use default empty parameters in solve function

Author: jurgen-lentz

docs/source/class-structure.rst

@@ -258,7 +258,7 @@ The currently defined entities are obtained from the various get methods of the
 (see section :ref:`secAMPLClass`). Once a reference to an entity is created, the entity is automatically kept up-to-date
 with the corresponding entity in the AMPL interpreter. That is, if a reference to a newly created AMPL variable
 is obtained by means of :meth:`.AMPL.getVariable()`, and the model the variable is part of is then solved
-by means of :meth:`.AMPL.solve("", "")`, the values of the instances of the variable will automatically be updated.
+by means of :meth:`.AMPL.solve()`, the values of the instances of the variable will automatically be updated.
 The following code snippet should demonstrate the concept.
 
 .. code-block:: R
@@ -271,7 +271,7 @@ The following code snippet should demonstrate the concept.
    # At this point x$value() evaluates to 0
    print(x$value())  # prints 0
 
-   ampl$solve("", "")
+   ampl$solve()
 
    # At this point x$value() evaluates to 10
    print(x$value())  # prints 10

docs/source/quick-start.rst

@@ -30,7 +30,7 @@ This is the complete listing of the example. Please note that, for clarity of pr
   ampl$readData("models/diet.dat")
 
   # Solve
-  ampl$solve("", "")
+  ampl$solve()
 
   # Get objective entity by AMPL name
   totalcost <- ampl$getObjective("Total_Cost")
@@ -43,7 +43,7 @@ This is the complete listing of the example. Please note that, for clarity of pr
   cat("Increased costs of beef and ham.\n")
 
   # Resolve and display objective
-  ampl$solve("", "")
+  ampl$solve()
   cat(sprintf("New objective value: %g\n", totalcost$value()))
 
   # Reassign data - all instances
@@ -52,7 +52,7 @@ This is the complete listing of the example. Please note that, for clarity of pr
   cat("Updated all costs.\n")
 
   # Resolve and display objective
-  ampl$solve("", "")
+  ampl$solve()
   cat(sprintf("New objective value: %g\n", totalcost$value()))
 
   # Get the values of the variable Buy in a dataframe object
@@ -123,7 +123,7 @@ To solve the currently loaded problem instance, it is sufficient to issue the co
 
 .. code-block:: R
 
-   ampl$solve("", "")
+   ampl$solve()
 
 
 Get an AMPL entity in the programming environment (get objective value)
@@ -175,7 +175,7 @@ values is overloaded, and is in both cases :meth:`Parameter.setValues()`.
    cost <- ampl$getParameter("cost")
    cost$setValues(data.frame(index=c("BEEF", "HAM"), value=c(5.01, 4.55)))
    cat("Increased costs of beef and ham.\n")
-   ampl$solve("", "")
+   ampl$solve()
    cat(sprintf("New objective value: %g\n", totalcost$value()))
 
 The code above assigns the values 5.01 and 4.55 to the parameter cost for the objects beef and ham respectively.
@@ -186,7 +186,7 @@ both the index and the value. A collection of values is assigned to each of the
 
    cost$setValues(c(3, 5, 5, 6, 1, 2, 5.01, 4.55))
    cat("Updated all costs.\n")
-   ampl$solve("", "")
+   ampl$solve()
    cat(sprintf("New objective value: %g\n", totalcost$value()))
 
 The statements above produce the following output::

examples/dietmodel.R

@@ -47,7 +47,7 @@ dietmodel <- function(solver=NULL, modelDirectory=NULL) {
   ampl$setData(df, 2, "")
 
