec:warm_start_bound_push} same as bound\_push for the regular initializer. $\;$ \\ The valid range for this real option is $0 < {\tt warm\_start\_bound\_push } < {\tt +inf}$ \paragraph{warm\_start\_bound\_frac:} same as bound\_frac for the regular initializer. $\;$ \\ \paragraph{warm\_start\_bound\_frac:}\label{sec:warm_start_bound_frac} same as bound\_frac for the regular initializer. $\;$ \\ The valid range for this real option is $0 < {\tt warm\_start\_bound\_frac } \le 0.5$ \paragraph{warm\_start\_slack\_bound\_frac:} same as slack\_bound\_frac for the regular initializer. $\;$ \\ \paragraph{warm\_start\_slack\_bound\_frac:}\label{sec:warm_start_slack_bound_frac} same as slack\_bound\_frac for the regular initializer. $\;$ \\ The valid range for this real option is $0 < {\tt warm\_start\_slack\_bound\_frac } \le 0.5$ \paragraph{warm\_start\_slack\_bound\_push:} same as slack\_bound\_push for the regular initializer. $\;$ \\ \paragraph{warm\_start\_slack\_bound\_push:}\label{sec:warm_start_slack_bound_push} same as slack\_bound\_push for the regular initializer. $\;$ \\ The valid range for this real option is $0 < {\tt warm\_start\_slack\_bound\_push } < {\tt +inf}$ \paragraph{warm\_start\_mult\_bound\_push:} same as mult\_bound\_push for the regular initializer. $\;$ \\ \paragraph{warm\_start\_mult\_bound\_push:}\label{sec:warm_start_mult_bound_push} same as mult\_bound\_push for the regular initializer. $\;$ \\ The valid range for this real option is $0 < {\tt warm\_start\_mult\_bound\_push } < {\tt +inf}$ \paragraph{warm\_start\_mult\_init\_max:} Maximum initial value for the equality multipliers. $\;$ \\ \paragraph{warm\_start\_mult\_init\_max:}\label{sec:warm_start_mult_init_max} Maximum initial value for the equality multipliers. $\;$ \\ The valid range for this real option is ${\tt -inf} < {\tt warm\_start\_mult\_init\_max } < {\tt +inf}$ \subsection{Restoration Phase} \paragraph{expect\_infeasible\_problem:} Enable heuristics to quickly detect an infeasible problem. $\;$ \\ \paragraph{expect\_infeasible\_problem:}\label{sec:expect_infeasible_problem} Enable heuristics to quickly detect an infeasible problem. $\;$ \\ This options is meant to activate heuristics that may speed up the infeasibility determination if \end{itemize} \paragraph{expect\_infeasible\_problem\_ctol:} Threshold for disabling "expect\_infeasible\_problem" option. $\;$ \\ \paragraph{expect\_infeasible\_problem\_ctol:}\label{sec:expect_infeasible_problem_ctol} Threshold for disabling "expect\_infeasible\_problem" option. $\;$ \\ If the constraint violation becomes smaller than this threshold, the "expect\_infeasible\_problem" \paragraph{start\_with\_resto:} Tells algorithm to switch to restoration phase in first iteration. $\;$ \\ \paragraph{start\_with\_resto:}\label{sec:start_with_resto} Tells algorithm to switch to restoration phase in first iteration. $\;$ \\ Setting this option to "yes" forces the algorithm to switch to the feasibility restoration phase in \end{itemize} \paragraph{soft\_resto\_pderror\_reduction\_factor:} Required reduction in primal-dual error in the soft restoration phase. $\;$ \\ \paragraph{soft\_resto\_pderror\_reduction\_factor:}\label{sec:soft_resto_pderror_reduction_factor} Required reduction in primal-dual error in the soft restoration phase. $\;$ \\ The soft restoration phase attempts to reduce the primal-dual error with regular steps. If the \paragraph{required\_infeasibility\_reduction:} Required reduction of infeasibility before leaving restoration phase. $\;$ \\ \paragraph{required\_infeasibility\_reduction:}\label{sec:required_infeasibility_reduction} Required reduction of infeasibility before leaving restoration phase. $\;$ \\ The restoration phase algorithm is performed, until a point is found that is acceptable to the \paragraph{bound\_mult\_reset\_threshold:} Threshold for resetting bound multipliers after the restoration phase. $\;$ \\ \paragraph{bound\_mult\_reset\_threshold:}\label{sec:bound_mult_reset_threshold} Threshold for resetting bound multipliers after the restoration phase. $\;$ \\ After returning from the restoration phase, the bound multipliers are updated with a Newton step \paragraph{constr\_mult\_reset\_threshold:} Threshold for resetting equality and inequality multipliers after restoration phase. $\;$ \\ \paragraph{constr\_mult\_reset\_threshold:}\label{sec:constr_mult_reset_threshold} Threshold for resetting equality and inequality multipliers after restoration phase. $\;$ \\ After returning from the restoration phase, the constraint multipliers are recomputed by a least \paragraph{evaluate\_orig\_obj\_at\_resto\_trial:} Determines if the original objective function should be evaluated at