A multi-stage linear program is defined with linking variables that connect consecutive stages. Optimality conditions for the composite problem are partitioned into local and linking conditions. When the Dantzig-Wolfe decomposition scheme is applied with the first stage as the master, the subproblem is also a MLP with one' less stage. The same decomposition is then applied to the subproblem, giving rise to a nested decomposition scheme, in which each stage acts as a master for the following stage and a subproblem for the preceding. Optimizing a single stage problem results in satisfying the "local" optimality conditions. A very general rule is given for selecting the next subproblem to optimize, and finite convergence to a solution satisfyi...
AbstractA decomposition method for nonlinear programming problems with structured linear constraints...
A sequential method is proposed for solving a nonlinear optimization problem depending on a paramete...
The thesis deals with the algorithms for two-stage stochastic programs. The first chapter considers ...
This paper considers large-scale multistage stochastic linear programs. Sampling is incorporated int...
This paper describes a vector space decomposition algorithmic framework for linear programming guide...
We propose and test a new pricing procedure for solving large-scale structured linear programs. The ...
AbstractThe computational difficulties that continue to plague decomposition algorithms, namely, “lo...
The Dantzig-Wolfe decomposition (linear programming) principle published in 1960 involves the solvin...
Abstract- Dantzig-Wolfe decomposition (DWD) principle relies on delayed column generation for solvin...
Nested decomposition of linear programs is the result of a multilevel, hierarchical application of t...
An interval linear program is where the matrix A, vectors b -, b +, and c are given. If A has full r...
A decomposition method for non-linear programming problems with structured linear constraints is des...
We consider a multiple objective linear program (MOLP) max{Cx|Ax = b,x in N_{0}^{n}} where C = (c_ij...
This dissertation focuses on how existing optimization algorithms can be modified to take advantage ...
A recent development in the field of discrete optimization is the combined use of (binary) decision ...
AbstractA decomposition method for nonlinear programming problems with structured linear constraints...
A sequential method is proposed for solving a nonlinear optimization problem depending on a paramete...
The thesis deals with the algorithms for two-stage stochastic programs. The first chapter considers ...
This paper considers large-scale multistage stochastic linear programs. Sampling is incorporated int...
This paper describes a vector space decomposition algorithmic framework for linear programming guide...
We propose and test a new pricing procedure for solving large-scale structured linear programs. The ...
AbstractThe computational difficulties that continue to plague decomposition algorithms, namely, “lo...
The Dantzig-Wolfe decomposition (linear programming) principle published in 1960 involves the solvin...
Abstract- Dantzig-Wolfe decomposition (DWD) principle relies on delayed column generation for solvin...
Nested decomposition of linear programs is the result of a multilevel, hierarchical application of t...
An interval linear program is where the matrix A, vectors b -, b +, and c are given. If A has full r...
A decomposition method for non-linear programming problems with structured linear constraints is des...
We consider a multiple objective linear program (MOLP) max{Cx|Ax = b,x in N_{0}^{n}} where C = (c_ij...
This dissertation focuses on how existing optimization algorithms can be modified to take advantage ...
A recent development in the field of discrete optimization is the combined use of (binary) decision ...
AbstractA decomposition method for nonlinear programming problems with structured linear constraints...
A sequential method is proposed for solving a nonlinear optimization problem depending on a paramete...
The thesis deals with the algorithms for two-stage stochastic programs. The first chapter considers ...