remove trailing whitespace

Author: svigerske

AUTHORS

@@ -111,21 +111,21 @@ Contributors:
   R interface with examples, all files in directory (and subdirectory of)
 
   contrib/RInterface
-  
+
 - Tony Kelman (Berkeley)
   improvements to Matlab interface
-  
+
   Ipopt/contrib/MatlabInterface
 
 - Gabriel Hackebeil
   improved use compound component spaces
-  
+
 - Nai-Yuan Chiang and Victor M. Zavala Tejeda (Argonne National Laboratory)
   inertia free curvature test in solution of primal-dual system (full approach)
 
 - Brad Bell
   recursive NLP example
-  
+
   examples/recursive_nlp/recursive_nlp.cpp
 
 - Mitchell Clement

ChangeLog

@@ -439,7 +439,7 @@ commit history.
         - fix a memory allocation in Java interface in cases where jint
           has a different size than int [r2513]
         - the buildsystem now tries the Accelerate framework instead of
-          vecLib for finding Blas/Lapack on MacOS X 
+          vecLib for finding Blas/Lapack on MacOS X
 
 2014-08-16 releases/3.11.9
         - fix compilation issue of Java interface on systems where Index
@@ -583,12 +583,12 @@ commit history.
           of internally within the mex code (replace Matlab call to ipopt with
           ipopt_auxdata if using auxiliary data in any callbacks) [r2282]
         - exposed more intermediate Ipopt information to iterfunc callback [r2283]
-        
+
         - fixes to JIpopt buildsystem (now may work on windows and uses libtool)
         - JIpopt now reads options file ipopt.opt by default, if present
         - removed predefined KEY_ strings in JIpopt
         - renamed API functions that retrieve solution values in JIpopt
-        
+
         - simplified installation of R interface
 
 2013-05-05 releases/3.10.4
@@ -796,7 +796,7 @@ commit history.
             (no longer in include/ipopt)
         - default for dual_inf_tol is now 1 (instead of 1e-4)
         - In matlab interface, here the text from Peter Carbonetto:
-          There have been several significant changes made to the MATLAB interface since the last release. The most important two changes are: 1) Following the "warm start" feature of IPOPT, you may pass in initial estimates for the Lagrange multipliers. 2) Callback routines for computing the objective, gradient (etc.) are now specified using function handles instead of strings. (Thanks to Marcus Brubaker at the University of Toronto for the initial suggestion.) 
+          There have been several significant changes made to the MATLAB interface since the last release. The most important two changes are: 1) Following the "warm start" feature of IPOPT, you may pass in initial estimates for the Lagrange multipliers. 2) Callback routines for computing the objective, gradient (etc.) are now specified using function handles instead of strings. (Thanks to Marcus Brubaker at the University of Toronto for the initial suggestion.)
 
 2007-09-25 releases/3.3.3
         - minor changes, bug fixes

configure.ac

@@ -357,7 +357,7 @@ fi
 if test "$enable_java" != no ; then
   # the following macros can make configure stop with an error
   # we could work around that, but having javac and no jar, java, or javadoc would be odd anyway
-  
+
   # look for jar: required to pack Java interface
   AX_PROG_JAR
   # look for more java to run tests and examples and do documentation
@@ -577,7 +577,7 @@ if test "$use_sipopt" = yes ; then
 fi
 
 # under Windows, the Makevars file for the R Interface need to be named Makevars.win
-case $build in 
+case $build in
   *-cygwin* | *-mingw* | *-msys* )
     AC_CONFIG_FILES([contrib/RInterface/src/Makevars.win:contrib/RInterface/src/Makevars.in])
     ;;

contrib/RInterface/CHANGELOG

@@ -13,9 +13,9 @@
             R> res$constraints
             R> res$lambda
 
-    * Removed ipoptr_environment as argument in ipoptr because it wasn't useful and it caused undesired behaviour in 
+    * Removed ipoptr_environment as argument in ipoptr because it wasn't useful and it caused undesired behaviour in
       combination with the data.table package (thanks to Florian Oswald for reporting).
-      
+
 20 November 2011, version 0.8.3:
 