restoration phase trial points. $\;$ \\ \paragraph{evaluate\_orig\_obj\_at\_resto\_trial:}\label{sec:evaluate_orig_obj_at_resto_trial} Determines if the original objective function should be evaluated at restoration phase trial points. $\;$ \\ Setting this option to "yes" makes the restoration phase algorithm evaluate the \subsection{Linear Solver} \paragraph{linear\_solver:} Linear solver used for step computations. $\;$ \\ \paragraph{linear\_solver:}\label{sec:linear_solver} Linear solver used for step computations. $\;$ \\ Determines which linear algebra package is to be used for the solution of the augmented linear \item pardiso: use the Pardiso package \item wsmp: use WSMP package \item taucs: use TAUCS package (not yet working) \item mumps: use MUMPS package \item custom: use custom linear solver \end{itemize} \paragraph{linear\_system\_scaling:} Method for scaling the linear system. $\;$ \\ \paragraph{linear\_system\_scaling:}\label{sec:linear_system_scaling} Method for scaling the linear system. $\;$ \\ Determines the method used to compute symmetric scaling factors for the augmented system (see also the "linear\_scaling\_on\_demand" option). This scaling is independentof the NLP problem This scaling is independent of the NLP problem scaling.  By default, MC19 is only used if MA27 or MA57 are selected as linear solvers. This \end{itemize} \paragraph{linear\_scaling\_on\_demand:} Flag indicating that linear scaling is only done if it seems required. $\;$ \\ \paragraph{linear\_scaling\_on\_demand:}\label{sec:linear_scaling_on_demand} Flag indicating that linear scaling is only done if it seems required. $\;$ \\ This option is only important if a linear scaling method (e.g., mc19) is used.  If you choose "no", \end{itemize} \paragraph{max\_refinement\_steps:} Maximum number of iterative refinement steps per linear system solve. $\;$ \\ \paragraph{max\_refinement\_steps:}\label{sec:max_refinement_steps} Maximum number of iterative refinement steps per linear system solve. $\;$ \\ Iterative refinement (on the full unsymmetric system) is performed for each right hand side. \paragraph{min\_refinement\_steps:} Minimum number of iterative refinement steps per linear system solve. $\;$ \\ \paragraph{min\_refinement\_steps:}\label{sec:min_refinement_steps} Minimum number of iterative refinement steps per linear system solve. $\;$ \\ Iterative refinement (on the full unsymmetric system) is performed for each right hand side. \subsection{Hessian Perturbation} \paragraph{max\_hessian\_perturbation:} Maximum value of regularization parameter for handling negative curvature. $\;$ \\ \paragraph{max\_hessian\_perturbation:}\label{sec:max_hessian_perturbation} Maximum value of regularization parameter for handling negative curvature. $\;$ \\ In order to guarantee that the search directions are indeed proper descent directions, Ipopt \paragraph{min\_hessian\_perturbation:} Smallest perturbation of the Hessian block. $\;$ \\ \paragraph{min\_hessian\_perturbation:}\label{sec:min_hessian_perturbation} Smallest perturbation of the Hessian block. $\;$ \\ The size of the perturbation of the Hessian block is never selected smaller than this value, unless \paragraph{first\_hessian\_perturbation:} Size of first x-s perturbation tried. $\;$ \\ \paragraph{first\_hessian\_perturbation:}\label{sec:first_hessian_perturbation} Size of first x-s perturbation tried. $\;$ \\ The first value tried for the x-s perturbation in the inertia correction scheme.(This is delta\_0 \paragraph{perturb\_inc\_fact\_first:} Increase factor for x-s perturbation for very first perturbation. $\;$ \\ \paragraph{perturb\_inc\_fact\_first:}\label{sec:perturb_inc_fact_first} Increase factor for x-s perturbation for very first perturbation. $\;$ \\ The factor by which the perturbation is increased when a trial value was not sufficient - this \paragraph{perturb\_inc\_fact:} Increase factor for x-s perturbation. $\;$ \\ \paragraph{perturb\_inc\_fact:}\label{sec:perturb_inc_fact} Increase factor for x-s perturbation. $\;$ \\ The factor by which the perturbation is increased when a trial value was not sufficient - this \paragraph{perturb\_dec\_fact:} Decrease factor for x-s perturbation. $\;$ \\ \paragraph{perturb\_dec\_fact:}\label{sec:perturb_dec_fact} Decrease factor for x-s perturbation. $\;$ \\ The factor by which the perturbation is decreased when a trial value is deduced from the size of \paragraph{jacobian\_regularization\_value:} Size of the regularization for rank-deficient constraint Jacobians. $\;$ \\ \paragraph{jacobian\_regularization\_value:}\label{sec:jacobian_regularization_value} Size of the regularization for rank-deficient constraint Jacobians. $\;$ \\ (This is bar