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Mar 24, 2022 · Vijay Vazirani got his undergraduate degree from MIT in 1979 and his PhD from the University of California, Berkeley in 1983. He is currently a Distinguished Professor at the University of California, Irvine.
Mar 14, 2013 · Approximation Algorithms. Vijay V. Vazirani. Springer Science & Business Media, Mar 14, 2013 - Computers - 380 pages. Although this may seem a paradox, all exact science is dominated by the idea of approximation. Bertrand Russell (1872-1970) Most natural optimization problems, including those arising in important application areas, are NP-hard.
Umesh Virkumar Vazirani is an Indian–American academic who is the Roger A. Strauch Professor of Electrical Engineering and Computer Science at the University of California, Berkeley, and the director of the Berkeley Quantum Computation Center. His research interests lie primarily in quantum computing. He is also a co-author of a textbook on ...
It is a pleasure to recommend Vijay Vazirani’s well-written and comprehensive book on this important and timely topic. I am sure the reader will find it most useful both as an introduction to approximability as well as a reference to the many aspects of approxi-mation algorithms.” László Lovász, Senior Researcher Microsoft Research
Dec 8, 2010 · Approximation Algorithms. by Vijay V. V. Vazirani (Author) 4.7 19 ratings. See all formats and editions. Although this may seem a paradox, all exact science is dominated by the idea of approximation. Bertrand Russell (1872-1970) Most natural optimization problems, including those arising in important application areas, are NP-hard.
- Vijay V. V. Vazirani
Vijay V. Vazirani. MathSciNet. Ph.D. University of California, Berkeley 1984. Dissertation: Maximum Matchings without Blossoms. Advisor: Manuel Blum. Click here to see the students listed in chronological order. According to our current on-line database, Vijay Vazirani has 8 students and 44 descendants. We welcome any additional information.
Vijay V. Vazirani interconnects Mathematical proof, Industrial organization, School choice and Strategic dominance in the investigation of issues within Incentive compatibility. His Mathematical optimization study integrates concerns from other disciplines, such as Mathematical economics and Selection.