     * Added #include <assert.h> to src/IpoptRNLP.hpp

contrib/RInterface/R/get.option.types.R

@@ -5,8 +5,8 @@
 # Author: Jelmer Ypma
 # Date:   18 April 2010
 #
-# This function converts a list with ipopt options into 
-# three sub-lists, where the options are sorted into 
+# This function converts a list with ipopt options into
+# three sub-lists, where the options are sorted into
 # the different value types (integer, numeric, string).
 #
 # Input: list of ipopt options and their values
@@ -175,14 +175,14 @@ get.option.types <- function(opts) {
               "wsmp_singularity_threshold"="numeric"
     )
 
- 
+
 
     # initialize list with options sorted by type
     converted.opts <- list( "integer"=list(), "string"=list(), "numeric"=list() )
 
     is.wholenumber <- function(x, tol = .Machine$double.eps^0.5)  abs(x - round(x)) < tol
 
-    # check if we have at least 1 element in the list, otherwise the 
+    # check if we have at least 1 element in the list, otherwise the
     # loop runs from 1 to down 0 and we get errors
     if ( length( opts ) > 0 ) {
 
@@ -200,7 +200,7 @@ get.option.types <- function(opts) {
                 }
                 cat( paste( "Warning: ", names(opts)[i], " is not a recognized option, we try to pass it to Ipopt as ", tmp.type, "\n" ) )
             }
-            
+
             if ( tmp.type=="string" ) {
                 converted.opts$string[[ names(opts)[i] ]] <- as.character(opts[[i]])
             } else if ( tmp.type=="integer" ) {

contrib/RInterface/R/ipoptr.R

@@ -7,10 +7,10 @@
 #
 # Changelog:
 #   09/03/2012: Added outputs, z_L, z_U, constraints, lambda (thanks to Michael Schedl)
-#   09/03/2012: Removed ipoptr_environment because this caused a bug in combination with 
+#   09/03/2012: Removed ipoptr_environment because this caused a bug in combination with
 #               data.table and it wasn't useful (thanks to Florian Oswald for reporting)
 #
-# Input: 
+# Input:
 #    x0 : vector with initial values
 #    eval_f : function to evaluate objective function
 #    eval_grad_f : function to evaluate gradient of objective function
@@ -39,39 +39,39 @@
 #    lambda      : final values for the Lagrange mulipliers
 
 ipoptr <-
-function( x0, 
-          eval_f, 
-          eval_grad_f, 
-          lb = NULL, 
-          ub = NULL, 
-          eval_g = function( x ) { return( numeric(0) ) }, 
-          eval_jac_g = function( x ) { return( numeric(0) ) }, 
+function( x0,
+          eval_f,
+          eval_grad_f,
+          lb = NULL,
+          ub = NULL,
+          eval_g = function( x ) { return( numeric(0) ) },
+          eval_jac_g = function( x ) { return( numeric(0) ) },
           eval_jac_g_structure = list(),
-          constraint_lb = numeric(0), 
+          constraint_lb = numeric(0),
           constraint_ub = numeric(0),
           eval_h = NULL,
           eval_h_structure = NULL,
           opts = list(),
           ... ) {
-    
+
     # define 'infinite' lower and upper bounds of the control if they haven't been set
     if ( is.null( lb ) ) { lb <- rep( -Inf, length(x0) ) }
     if ( is.null( ub ) ) { ub <- rep(  Inf, length(x0) ) }
 
