Install cplex on linux cosce128lin64.bin

We first start with installing required CPLEX and Do-CPLEX Python Optimization libraries: Select вЂ Python 3вЂ™ kernel and hit вЂ SelectвЂ™. Next, the application control moves to Jupyter Lab environment where the system prompts to select the kernel. To create Jupyter notebook instance, Go to Jupyter Notebook tab and click вЂ +вЂ™ button, enter name for the new notebook and hit вЂ CreateвЂ™. Setting-Up Jupyter Notebook: We can create a new Jupyter Notebook via ML Scenario Manager and use it for writing experimental python optimization script. Select вЂ diet_optimization.csvвЂ™ dataset loaded already in SAP DI Data Manager environment by clicking on the button highlighted, enter вЂNAMEвЂ™ and hit вЂ RegisterвЂ™: Click on вЂ +вЂ™ button under вЂ DatasetsвЂ™ tab: Registering the Data-set: We can start of Registering the вЂ Diet_Optimization.csvвЂ™ Dataset to the scenario.

Go to SAP DI Launchpad and click on ML Scenario Manager as highlighted in screen shot below:Д¬lick on вЂ+вЂ™ button, give scenario a name like вЂ Optimization_CPLEXвЂ™ and hit Create button:

Artifacts like data-sets: Datasets (or) Model Formulation file (in LP format), Jupyter Notebook for experimentation, Pipelines for optimization solvers producing solver output. 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.ДЄs a sample scenario, I am considering a Diet Optimization problem. Optimization Techniques can fall under following categories:Дў) Integer Programming (or Mixed Integer Programming)

Most machine learning algorithms solve an optimization problem internally using methods like вЂњGradient DescentвЂќ, 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 вЂњSolversвЂќ 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 вЂњObjective functionвЂќ given the set of вЂњDecision VariablesвЂќ bound by a list of (linear/non-linear) mathematical вЂњConstraintsвЂќ. Below article briefly talks about what entails optimization and its widespread reach across day-to-day users and enterprises.

Install cplex on linux cosce128lin64.bin