In SSC when a variable is defined as an integer variable or binary, the procedure uses the algorithm of Branch and Bound for optimization. The Branch and Bound resolves a succession of relaxed problems (deprived of integer constraints); to solve these problems is used the Simplex algorithm.
Conversely, the Branch and Bound method is not efficient, and the one implemented in this library, used to solve integer linear programming problems, is not highly optimized. Additionally, there are methods in the literature that are certainly much more efficient in terms of computational complexity and memory usage. Consequently, the size of solvable MILP problems is necessarily limited.
In SSC you can solve problems with free variables, integer, binary and semi-continuous. This subclass of problems are usually named with the initials MILP (Mixed Integer Linear Programming). For MILP problems, which have all or part of integer or binary or semi-continuous variables, SSC uses the algorithm of Branch and Bound (B&B) for their resolution.
Starting from version 2.1.0 it is possible to perform an implementation of the parallel simplex. This option (see example 1.14) allows the use of multiple threads for solving the simplex and provides advantages in the case of architectures with at least 4 or more physical cores.