Computing Atlas

How Computing Was Built
Sign In
Text size
100%
Theme
Pioneer

Narendra Karmarkar

Also Known As Narendra Krishna Karmarkar
Theory of Computation

Narendra Karmarkar is an Indian mathematician and computer scientist who in 1984 devised Karmarkars algorithm, the first interior-point method proven to solve linear programming problems in polynomial time while also running dramatically faster than the older simplex method on many large real-world problems. The algorithm had immediate industrial impact on scheduling and resource-allocation problems in telecommunications and logistics.

Facts
Birth Year
1956 1
Nationality
Indian 1
Award
Fulkerson Prize 2
Award
Paris Kanellakis Award 2
Award
Frederick W. Lanchester Prize 2
Narendra Karmarkar
Filter Results2 entries
Connections

Credited

Source Wikipedia: Interior-point method
Source Karmarkar's Algorithm (Wikipedia)

In Field

Source Wikipedia: Narendra Karmarkar

Invented

Source Wikipedia: Interior-point method

Narendra Karmarkar published his interior-point method for linear programming in 1984.

In the Other Atlases

Nationality

Also in Geography Atlas: the nationality of this subject.

Sources
1. Wikipedia: Narendra Karmarkar
Wikimedia Foundation
  • Lead section
    Narendra Krishna Karmarkar (born 1956) is an Indian mathematician.
  • In Field: Algorithms and Complexity Theory, Lead paragraph
View the Source
2. Wikidata: Narendra Karmarkar
Wikidata Q29664, class allow-list match (w-wdresolver-0926)View the Source
Karmarkar's Algorithm (Wikipedia)
Credited: Karmarkar's Algorithm, Intro, lead sentence
Quote, Credited: Karmarkar's Algorithm, Intro, lead sentence
Narendra Karmarkar
View the Source
Wikipedia: Interior-point method
  • Credited: Interior Point Method Algorithm, Lead
    In 1984, Narendra Karmarkar developed a method for linear programming
  • Invented: Interior Point Method Algorithm, Lead
    In 1984, Narendra Karmarkar developed a method for linear programming
View the Source
Comments (0)
No comments yet. Be the first to share a thought.
Reader Challenges (0)
No disputes yet. Spotted an error or a better source? Open the first one.