Name
Abstract | ||
|---|---|---|
An augmented Lagrangian method is proposed for handling the common rows in large scale linear programming problems with block-diagonal structure and linking constraints. Using a diagonal quadratic approximation of the augmented Lagrangian one obtains subproblems that can be readily solved in parallel by a nonlinear primal-dual barrier method for convex separable programs. The combined augmented Lagrangian/barrier method applies in a natural way to stochastic programming and multicommodity networks. |
Year | DOI | Venue |
|---|---|---|
1992 | 10.1016/0167-6377(92)90046-6 | Operations Research Letters |
Keywords | Field | DocType |
augmented lagrangian,linear programming,augmented lagrangian method,stochastic programming,linear programming problem,diagonal quadratic approximation,combined augmented lagrangian,nonlinear primal-dual barrier method,block-diagonal structure,large scale,diagonal quadratic approximation method,decomposition,barrier method,convex separable program,common row,linear program | Diagonal,Mathematical optimization,Combinatorics,Nonlinear system,Quadratic equation,Regular polygon,Augmented Lagrangian method,Linear programming,Quadratic programming,Stochastic programming,Mathematics | Journal |
Volume | Issue | ISSN |
12 | 4 | Operations Research Letters |
Citations | PageRank | References |
36 | 9.33 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| John M. Mulvey | 1 | 417 | 115.70 |
| Andrzej Ruszczyński | 2 | 798 | 84.38 |