delta\_c in the implementation paper.) The valid range for this real option is \subsection{Quasi-Newton} \paragraph{hessian\_approximation:} Indicates what Hessian information is to be used. $\;$ \\ \paragraph{hessian\_approximation:}\label{sec:hessian_approximation} Indicates what Hessian information is to be used. $\;$ \\ This determines which kind of information for the Hessian of the Lagrangian function is used by the \end{itemize} \paragraph{limited\_memory\_max\_history:} Maximum size of the history for the limited quasi-Newton Hessian approximation. $\;$ \\ \paragraph{limited\_memory\_max\_history:}\label{sec:limited_memory_max_history} Maximum size of the history for the limited quasi-Newton Hessian approximation. $\;$ \\ This option determines the number of most recent iterations that are taken into account for the \paragraph{limited\_memory\_max\_skipping:} Threshold for successive iterations where update is skipped. $\;$ \\ \paragraph{limited\_memory\_max\_skipping:}\label{sec:limited_memory_max_skipping} Threshold for successive iterations where update is skipped. $\;$ \\ If the update is skipped more than this number of successive iterations, we quasi-Newton \subsection{Derivative Test} \paragraph{derivative\_test:} Enable derivative checker $\;$ \\ \paragraph{derivative\_test:}\label{sec:derivative_test} Enable derivative checker $\;$ \\ If this option is enabled, a (slow) derivative test will be performed before the optimization. \end{itemize} \paragraph{derivative\_test\_perturbation:} Size of the finite difference perturbation in derivative test. $\;$ \\ \paragraph{derivative\_test\_perturbation:}\label{sec:derivative_test_perturbation} Size of the finite difference perturbation in derivative test. $\;$ \\ This determines the relative perturbation of the variable entries. The valid range for this real option is \paragraph{derivative\_test\_tol:} Threshold for indicating wrong derivative. $\;$ \\ \paragraph{derivative\_test\_tol:}\label{sec:derivative_test_tol} Threshold for indicating wrong derivative. $\;$ \\ If the relative deviation of the estimated derivative from the given one is larger than this \paragraph{derivative\_test\_print\_all:} Indicates whether information for all estimated derivatives should be printed. $\;$ \\ \paragraph{derivative\_test\_print\_all:}\label{sec:derivative_test_print_all} Indicates whether information for all estimated derivatives should be printed. $\;$ \\ Determines verbosity of derivative checker. The default value for this string option is "no". \end{itemize} \paragraph{point\_perturbation\_radius:}\label{sec:point_perturbation_radius} Maximal perturbation of an evaluation point. $\;$ \\ If a random perturbation of a points is required, this number indicates the maximal perturbation. This is for example used when determining the center point at which the finite difference derivative test is executed. The valid range for this real option is $0 \le {\tt point\_perturbation\_radius } < {\tt +inf}$ and its default value is $10$. \subsection{MA27 Linear Solver} \paragraph{ma27\_pivtol:} Pivot tolerance for the linear solver MA27. $\;$ \\ \paragraph{ma27\_pivtol:}\label{sec:ma27_pivtol} Pivot tolerance for the linear solver MA27. $\;$ \\ A smaller number pivots for sparsity, a larger number pivots for stability.  This option is only \paragraph{ma27\_pivtolmax:} Maximum pivot tolerance for the linear solver MA27. $\;$ \\ \paragraph{ma27\_pivtolmax:}\label{sec:ma27_pivtolmax} Maximum pivot tolerance for the linear solver MA27. $\;$ \\ Ipopt may increase pivtol as high as pivtolmax to get a more accurate solution to the linear \paragraph{ma27\_liw\_init\_factor:} Integer workspace memory for MA27. $\;$ \\ \paragraph{ma27\_liw\_init\_factor:}\label{sec:ma27_liw_init_factor} Integer workspace memory for MA27. $\;$ \\ The initial integer workspace memory = liw\_init\_factor * memory required by unfactored \paragraph{ma27\_la\_init\_factor:} Real workspace memory for MA27. $\;$ \\ \paragraph{ma27\_la\_init\_factor:}\label{sec:ma27_la_init_factor} Real workspace memory for MA27. $\;$ \\ The initial real workspace memory = la\_init\_factor * memory required by unfactored \paragraph{ma27\_meminc\_factor:} Increment factor for workspace size for MA27. $\;$ \\ \paragraph{ma27\_meminc\_factor:}\label{sec:ma27_meminc_factor} Increment factor for workspace size for MA27. $\;$ \\ If the integer or real workspace is not large enough, Ipopt will increase its size by this \subsection{MA57 Linear Solver} \paragraph{ma57\_pivtol:} Pivot tolerance for the linear solver MA57. $\;$ \\ \paragraph{ma57\_pivtol:}\label{sec:ma57_pivtol} Pivot tolerance for the linear solver MA57. $\;$ \\ A smaller number pivots for sparsity, a larger number pivots for stability. This option is only \paragraph{ma57\_pivtolmax:} Maximum pivot tolerance for the linear solver MA57. $\;$ \\ \paragraph{ma57\_pivtolmax:}\label{sec:ma57_pivtolmax} Maximum pivot tolerance for the linear solver MA57. $\;$ \\ Ipopt may increase pivtol as high as ma57\_pivtolmax to get a more accurate solution \paragraph{ma57\_pre\_alloc:} Safety factor for work space memory allocation for the linear solver MA57. $\;$ \\ \paragraph{ma57\_pre\_alloc:}\label{sec:ma57_pre_alloc} Safety factor for work space memory allocation for the linear solver MA57. $\;$ \\ If 1 is chosen, the suggested amount of work space is used.  