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Mathematical Programming, 1976
The Bottleneck Linear Programming problem (BLP) is to maximizex0 = maxjcj,xj > 0 subject toAx = b, x ź 0. A relationship between the BLP and a problem solvable by a "greedy" algorithm is established. Two algorithms for the BLP are developed and computational experience is reported.
Robert S. Garfinkel, Mendu Rao
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The Bottleneck Linear Programming problem (BLP) is to maximizex0 = maxjcj,xj > 0 subject toAx = b, x ź 0. A relationship between the BLP and a problem solvable by a "greedy" algorithm is established. Two algorithms for the BLP are developed and computational experience is reported.
Robert S. Garfinkel, Mendu Rao
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Quantified linear programming [PDF]
, 2015 In many real world problems, dealing with uncertainty is a significant challenge for mathematical programming and related areas, and typically, standard (mixed-integer) linear programming formulations are not well suited to model such problems. However, a powerful way to express uncertainty or adversarial situations is through the use of quantified ...
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Linear Programming with MATLAB
20071. Introduction 2. Linear algebra 3. The simplex method 4. Duality 5. Solving large linear programs 6. Sensitivity and parametric linear programming 7. Quadratic programming and complementarity problems 8. Interior point methods 9. Approximation and classification A. Linear algebra, convexity, and nonlinear functions B.
Michael C. Ferris +2 more
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On linear programs with linear complementarity constraints
Journal of Global Optimization, 2011zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jing Hu +3 more
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On Parametric Linear Programming
SIAM Journal on Applied Mathematics, 1967The problem of solving linear programs under parametric variation of the entire coefficients matrix by a matrix of rank one is considered. An algorithm is presented for determining the feasible region with respect to the parameter and the optimal solutions as a function of the parameter, when they exist.
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1996
We consider the following Colourful generalization of Linear Programming: given sets of points S1, ..., S k ⊕ℝd, referred to as colours, and a point b e ℝd, decide whether there is a colourful T = {s1,..., s k } such that b e conv(T), and if there is, find one. Linear Programming is obtained by taking k = d + 1 and S1 = ... = S d +1. If k = d + 1 and b
Imre Bárány, Shmuel Onn
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We consider the following Colourful generalization of Linear Programming: given sets of points S1, ..., S k ⊕ℝd, referred to as colours, and a point b e ℝd, decide whether there is a colourful T = {s1,..., s k } such that b e conv(T), and if there is, find one. Linear Programming is obtained by taking k = d + 1 and S1 = ... = S d +1. If k = d + 1 and b
Imre Bárány, Shmuel Onn
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Journal of the Operational Research Society, 1975
We consider here the problem of finding a non-negative solution to a set of linear equations which minimizes a bottleneck type objective. Two algorithms are described for the general problem and a single algorithm for the bottleneck transportation problem.
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We consider here the problem of finding a non-negative solution to a set of linear equations which minimizes a bottleneck type objective. Two algorithms are described for the general problem and a single algorithm for the bottleneck transportation problem.
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Studies in Linear and Non-Linear Programming.
The American Mathematical Monthly, 1959Klaus Krickeberg +3 more
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Breast Cancer Diagnosis and Prognosis via Linear Programming
Operations Research, 1995Olvi L. Mangasarian, William H Wolberg
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