     # internal function to check the arguments of the functions
     checkFunctionArguments <- function( fun, arglist, funname ) {
         if( !is.function(fun) ) stop(paste(funname, " must be a function\n", sep = ""))
-        
+
         # determine function arguments
         fargs <- formals(fun)
-        
+
         if ( length(fargs) > 1 ) {
             # determine argument names user-defined function
             argnames_udf <- names(fargs)[2:length(fargs)]    # remove first argument, which is x
-            
+
             # determine argument names that where supplied to ipoptr()
             argnames_supplied <- names(arglist)
-            
+
             # determine which arguments where required but not supplied
             m1 = match(argnames_udf, argnames_supplied)
             if( any(is.na(m1)) ){
@@ -80,7 +80,7 @@ function( x0,
                     stop(paste(funname, " requires argument '", argnames_udf[mx1], "' but this has not been passed to the 'ipoptr' function.\n", sep = ""))
                 }
             }
-            
+
             # determine which arguments where supplied but not required
             m2 = match(argnames_supplied, argnames_udf)
             if( any(is.na(m2)) ){
@@ -92,28 +92,28 @@ function( x0,
         }
         return( 0 )
     }
-    
+
     # extract list of additional arguments and check user-defined functions
     arglist <- list(...)
     checkFunctionArguments( eval_f, arglist, 'eval_f' )
     checkFunctionArguments( eval_grad_f, arglist, 'eval_grad_f' )
-    
+
     num.constraints <- length( constraint_lb )
     if ( num.constraints > 0 ) {
         checkFunctionArguments( eval_g, arglist, 'eval_g' )
         checkFunctionArguments( eval_jac_g, arglist, 'eval_jac_g' )
     }
-    
+
     # write wrappers around user-defined functions to pass additional arguments
     eval_f_wrapper = function(x){ eval_f(x, ...) }
     eval_grad_f_wrapper = function(x){ eval_grad_f(x, ...) }
-    
+
     if ( num.constraints > 0 ) {
         eval_g_wrapper = function( x ) { eval_g(x, ...) }
         eval_jac_g_wrapper = function( x ) { eval_jac_g(x, ...) }
     } else {
-        eval_g_wrapper = function( x ) { return( numeric(0) ) } 
-        eval_jac_g_wrapper = function( x ) { return( numeric(0) ) } 
+        eval_g_wrapper = function( x ) { return( numeric(0) ) }
+        eval_jac_g_wrapper = function( x ) { return( numeric(0) ) }
     }
 
     # approximate Hessian
@@ -127,32 +127,32 @@ function( x0,
 
 
     # build ipoptr object
-    ret <- list( "x0"=x0, 
-                 "eval_f"=eval_f_wrapper, 
-                 "eval_grad_f"=eval_grad_f_wrapper, 
-                 "lower_bounds"=lb, 
-                 "upper_bounds"=ub, 
-                 "eval_g"=eval_g_wrapper, 
-                 "eval_jac_g"=eval_jac_g_wrapper, 
-                 "constraint_lower_bounds"=constraint_lb, 
-                 "constraint_upper_bounds"=constraint_ub, 
+    ret <- list( "x0"=x0,
+                 "eval_f"=eval_f_wrapper,
+                 "eval_grad_f"=eval_grad_f_wrapper,
+                 "lower_bounds"=lb,
+                 "upper_bounds"=ub,
+                 "eval_g"=eval_g_wrapper,
+                 "eval_jac_g"=eval_jac_g_wrapper,
+                 "constraint_lower_bounds"=constraint_lb,
+                 "constraint_upper_bounds"=constraint_ub,
                  "eval_jac_g_structure"=eval_jac_g_structure,
                  "eval_h"=eval_h_wrapper,
                  "eval_h_structure"=eval_h_structure,
                  "options"=get.option.types(opts),
                  "environment" = new.env() )
-    
+
     attr(ret, "class") <- "ipoptr"
-    
+
     # add the current call to the list
     ret$call <- match.call()
-    
+
     # check whether we have a correctly formed ipoptr object
     is.ipoptr( ret )
-    
+
     # pass ipoptr object to C code
     solution <- .Call( IpoptRSolve, ret )
-    
+
     # add solution variables to object
     ret$status      <- solution$status
     ret$message     <- solution$message
@@ -163,6 +163,6 @@ function( x0,
     ret$z_U         <- solution$z_U
     ret$constraints <- solution$constraints
     ret$lambda      <- solution$lambda
-    
+
     return( ret )
 }

contrib/RInterface/R/is.ipoptr.R

@@ -9,69 +9,69 @@
 # Output: bool telling whether the object is an ipoptr or not
 #
 # Changelog:
-#   09/03/2012: Removed ipoptr_environment because this caused a bug in combination with 
+#   09/03/2012: Removed ipoptr_environment because this caused a bug in combination with
 #               data.table and it wasn't useful (thanks to Florian Oswald for reporting)
 
