George Dantzig

George Dantzig

George Dantzig (1914 – 2005) was an American mathematical scientist who solved two unsolved problems in statistical theory, which he had mistaken for homework after arriving late to a lecture of UC Berkeley statistician Jerzy Neyman. Dantzig was the Professor Emeritus of Transportation Sciences and Professor of Operations Research and of Computer Science at Stanford.

Born in Portland, Oregon, George Bernard Dantzig was named after George Bernard Shaw, the Irish writer. His father, Tobias Dantzig, was a Baltic German mathematician and linguist, and his mother, Anja Dantzig (née Ourisson), was a French linguist. Dantzig’s parents met during their study at the Sorbonne, where Tobias studied mathematics under Henri Poincaré.

After emigrating to the US early in the 1920s, the Dantzig family moved from Baltimore to Washington. His mother became a linguist at the Library of Congress, and his father became a math tutor at the University of Maryland. George attended Powell Junior High School and Central High School. By the time he reached high school he was already fascinated by geometry, and this interest was further nurtured by his father, challenging him with complicated problems, particularly in projective geometry. George Dantzig earned bachelor’s degrees in mathematics and physics from the University of Maryland in 1936, and his master’s degree in mathematics from the University of Michigan in 1938. After a two-year period at the Bureau of Labor Statistics, he enrolled in the doctoral program in mathematics at the University of California, Berkeley, where he studied statistics under Jerzy Neyman.

With the outbreak of World War II, George took a leave of absence from the doctoral program at Berkeley to join the U.S. Air Force Office of Statistical Control. In 1946, he returned to Berkeley to complete the requirements of his program and received his Ph.D. that year. In 1952 Dantzig joined the mathematics division of the RAND Corporation. By 1960 he became a professor in the Department of Industrial Engineering at UC Berkeley, where he founded and directed the Operations Research Center (which studied what is now called management science or decision theory).

In 1966 he joined the Stanford faculty as Professor of Operations Research and of Computer Science. A year later, the Program in Operations Research became a full-fledged department. In 1973 he founded the Systems Optimization Laboratory (SOL) there. On a sabbatical leave that year, he headed the Methodology Group at the International Institute for Applied Systems Analysis (IIASA) in Laxenburg, Austria. Later he became the C. A. Criley Professor of Transportation Sciences at Stanford, and kept going, well beyond his mandatory retirement in 1985.

Freund wrote further that ‘through his research in mathematical theory, computation, economic analysis, and applications to industrial problems, [Dantzig] has contributed more than any other researcher to the remarkable development of linear programming.’ Linear programming is a mathematical method for determining a way to achieve the best outcome (such as maximum profit or lowest cost), given some conditions (e.g. maximize A, subject to X, and Y). Dantzig’s seminal work allows the airline industry, for example, to schedule crews and make fleet assignments.

Based on his work tools are developed ‘that shipping companies use to determine how many planes they need and where their delivery trucks should be deployed. The oil industry long has used linear programming in refinery planning, as it determines how much of its raw product should become different grades of gasoline and how much should be used for petroleum-based byproducts. It’s used in manufacturing, revenue management, telecommunications, advertising, architecture, circuit design and countless other areas.’

An event in Dantzig’s life became the origin of a famous story in 1939 while he was a graduate student at UC Berkeley. Near the beginning of a class for which Dantzig was late, professor Jerzy Neyman wrote two examples of famously unsolved statistics problems on the blackboard. When Dantzig arrived, he assumed that the two problems were a homework assignment and wrote them down.

According to Dantzig, the problems ‘seemed to be a little harder than usual,’ but a few days later he handed in completed solutions for the two problems, still believing that they were an assignment that was overdue. Six weeks later, Dantzig received a visit from an excited professor Neyman, eager to tell him that the homework problems he had solved were two of the most famous unsolved problems in statistics. He had prepared one of Dantzig’s solutions for publication in a mathematical journal. As Dantzig told it in a 1986 interview in the ‘College Mathematics Journal’: ‘A year later, when I began to worry about a thesis topic, Neyman just shrugged and told me to wrap the two problems in a binder and he would accept them as my thesis.

Years later another researcher, Abraham Wald, was preparing to publish a paper which arrived at a conclusion for the second problem, and included Dantzig as its co-author when he learned of the earlier solution. This story began to spread, and was used as a motivational lesson demonstrating the power of positive thinking. Over time Dantzig’s name was removed and facts were altered, but the basic story persisted in the form of an urban legend, and as an introductory scene in the movie ‘Good Will Hunting.’

Linear programming arose as a mathematical model developed during World War II to plan expenditures and returns in order to reduce costs to the army and increase losses to the enemy. It was kept secret until 1947. Postwar, many industries found its use in their daily planning. The founders of this subject are Leonid Kantorovich, a Russian mathematician who developed linear programming problems in 1939, Dantzig, who published the simplex method in 1947, and John von Neumann, who developed the theory of the duality in the same year.

Dantzig’s original example of finding the best assignment of 70 people to 70 jobs exemplifies the usefulness of linear programming. The computing power required to test all the permutations to select the best assignment is vast; the number of possible configurations exceeds the number of particles in the universe. However, it takes only a moment to find the optimum solution by posing the problem as a linear program and applying Danzig’s Simplex algorithm. The theory behind linear programming drastically reduces the number of possible optimal solutions that must be checked.

In 1963, Dantzig’s ‘Linear Programming and Extensions’ was published by Princeton University Press. Rich in insight and coverage of significant topics, the book quickly became ‘the bible’ of linear programming.

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