As an example of the use of the library functions, Figure 3.1 shows the code for implementing a generic MILP solver with default parameter settings.
./symphony -F sample.mpsTo read and solve a model in LP format, the command would be
./symphony -L sample.lpThe user does not have to invoke a command to read the input file. During the call to sym_parse_ command_line(), SYMPHONY determines that the user wants to read in an MPS file. During the subsequent call to sym_load_problem(), the file is read and the problem data stored. To read an GMPL file, the user would issue the command
./symphony -F sample.mod -D sample.datAlthough the same command-line switch is used to specify the model file, the additional presence of the -D option indicates to SYMPHONY that the model file is in GMPL format and GLPK's GMPL parser is invoked . Note that the interface and the code of Figure 3.1 is the same for both sequential and parallel computations. The choice between sequential and parallel execution modes is made at compile-time through modification of the makefile or the project settings, depending on the operating system.
To start the solution process from a warm start, the sym_warm_solve() command is used. SYMPHONY automatically records the warm start information resulting from the last solve call and restarts from that checkpoint if a call to sym_warm_solve() is made. Alternatively, external warm start information can be loaded manually. Figure 3.2 illustrates the use of the re-solve capability by showing the code for implementing a solver that changes from depth first search to best first search after the first feasible solution is found.
Finally, SYMPHONY now also has a bicriteria solve call. The applications of such a solver are numerous. Besides yielding the ability to closely examine the tradeoffs between competing objectives, the method can be used to perform detailed sensitivity analysis in a manner analogous to that which can be done with simplex based solvers for linear programs. As an example, suppose we would like to know exactly how the optimal objective function value for a given pure integer program depends on the value of a given objective function coefficient. Consider increasing the objective function coefficient of variable from its current value. Taking the first objective function to be the original one and taking the second objective function to be the unit vector, we can derive the desired sensitivity function by using the bicriteria solution algorithm to enumerate all supported solutions and breakpoints. This information can easily be used to obtain the desired function. Figure 3.4 shows the code for performing this analysis on variable 0.
In addition to the parts of the API we have just described, there are a number of standard subroutines for accessing and modifying problem data and parameters. These can be used between calls to the solver to change the behavior of the algorithm or to modify the instance being solved. These modifications are discussed in more detail in Section 126.96.36.199.