Why is mathematical optimization important to data scientists?

A growing number of data scientists are adding mathematical optimization (MO), which is also known as mixed-integer programming (MIP), into their analytics toolbox – as they are discovering that MO solves problems, answers questions, and delivers insights that machine learning (ML) cannot. Incorporating MO into your data science repertoire gives you the opportunity to utilize a broader range of applications, maximize the business value of your data, and increase your overall impact on your organization.

Although the techniques of MO were invented more than 70 years ago, recent advances in computing power, algorithms, and data availability and quality have made it possible for MO technologies to rapidly and effectively handle the world’s most complex business problems and automatically generate optimal solutions. As a result, MO has had an immense impact on a wide variety of business areas – including finance, manufacturing, mining, electrical power, and logistics – and has enabled companies in these and many other industries to boost their operational efficiency and overall profitability.

In this video, Gurobi CEO and Co-founder Ed Rothberg explains how mixed-integer programming (MIP) combines expressiveness and robustness to produce high-quality, reliable solutions.

 

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