However, choosing a larger number \subsection{MUMPS Linear Solver} \paragraph{mumps\_pivtol:} Pivot tolerance for the linear solver MUMPS. $\;$ \\ \paragraph{mumps\_pivtol:}\label{sec:mumps_pivtol} Pivot tolerance for the linear solver MUMPS. $\;$ \\ A smaller number pivots for sparsity, a larger number pivots for stability.  This option is only \paragraph{mumps\_pivtolmax:} Maximum pivot tolerance for the linear solver MUMPS. $\;$ \\ \paragraph{mumps\_pivtolmax:}\label{sec:mumps_pivtolmax} Maximum pivot tolerance for the linear solver MUMPS. $\;$ \\ Ipopt may increase pivtol as high as pivtolmax to get a more accurate solution to the linear \paragraph{mumps\_mem\_percent:} Percentage increase in the estimated working space for MUMPS. $\;$ \\ \paragraph{mumps\_mem\_percent:}\label{sec:mumps_mem_percent} Percentage increase in the estimated working space for MUMPS. $\;$ \\ In MUMPS when significant extra fill-in is caused by numerical pivoting, larger values of \paragraph{mumps\_permuting\_scaling:} Controls permuting and scaling in MUMPS $\;$ \\ \paragraph{mumps\_permuting\_scaling:}\label{sec:mumps_permuting_scaling} Controls permuting and scaling in MUMPS $\;$ \\ This is ICTL(6) in MUMPS. The valid range for this integer option is $0 \le {\tt mumps\_permuting\_scaling } \le 7$ \paragraph{mumps\_pivot\_order:} Controls pivot order in MUMPS $\;$ \\ \paragraph{mumps\_pivot\_order:}\label{sec:mumps_pivot_order} Controls pivot order in MUMPS $\;$ \\ This is ICTL(7) in MUMPS. The valid range for this integer option is $0 \le {\tt mumps\_pivot\_order } \le 7$ \paragraph{mumps\_scaling:} Controls scaling in MUMPS $\;$ \\ \paragraph{mumps\_scaling:}\label{sec:mumps_scaling} Controls scaling in MUMPS $\;$ \\ This is ICTL(8) in MUMPS. The valid range for this integer option is $-2 \le {\tt mumps\_scaling } \le 7$ \subsection{Pardiso Linear Solver} \paragraph{pardiso\_matching\_strategy:} Matching strategy to be used by Pardiso $\;$ \\ \paragraph{pardiso\_matching\_strategy:}\label{sec:pardiso_matching_strategy} Matching strategy to be used by Pardiso $\;$ \\ This is IPAR(13) in Pardiso manual.  This option is only available if Ipopt has been compiled with \end{itemize} \paragraph{pardiso\_out\_of\_core\_power:} Enables out-of-core variant of Pardiso $\;$ \\ \paragraph{pardiso\_out\_of\_core\_power:}\label{sec:pardiso_out_of_core_power} Enables out-of-core variant of Pardiso $\;$ \\ Setting this option to a positive integer k makes Pardiso work in the out-of-core variant where the \subsection{WSMP Linear Solver} \paragraph{wsmp\_num\_threads:} Number of threads to be used in WSMP $\;$ \\ \paragraph{wsmp\_num\_threads:}\label{sec:wsmp_num_threads} Number of threads to be used in WSMP $\;$ \\ This determines on how many processors WSMP is running on.  This option is only available if \paragraph{wsmp\_ordering\_option:} Determines how ordering is done in WSMP $\;$ \\ \paragraph{wsmp\_ordering\_option:}\label{sec:wsmp_ordering_option} Determines how ordering is done in WSMP $\;$ \\ This corresponds to the value of WSSMP's IPARM(16).  This option is only available if \paragraph{wsmp\_pivtol:} Pivot tolerance for the linear solver WSMP. $\;$ \\ \paragraph{wsmp\_pivtol:}\label{sec:wsmp_pivtol} Pivot tolerance for the linear solver WSMP. $\;$ \\ A smaller number pivots for sparsity, a larger number pivots for stability.  This option is only \paragraph{wsmp\_pivtolmax:} Maximum pivot tolerance for the linear solver WSMP. $\;$ \\ \paragraph{wsmp\_pivtolmax:}\label{sec:wsmp_pivtolmax} Maximum pivot tolerance for the linear solver WSMP. $\;$ \\ Ipopt may increase pivtol as high as pivtolmax to get a more accurate solution to the linear \paragraph{wsmp\_scaling:} Determines how the matrix is scaled by WSMP. $\;$ \\ \paragraph{wsmp\_scaling:}\label{sec:wsmp_scaling} Determines how the matrix is scaled by WSMP. $\;$ \\ This corresponds to the value of WSSMP's IPARM(10). This option is only available if Ipopt and its default value is $0$.
• ## trunk/Ipopt/src/Algorithm/IpAlgBuilder.cpp

