Narendra Karmarkar
| Narendra Krishna Karmarkar | |
|---|---|
| Born | Gwalior |
| Fields | Mathematics, Computing Science |
| Institutions | Bell Labs |
| Alma mater | Indian Institute of Technology Bombay, California Institute of Technology, University of California, Berkeley |
| Thesis | Coping with NP-Hard Problems (1983) |
| Doctoral advisor | Richard M. Karp |
| Known for | Karmarkar's algorithm |
Narendra Krishna Karmarkar (born 1957) is an Indian mathematician, who developed Karmarkar's algorithm. He is listed as an ISI highly cited researcher.
Narendra Karmarkar secured an All India Rank 1 in the Joint Entrance Examination conducted by the prestigious IITs (IIT-JEE), he took admission in the Indian Institute of Technology Bombay. Karmarkar received his B.Tech in Electrical Engineering from IIT Bombay in 1978, M.S. from the California Institute of Technology and Ph.D. in Computer Science from the University of California, Berkeley in 1983 under the supervision of Richard M. Karp.
He invented a polynomial algorithm for linear programming also known as the interior point method. The algorithm is a cornerstone in the field of Linear Programming. He published his famous result in 1984 while he was working for Bell Laboratories in New Jersey. Karmarkar was a professor at the Tata Institute of Fundamental Research in Mumbai. He is currently working on a new architecture for supercomputing.
Karmarkar has received a number of awards:
Karmarkar's algorithm solves linear programming problems in polynomial time. These problems are represented by "n" variables and "m" constraints. The previous method of solving these problems consisted of problem representation by an "x" sided solid with "y" vertices, where the solution was approached by traversing from vertex to vertex. Karmarkar's novel method approaches the solution by cutting through the above solid in its traversal. Consequently, complex optimization problems are solved much faster using the Karmarkar algorithm. A practical example of this efficiency is the solution to a complex problem in communications network optimization where the solution time was reduced from weeks to days. His algorithm thus enables faster business and policy decisions. Karmarkar's algorithm has stimulated the development of several interior point methods, some of which are used in current codes for solving linear programs.
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