Olvi L. Mangasarian

Olvi Leon Mangasarian (born 12 January 1934 - 15 March 2020) was the John von Neumann Professor Emeritus of Mathematics and Computer Sciences in Department of Mathematics, University of California, San Diego and Professor Emeritus of Computer Sciences at the University of Wisconsin-Madison and a recognised expert on optimization, data mining, and classification.[1] In 2000, while professor in the Computer Science Department of the University of Wisconsin–Madison, he was awarded the Frederick W. Lanchester Prize for pioneering work in introducing the use of Operations Research techniques to the field of data mining with a particularly notable application being to breast cancer diagnosis.[2]

Olvi L. Mangasarian
Born (1934-01-12) 12 January 1934
Baghdad, Iraq
Died15 March 2020(2020-03-15) (aged 86)
Madison, Wisconsin
Alma materHarvard University
Scientific career
FieldsMathematics
InstitutionsUniversity of Wisconsin–Madison
Doctoral advisorBernard Budiansky

Selected publications
  • Mangasarian, O. L. (1993). Nonlinear programming (Vol. 10). SIAM.

Festschrift
  • Pang, J. S. (1999). Computational Optimization: A Tribute to Olvi Mangasarian, Volumes I and II. Kluwer Acad. Publ.
gollark: I mostly learn things by writing simple things in them using example code, manuals and in Nim's case the big table of stdlib functions.
gollark: I was going to say something from there, but the client on my phone isn't showing this room.
gollark: I basically don't use Matrix at all because there aren't any interesting communities there (which I'm aware of) and joining things is sloooow.
gollark: But I have Discord open more so I'm not using that.
gollark: I managed to get my dendrite instance to join the matrix room after several minutes of waiting.

References
  1. M Ferris and M Solodov (2004) Foreword: special issue for the 70th birthday of professor Olvi Mangasarian, Optimization Methods and Software 19(5), 439–441.
  2. Mangasarian, O. L., Street, W. N., & Wolberg, W. H. (1995). Breast cancer diagnosis and prognosis via linear programming. Operations Research, 43(4), 570–577.


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