 r1215 "Determines the method used to compute symmetric scaling " "factors for the augmented system (see also the " "\"linear_scaling_on_demand\" option).  This scaling is independent" "\"linear_scaling_on_demand\" option).  This scaling is independent " "of the NLP problem scaling.  By default, MC19 is only used if MA27 or " "MA57 are selected as linear solvers. This option is only available if "
• ## trunk/Ipopt/src/Algorithm/IpBacktrackingLineSearch.cpp

 r1121 // Copyright (C) 2004, 2007 International Business Machines and others. // Copyright (C) 2004, 2008 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. "min_dual_infeas", "choose step size minimizing new dual infeasibility", "safe_min_dual_infeas", "like \"min_dual_infeas\", but safeguarded by \"min\" and \"max\"", "primal-and-full", "use the primal step size, and full step if delta_x <= alpga_for_y_tol", "dual-and-full", "use the dual step size, and full step if delta_x <= alpga_for_y_tol", "primal-and-full", "use the primal step size, and full step if delta_x <= alpha_for_y_tol", "dual-and-full", "use the dual step size, and full step if delta_x <= alpha_for_y_tol", "acceptor", "Call LSAcceptor to get step size for y", "This option determines how the step size (alpha_y) will be calculated when updating the "
• ## trunk/Ipopt/src/Algorithm/IpDefaultIterateInitializer.cpp

