Interior Point Methods for Linear Programming: Just Call Newton, Lagrange, and Fiacco and McCormick!

Roy Marsten, Radhika Subramanian, Matthew Saltzman, Irvin Lustig and David Shanno
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Roy Marsten: School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332
Radhika Subramanian: School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332
Matthew Saltzman: Department of Mathematical Sciences, Clemson University, Clemson, SC 29631
Irvin Lustig: Department of Civil Engineering and Operations Research, Princeton University, Princeton, NJ 08544
David Shanno: RUTCOR, Rutgers University, New Brunswick, NJ 08903

Interfaces, 1990, vol. 20, issue 4, 105-116

Abstract: Interior point methods are the right way to solve large linear programs. They are also much easier to derive, motivate, and understand than they at first appeared. Lagrange told us how to convert a minimization with equality constraints into an unconstrained minimization. Fiacco and McCormick told us how to convert a minimization with inequality constraints into a sequence of unconstrained minimizations. Newton told us how to solve unconstrained minimizations. Linear programs are minimizations with equations and inequalities. Voila!

Keywords: programming: linear; tutorial (search for similar items in EconPapers)
Date: 1990
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Citations: View citations in EconPapers (8)

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