Narendra Karmarkar

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Narendra K. Karmarkar (b. 1957 in a Maharashtrian family) is an Indian mathematician, renowned for developing a new algorithm known as Karmarkar's algorithm. This algorithm solves linear programming problems in polynomial time. The significance of his invention lies in the fact that it was the first algorithm to solve linear programming problems which had a good running time both theoretically and in practice. It has stimulated the development of several other interior point methods, some of which are used in current codes for solving linear programs.

Dr. Karmarkar received his B.Tech at the IIT Bombay in 1978. Later, he received his M.S. at the California Institute of Technology, and his Ph.D. at the Institute of Computer Science at the University of California, Berkeley. He published his famous result in 1984 while he was working for Bell Laboratories in New Jersey. Dr. Karmarkar was a professor at the Tata Institute of Fundamental Research in Bombay. He has started a company, with funding from Mr. Ratan Tata to the tune of INR 400 crore (US$ 80 Million), in the field of High Performance Computing [1].

[edit] Awards (selected)

Dr. Karmarkar received a number of awards for his algorithm, among them:

Distinguished Alumnus Award, Computer Science and Engineering, University of California, Berkeley (1993)
Ramanujan Prize for Computing, given by Asian Institute Informatics (1989)
Fulkerson Prize in Discrete Mathematics given jointly by the American Mathematical Society & Mathematical Programming Society (1988)
Fellow of Bell Laboratories (1987- )
Texas Instruments Founders’ Prize (1986)
Marconi International Young Scientist Award (1985)
Lanchester Prize of the Operations Research Society of America for the Best Published Contributions to Operations Research (1984)
National Science Talent Award in Mathematics, India (1972, India)

The Association for Computing Machinery awarded him the prestigious Paris Kanellakis Award for his work. The award citation reads:

For his theoretical work in devising an Interior Point method for linear programming that provably runs in polynomial time, and for his implementation work suggesting that Interior Point methods could be effective for linear programming in practice as well as theory. Together, these contributions inspired a renaissance in the theory and practice of linear programming, leading to orders of magnitude improvement in the effectiveness of widely-used commercial optimization codes.

He is also in a rare club of the few IIT Joint Entrance Examination toppers (AIR Rank 1) [2].