 r1213 "are initialized to the value of \"bound_mult_init_val\".  If " "\"mu-based\" is chosen, the each value is initialized to the the value " "of \"mu_init\" devided by the corresponding slack variable.  This " "of \"mu_init\" divided by the corresponding slack variable.  This " "latter option might be useful if the starting point is close to the " "optimal solution."); "If set to yes, Ipopt ignores the user provided point and solves a " "least square problem for the primal variables (x and s), to fit the " "linearize equality and inequality constraints.  This might be useful " "linearized equality and inequality constraints.  This might be useful " "if the user doesn't know anything about the starting point, or for " "solving an LP or QP.");
• ## trunk/Ipopt/src/Algorithm/IpIpoptAlg.cpp

 r1156 "adaptive mu strategy with the \"probing\" oracle, and uses " "\"corrector_type=affine\" without any safeguards; you should not set " "any of those options explicitly in addition.  Also, unless" "any of those options explicitly in addition.  Also, unless " "otherwise specified, the values of \"bound_push\", \"bound_frac\", and " "\"bound_mult_init_val\" are set more aggressive, and sets " } IpData().TimingStats().OverallAlgorithm().End(); return SUCCESS; return FEASIBLE_POINT_FOUND; } catch (TOO_FEW_DOF& exc) {
• ## trunk/Ipopt/src/Algorithm/IpOrigIpoptNLP.cpp

