# Author

Senior Manager

Rob Pratt has worked at SAS since 2000 and is a Senior Manager in the Scientific Computing department in the Analytics R&D division. He manages a team of developers responsible for the optimization modeling language and solvers for linear, mixed integer linear, quadratic, and conic optimization. He earned a B.S. in Mathematics (with a second major in English) from the University of Dayton and both an M.S. in Mathematics and a Ph.D. in Operations Research from The University of North Carolina at Chapel Hill.

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Automated linearization in SAS Optimization

Linear programming (LP) and mixed integer linear programming (MILP) solvers are powerful tools. Many real-world business problems, including facility location, production planning, job scheduling, and vehicle routing, naturally lead to linear optimization models. Sometimes a model that is not quite linear can be transformed to an equivalent linear model to reduce

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Mathematical optimization at SAS

Note from Udo Sglavo on mathematical optimization: When data scientists look at the essence of analytics and wonder about their daily endeavor, it often comes down to supporting better decisions. Peter F. Drucker, the founder of modern management, stated: "Whenever you see a successful business, someone once made a courageous decision."

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What is optimization? And why it matters for your decisions

A note from Udo Sglavo: This post offers an introduction to complex optimization problems and the sophisticated algorithms SAS provides to solve them. In previous posts of this series, we learned that data availability, combined with more and cheaper computing power, creates an essential opportunity for decision-makers. After looking at network analytics

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Solving Kakuro Puzzles with SAS/OR

Erwin Kalvelagen recently posted about a logic puzzle called Kakuro, also known as Cross Sums. As in traditional crossword puzzles, there are horizontal and vertical clues. As in Sudoku, each white cell is to be filled in with a digit from 1 to 9, with no digit repeated within the

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Detecting Anomalies in the NFL Schedule

Super Bowl 50 (L?) is this Sunday, so it's time for another (American) football-related post. Steven Miller, a mathematics professor at Rutgers University, recently noted that the 2015 NFL schedule allowed a competitive advantage for some teams (including the Carolina Panthers). This figure he generated displays the 2015 regular season