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Linear programming (LP), also called linear optimization, is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical model whose requirements and objective are represented by linear relationships. Linear programming is a special case of mathematical programming (also known as mathematical optimization).
Benson's algorithm, named after Harold Benson, is a method for solving multi-objective linear programming problems and vector linear programs. This works by finding the "efficient extreme points in the outcome set". [1]
In large linear-programming problems A is typically a sparse matrix and, when the resulting sparsity of B is exploited when maintaining its invertible representation, the revised simplex algorithm is much more efficient than the standard simplex method. Commercial simplex solvers are based on the revised simplex algorithm.
An integer programming problem is a mathematical optimization or feasibility program in which some or all of the variables are restricted to be integers.In many settings the term refers to integer linear programming (ILP), in which the objective function and the constraints (other than the integer constraints) are linear.
An interior point method was discovered by Soviet mathematician I. I. Dikin in 1967. [1] The method was reinvented in the U.S. in the mid-1980s. In 1984, Narendra Karmarkar developed a method for linear programming called Karmarkar's algorithm, [2] which runs in probably polynomial time (() operations on L-bit numbers, where n is the number of variables and constants), and is also very ...
Suppose we have the linear program: Maximize c T x subject to Ax ≤ b, x ≥ 0. We would like to construct an upper bound on the solution. So we create a linear combination of the constraints, with positive coefficients, such that the coefficients of x in the constraints are at least c T. This linear combination gives us an upper bound on the ...
The affine scaling method is an interior point method, meaning that it forms a trajectory of points strictly inside the feasible region of a linear program (as opposed to the simplex algorithm, which walks the corners of the feasible region). In mathematical optimization, affine scaling is an algorithm for solving linear programming problems.
The discovery of linear time algorithms for linear programming and the observation that the same algorithms could in many cases be used to solve geometric optimization problems that were not linear programs goes back at least to Megiddo (1983, 1984), who gave a linear expected time algorithm for both three-variable linear programs and the ...