 r1173 "yes", "Assume this is problem has known structure", "If \"yes\" is chosen, then the algorithm assumes that an NLP is now to " "be solved, whose strcture is identical to one that already was " "be solved, whose structure is identical to one that already was " "considered (with the same NLP object)."); roptions->SetRegisteringCategory("NLP");
• ## trunk/Ipopt/src/Algorithm/IpPDFullSpaceSolver.cpp

 r1218 roptions->AddLowerBoundedNumberOption( "neg_curv_test_tol", "Tolerance for heurstic to ignore wrong inertia.", "Tolerance for heuristic to ignore wrong inertia.", 0.0, true, 0.0, "If positive, incorrect inertia in the augmented system is ignored, and "
• ## trunk/Ipopt/src/Algorithm/IpPenaltyLSAcceptor.cpp

 r1212 roptions->AddLowerBoundedNumberOption( "nu_inc", "Incrememt of the penalty parameter.", "Increment of the penalty parameter.", 0.0, true, 1e-4, "");
• ## trunk/Ipopt/src/Algorithm/IpRestoMinC_1Nrm.cpp

 r1019 // Copyright (C) 2004, 2007 International Business Machines and others. // Copyright (C) 2004, 2008 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. prefix); // This is registered in OptimalityErrorConvergenceCheck options.GetNumericValue("constr_viol_tol", constr_viol_tol_, prefix); // Avoid that the restoration phase is trigged by user option in // first iteration of the restoration phase // sufficiently feasible point has been found if (square_problem) { Jnlst().Printf(J_DETAILED, J_LINE_SEARCH, "Recursive restoration phase algorithm termined successfully for square problem.\n"); IpData().AcceptTrialPoint(); THROW_EXCEPTION(FEASIBILITY_PROBLEM_SOLVED, "Restoration phase converged to sufficiently feasible point of original square problem."); Number constr_viol = IpCq().unscaled_curr_nlp_constraint_violation(NORM_MAX); if (constr_viol <= constr_viol_tol_) { Jnlst().Printf(J_DETAILED, J_LINE_SEARCH, "Recursive restoration phase algorithm termined successfully for square problem.\n"); IpData().AcceptTrialPoint(); THROW_EXCEPTION(FEASIBILITY_PROBLEM_SOLVED, "Restoration phase converged to sufficiently feasible point of original square problem."); } }
• ## trunk/Ipopt/src/Algorithm/IpRestoMinC_1Nrm.hpp

 r735 // Copyright (C) 2004, 2006 International Business Machines and others. // Copyright (C) 2004, 2008 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. *  the first time the restoration phase is called.  (ToDo) */ bool expect_infeasible_problem_; /** Constraint violation tolerance */ Number constr_viol_tol_; //@}
• ## trunk/Ipopt/src/Algorithm/LinearSolvers/IpMa27TSolverInterface.cpp

 r1217 // Copyright (C) 2004, 2006 International Business Machines and others. // Copyright (C) 2004, 2008 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. roptions->AddStringOption2( "ma27_skip_inertia_check", "Always pretent inertia is correct.", "Always pretend inertia is correct.", "no", "no", "check interia", "no", "check inertia", "yes", "skip inertia check", "Setting this option to \"yes\" essentially disables inertia check. " "This option makes the algorithm non-robust and easily fail, but it " "might give some insight into the necessity of interia control."); "might give some insight into the necessity of inertia control."); roptions->AddStringOption2( "ma27_ignore_singularity",
• ## trunk/Ipopt/src/Algorithm/LinearSolvers/IpPardisoSolverInterface.cpp

 r1161 // Copyright (C) 2005, 2007 International Business Machines and others. // Copyright (C) 2005, 2008 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. roptions->AddStringOption2( "pardiso_redo_symbolic_fact_only_if_inertia_wrong", "Toggel for handling case when elements were pertured by Pardiso.", "Toggle for handling case when elements were perturbed by Pardiso.", "no", "no", "Always redo symbolic factorization when elements were perturbed", "Always pretent inertia is correct.", "no", "no", "check interia", "no", "check inertia", "yes", "skip inertia check", "Setting this option to \"yes\" essentially disables inertia check. " "This option makes the algorithm non-robust and easily fail, but it " "might give some insight into the necessity of interia control."); "might give some insight into the necessity of inertia control."); roptions->AddIntegerOption( "pardiso_iter_tol_exponent",
• ## trunk/Ipopt/src/Algorithm/LinearSolvers/IpWsmpSolverInterface.cpp

 r1019 // Copyright (C) 2005, 2007 International Business Machines and others. // Copyright (C) 2005, 2008 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. "Iteration in which the matrices are written to files.", -1, -1, "If non-negative, this option determins the iteration in which all " "If non-negative, this option determines the iteration in which all " "matrices given to WSMP are written to files.  This option is only " "available if Ipopt has been compiled with WSMP.");
• ## trunk/Ipopt/src/Apps/AmplSolver/AmplTNLP.cpp

 r1156 solve_result_num = 1; } else if (status == FEASIBLE_POINT_FOUND) { message = "Found feasible point for square problem."; solve_result_num = 2; } else if (status == LOCAL_INFEASIBILITY) { message = "Converged to a locally infeasible point. Problem may be infeasible.";
• ## trunk/Ipopt/src/Interfaces/IpAlgTypes.hpp

