Results 241 to 250 of about 3,786,566 (280)
Some of the next articles are maybe not open access.
Presolving in linear programming
Mathematical Programming, 1995zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Erling D. Andersen, Knud D. Andersen
openaire +1 more source
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
openaire +1 more source
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
openaire +2 more sources
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
openaire +2 more sources
Linearization by Program Transformation
2004We identify a restricted class of terms of the lambda calculus, here called weak linear, that includes the linear lambda-terms keeping their good properties of strong normalization, non-duplicating reductions and typability in polynomial time. The advantage of this class over the linear lambda-calculus is the possibility of transforming general terms ...
Sandra Alves, Mário Florido
openaire +2 more sources
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
openaire +2 more sources
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.
openaire +1 more source
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.
openaire +1 more source
Mathematical Programming, 1981
This paper treats entropy constrained linear programs from modelling as well as computational aspects. The optimal solutions to linear programs with one additional entropy constraint are expressed in terms of Lagrange-multipliers. Conditions for uniqueness are given. Sensitivity and duality are studied. The Newton—Kantorovich method is used to obtain a
openaire +3 more sources
This paper treats entropy constrained linear programs from modelling as well as computational aspects. The optimal solutions to linear programs with one additional entropy constraint are expressed in terms of Lagrange-multipliers. Conditions for uniqueness are given. Sensitivity and duality are studied. The Newton—Kantorovich method is used to obtain a
openaire +3 more sources
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
openaire +1 more source
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
openaire +1 more source
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.
openaire +2 more sources