Converting a Linear Programming problem for solving using Machine Learning I have a typical linear programming optimization problem with a huge data set (millions of records and upwards of 150 GB data file). The size of the data is causing cost issues for reaching an optimal solution.

Is it possible to convert linear programming problems to machine learning type of problems that can be solved with more cost effective means? 

Thanks
Are you using a binary variable to denote whether a medicine is used for a disease or not ? If yes, did you exclude the combinations of medicines-diseases where the medicine does not cure the disease ? 

By excluding these combinations, you will be reducing the size of your math program. For example, you have Binary_Input_Data(Medicine, Disease) = 1 if the medicine can treat the disease. Then, your Binary_Variable(Medicine, Disease) must be generated only when Binary_Input_Data(Medicine, Disease) = 1.
Are you using a binary variable to denote whether a medicine is used for a disease or not ? If yes, did you exclude the combinations of medicines-diseases where the medicine does not cure the disease ? 

By excluding these combinations, you will be reducing the size of your math program. For example, you have Binary_Input_Data(Medicine, Disease) = 1 if the medicine can treat the disease. Then, your Binary_Variable(Medicine, Disease) must be generated only when Binary_Input_Data(Medicine, Disease) = 1.