 is.ipoptr <- function(x) {
-    
+
     # Check whether the object exists and is a list
     if( is.null(x) ) { return( FALSE ) }
     if( !is.list(x) ) { return( FALSE ) }
-   
+
     # Define local flag defining whether we approximate the Hessian or not
     flag_hessian_approximation = FALSE
     if ( !is.null( x$options$string$hessian_approximation ) ) {
         flag_hessian_approximation = ( x$options$string$hessian_approximation == "limited-memory" )
     }
-    
+
     # Check whether the needed functions are supplied
     stopifnot( is.function(x$eval_f) )
     stopifnot( is.function(x$eval_grad_f) )
     stopifnot( is.function(x$eval_g) )
     stopifnot( is.function(x$eval_jac_g) )
     if ( !flag_hessian_approximation ) { stopifnot( is.function(x$eval_h) ) }
-    
+
     # Check whether bounds are defined for all controls
     stopifnot( length( x$x0 ) == length( x$lower_bounds ) )
     stopifnot( length( x$x0 ) == length( x$upper_bounds ) )
-    
+
     # Check whether the initial value is within the bounds
     stopifnot( all( x$x0 >= x$lower_bounds ) )
     stopifnot( all( x$x0 <= x$upper_bounds ) )
-    
+
     num.controls <- length( x$x0 )
     num.constraints <- length( x$constraint_lower_bounds )
-    
+
     # Check the length of some return values
     stopifnot( length(x$eval_f( x$x0 ))==1 )
     stopifnot( length(x$eval_grad_f( x$x0 ))==num.controls )
     stopifnot( length(x$eval_g( x$x0 ))==num.constraints )
     stopifnot( length(x$eval_jac_g( x$x0 ))==length(unlist(x$eval_jac_g_structure)) )  # the number of non-zero elements in the Jacobian
-    if ( !flag_hessian_approximation ) { 
+    if ( !flag_hessian_approximation ) {
         stopifnot( length(x$eval_h( x$x0, 1, rep(1,num.constraints) ))==length(unlist(x$eval_h_structure)) )  # the number of non-zero elements in the Hessian
     }
-    
+
     # Check the whether we don't have NA's in initial values
     stopifnot( all(!is.na(x$eval_f( x$x0 ))) )
     stopifnot( all(!is.na(x$eval_grad_f( x$x0 ))) )
     stopifnot( all(!is.na(x$eval_g( x$x0 ))) )
     stopifnot( all(!is.na(x$eval_jac_g( x$x0 ))) )  # the number of non-zero elements in the Jacobian
-    if ( !flag_hessian_approximation ) { 
+    if ( !flag_hessian_approximation ) {
         stopifnot( all(!is.na(x$eval_h( x$x0, 1, rep(1,num.constraints) ))) )  # the number of non-zero elements in the Hessian
     }
-    
+
     # Check whether a correct structure was supplied, and check the size
     stopifnot( is.list(x$eval_jac_g_structure) )
-    
+
     stopifnot( length(x$eval_jac_g_structure)==num.constraints )
     if ( !flag_hessian_approximation ) {
         stopifnot( length(x$eval_h_structure)==num.controls )
         stopifnot( is.list(x$eval_h_structure) )
     }
-    
+
     # Check the number of non-linear constraints
     stopifnot( length(x$constraint_lower_bounds)==length(x$constraint_upper_bounds) )
-    
+
     # Check whether none of the non-zero indices are larger than the number of controls
     # Also, the smallest index should be bigger than 0
     if ( length( x$eval_jac_g_structure ) > 0 ) {
@@ -82,7 +82,7 @@ is.ipoptr <- function(x) {
         stopifnot( max(unlist(x$eval_h_structure)) <= num.controls )
         stopifnot( min(unlist(x$eval_h_structure)) > 0 )
     }
-    
+
     # Check whether option to approximate hessian and eval_h are both set
     # If we approximate the hessian, then we don't want to set eval_h
     if ( flag_hessian_approximation ) {
@@ -93,7 +93,7 @@ is.ipoptr <- function(x) {
             warning("Option supplied to approximate hessian, but eval_h_structure is defined.\nSolution: remove option hessian_approximation=limited-memory to use analytic derivatives.")
         }
     }
-    
+
 
     return( TRUE )
 }