A01=George B. Dantzig
Accuracy and precision
Algorithm
Approximation theory
Author_George B. Dantzig
Automatic programming
Axiom
Basic solution (linear programming)
Canonical form
Category=KC
Category=PBF
Category=PBUH
Change of variables
Coefficient
Combination
Computation
Computational complexity theory
Counterexample
Dimensional analysis
Dual basis
Duality (optimization)
Dynamic problem (algorithms)
Dynamic programming
Elementary matrix
eq_bestseller
eq_business-finance-law
eq_isMigrated=0
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Equation
Ford-Fulkerson algorithm
Gradient descent
Input-Output Analysis
Integer programming
Invertible matrix
Iteration
Iterative method
Lagrange multiplier
Linear approximation
Linear combination
Linear differential equation
Linear equation
Linear function
Linear inequality
Linear interpolation
Linear map
Linear programming
Linearity
Local optimum
Loss function
Mathematical optimization
Maxima and minima
Monotonic function
Neyman-Pearson lemma
Nonlinear programming
Optimality criterion
Parametric programming
Permutation
Permutation matrix
Pivot element
Polynomial
Programming tool
Projection method (fluid dynamics)
Quadratic programming
Quantity
Requirement
Sensitivity analysis
Simplex algorithm
Simultaneous equations
Slack variable
Solution set
Special case
Summation
Theorem
Theory of computation
Uniqueness theorem
Upper and lower bounds
Utilization
Variable (computer science)
Variable (mathematics)
Product details
- ISBN 9780691059136
- Weight: 879g
- Dimensions: 152 x 235mm
- Publication Date: 23 Aug 1998
- Publisher: Princeton University Press
- Publication City/Country: US
- Product Form: Paperback
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In real-world problems related to finance, business, and management, mathematicians and economists frequently encounter optimization problems. In this classic book, George Dantzig looks at a wealth of examples and develops linear programming methods for their solutions. He begins by introducing the basic theory of linear inequalities and describes the powerful simplex method used to solve them. Treatments of the price concept, the transportation problem, and matrix methods are also given, and key mathematical concepts such as the properties of convex sets and linear vector spaces are covered. George Dantzig is properly acclaimed as the "father of linear programming." Linear programming is a mathematical technique used to optimize a situation. It can be used to minimize traffic congestion or to maximize the scheduling of airline flights. He formulated its basic theoretical model and discovered its underlying computational algorithm, the "simplex method," in a pathbreaking memorandum published by the United States Air Force in early 1948.
Linear Programming and Extensions provides an extraordinary account of the subsequent development of his subject, including research in mathematical theory, computation, economic analysis, and applications to industrial problems. Dantzig first achieved success as a statistics graduate student at the University of California, Berkeley. One day he arrived for a class after it had begun, and assumed the two problems on the board were assigned for homework. When he handed in the solutions, he apologized to his professor, Jerzy Neyman, for their being late but explained that he had found the problems harder than usual. About six weeks later, Neyman excitedly told Dantzig, "I've just written an introduction to one of your papers. Read it so I can send it out right away for publication." Dantzig had no idea what he was talking about. He later learned that the "homework" problems had in fact been two famous unsolved problems in statistics.
George B. Dantzig (1914–2005) is widely acclaimed as the father of linear programming and was a leading figure in the development of mathematical optimization, making important contributions to fields such as industrial engineering, economics, and statistics. He was professor emeritus of operations research and computer science at Stanford University.