 r1191 LOCAL_INFEASIBILITY, USER_REQUESTED_STOP, FEASIBLE_POINT_FOUND, DIVERGING_ITERATES, RESTORATION_FAILURE,
• ## trunk/Ipopt/src/Interfaces/IpIpoptApplication.cpp

 r1216 options_to_print.push_back("bound_mult_init_val"); options_to_print.push_back("constr_mult_init_max"); options_to_print.push_back("bound_mult_init_method"); options_to_print.push_back("bound_mult_init_val"); options_to_print.push_back("derivative_test_tol"); options_to_print.push_back("derivative_test_print_all"); options_to_print.push_back("point_perturbation_radius"); // Special linear solver jnlst_->Printf(J_SUMMARY, J_MAIN, "\nEXIT: Solved To Acceptable Level.\n"); } else if (status == FEASIBLE_POINT_FOUND) { retValue = Feasible_Point_Found; jnlst_->Printf(J_SUMMARY, J_MAIN, "\nEXIT: Feasible point for square problem found.\n"); } else if (status == DIVERGING_ITERATES) { retValue = Diverging_Iterates;
• ## trunk/Ipopt/src/Interfaces/IpReturnCodes.inc

 r909 INTEGER IP_USER_REQUESTED_STOP PARAMETER( IP_USER_REQUESTED_STOP = 5 ) INTEGER IP_FEASIBLE_POINT_FOUND PARAMETER( IP_FEASIBLE_POINT_FOUND = 6 ) INTEGER IP_ITERATION_EXCEEDED
• ## trunk/Ipopt/src/Interfaces/IpReturnCodes_inc.h

 r1175 # From configure.ac 0.9. # Guess values for system-dependent variables and create Makefiles. # Generated by GNU Autoconf 2.59 for Ipopt 3.3dev. # Generated by GNU Autoconf 2.59 for Ipopt 3.4trunk. # # Report bugs to . # # # Copyright 2006, 2007 International Business Machines and others. # Copyright 2007, 2008 International Business Machines and others. # All Rights Reserved. # This file is part of the open source package Coin which is distributed PACKAGE_NAME='Ipopt' PACKAGE_TARNAME='ipopt' PACKAGE_VERSION='3.3dev' PACKAGE_STRING='Ipopt 3.3dev' PACKAGE_VERSION='3.4trunk' PACKAGE_STRING='Ipopt 3.4trunk' PACKAGE_BUGREPORT='http://projects.coin-or.org/Ipopt/newticket' # This message is too long to be a string in the A/UX 3.1 sh. cat <<_ACEOF \configure' configures Ipopt 3.3dev to adapt to many kinds of systems. \configure' configures Ipopt 3.4trunk to adapt to many kinds of systems. Usage: $0 [OPTION]... [VAR=VALUE]... if test -n "$ac_init_help"; then case $ac_init_help in short | recursive ) echo "Configuration of Ipopt 3.3dev:";; short | recursive ) echo "Configuration of Ipopt 3.4trunk:";; esac cat <<\_ACEOF if$ac_init_version; then cat <<\_ACEOF Ipopt configure 3.3dev Ipopt configure 3.4trunk generated by GNU Autoconf 2.59 Copyright 2006, 2007 International Business Machines and others. Copyright 2007, 2008 International Business Machines and others. All Rights Reserved. This file is part of the open source package Coin which is distributed running configure, to aid debugging if configure makes a mistake. It was created by Ipopt $as_me 3.3dev, which was It was created by Ipopt$as_me 3.4trunk, which was generated by GNU Autoconf 2.59.  Invocation command line was # Define the identity of the package. PACKAGE='ipopt' VERSION='3.3dev' VERSION='3.4trunk' cat >&5 <<_CSEOF This file was extended by Ipopt $as_me 3.3dev, which was This file was extended by Ipopt$as_me 3.4trunk, which was generated by GNU Autoconf 2.59.  Invocation command line was cat >>$CONFIG_STATUS <<_ACEOF ac_cs_version="\\ Ipopt config.status 3.3dev Ipopt config.status 3.4trunk configured by$0, generated by GNU Autoconf 2.59, with options \\"echo "$ac_configure_args" | sed 's/[\\""\\$]/\\\\&/g'\\"