   # Solve the model
-  ampl$solve("", "")
+  ampl$solve()
 
   # Print out the result
   cat(sprintf("Objective: %f\n", ampl$getObjective("Total_Cost")$value()))

examples/efficientfrontier.R

@@ -43,7 +43,7 @@ efficientfrontier <- function(solver=NULL, modelDirectory=NULL) {
   # Relax the integrality
   ampl$setOption("relax_integrality", TRUE)
   # Solve the problem
-  ampl$solve("", "")
+  ampl$solve()
   # Calibrate the efficient frontier range
   minret <- portfolioReturn$value()
   values <- averageReturn$getValues()
@@ -60,14 +60,14 @@ efficientfrontier <- function(solver=NULL, modelDirectory=NULL) {
     ampl$eval("let stockopall:={};let stockrun:=stockall;")
     # Relax integrality
     ampl$setOption("relax_integrality", TRUE)
-    ampl$solve("", "")
+    ampl$solve()
     cat(sprintf("QP result = %g\n", variance$value()))
     # Adjust included stocks
     ampl$eval("let stockrun:={i in stockrun:weights[i]>0};")
     ampl$eval("let stockopall:={i in stockrun:weights[i]>0.5};")
     # Set integrality back
     ampl$setOption("relax_integrality", FALSE)
-    ampl$solve("", "")
+    ampl$solve()
     cat(sprintf("QMIP result = %g\n", variance$value()))
     # Store data of corrent frontier point
     returns <- c(returns, maxret - (i - 1) * stepsize)

examples/firstexample.R

@@ -19,7 +19,7 @@ firstexample <- function(solver=NULL, modelDirectory=NULL) {
   ampl$readData(paste(modelDirectory, "/diet/diet.dat", sep=""))
 
   # Solve
-  ampl$solve("", "")
+  ampl$solve()
 
   # Get objective entity by AMPL name
   totalcost <- ampl$getObjective("Total_Cost")
@@ -34,7 +34,7 @@ firstexample <- function(solver=NULL, modelDirectory=NULL) {
   cat(sprintf("Increased costs of beef and ham.\n"))
 
   # Resolve and display objective
-  ampl$solve("", "")
+  ampl$solve()
   cat(sprintf("New objective value: %f\n", totalcost$value()))
 
   # Reassign data - all instances
@@ -43,7 +43,7 @@ firstexample <- function(solver=NULL, modelDirectory=NULL) {
   cat(sprintf("Updated all costs.\n"))
 
   # Resolve and display objective
-  ampl$solve("", "")
+  ampl$solve()
   cat(sprintf("New objective value: %f\n", totalcost$value()))
 
   # Get the values of the variable Buy in a dataframe object

examples/trackingmodel.R

@@ -36,7 +36,7 @@ trackingmodel <- function(solver=NULL, modelDirectory=NULL) {
   # Relax the integrality
   ampl$setOption("relax_integrality", TRUE)
   # Solve the problem
-  ampl$solve("", "")
+  ampl$solve()
   cat(sprintf("QP objective value ", ampl$getObjectives()[[1]]$value()))
 
   lowcutoff <- 0.04
@@ -62,6 +62,6 @@ trackingmodel <- function(solver=NULL, modelDirectory=NULL) {
   # Get back to the integer problem
   ampl$setOption("relax_integrality", FALSE)
   # Solve the (integer) problem
-  ampl$solve("", "")
+  ampl$solve()
   cat(sprintf("QMIP objective value %g\n", ampl$getObjectives()[[1]]$value()))
 }

src/rampl.cpp

@@ -318,8 +318,17 @@ bool RAMPL::isRunning() const {
   :param string solver: The solver that will be used to solve the problem.
   :raises Error: If the underlying interpreter is not running.
 */
-void RAMPL::solve(std::string problem, std::string solver) {
-  _impl.solve(problem, solver);
+void RAMPL::solve(Rcpp::Nullable<std::string> problem = R_NilValue, Rcpp::Nullable<std::string> solver = R_NilValue) {
+  if (problem.isNotNull() && solver.isNotNull())
+    _impl.solve(problem, solver);
+  else if (problem.isNotNull())
+    _impl.solve(problem, "");
+  else if (solver.isNotNull())
+    _impl.solve("", solver);
+  else
+    _impl.solve("", "")
+  
+  
   //return _impl.solve("", ""); // FIXME: does not print to stdout with R IDE on Windows
 }