I have been working on a problem with some boolean and float decision variables. The constraints are mostly linear programming related. The existence of the boolean variables, however, seems to have made the optimizer to solve the problem with MILP instead of the simple and faster LP. This hugely restricts the size of my model and I am trying to find different means to make my model to run in a more efficient way.
I wonder if approximation algorithm is a sensible solution to my issue. Since I am a rookie on this topic, I have chosen a book to read (which I can post the official link here if it is not against the rule of this forum). The book seems to be mostly about set cover problems and I wonder if this actually covers most approximation algorithm. In addition, I wonder if there are alternative ways to solve the MILP in a quicker way.
Thank you in advance for your time.