SAP DI ML Scenario Manager can be used to create a single Dashboard where all artifacts related to an Optimization problem can be stored and managed through their end of life. We will see Step-by-step on technically implementing this Optimization problem which falls under Mixed Integer Programming (MIP) Problem in SAP Data Intelligence. The objective is to get the cost optimized balanced diet, based on list of ingredients and cost of each ingredient. In rest of the article, I will show how SAP Data Intelligence provides end-to-end orchestration platform to host, compile, solve and execute an optimization problem that could fall in categories listed above.Īs a sample scenario, I am considering a Diet Optimization problem. Optimization Techniques can fall under following categories:Ģ) Integer Programming (or Mixed Integer Programming)
Most machine learning algorithms solve an optimization problem internally using methods like “Gradient Descent”, thus optimization has become an important part of analytics-based solutions in Enterprises. Modern day optimization problems can have several million decision variables and are typically solved using sophisticated “Solvers” such as IBM CPLEX, Gurobi or FICO Xpress. Optimization techniques (or prescriptive analytics) are used to determine optimal solution (best decision) to a problem, formulated in form of (linear/non-linear) mathematical “Objective function” given the set of “Decision Variables” bound by a list of (linear/non-linear) mathematical “Constraints”. Below article briefly talks about what entails optimization and its widespread reach across day-to-day users and enterprises.
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