MODAL-SynLab
Refine
Year of publication
Document Type
- ZIB-Report (102)
- Article (90)
- In Proceedings (70)
- Master's Thesis (10)
- Proceedings (5)
- Book chapter (4)
- Doctoral Thesis (4)
- Bachelor's Thesis (3)
- Software (2)
- In Collection (1)
Keywords
- mixed integer programming (11)
- mixed-integer programming (8)
- linear programming (6)
- parallelization (5)
- MINLP (4)
- branch-and-bound (4)
- presolving (4)
- Column generation (3)
- Mixed-Integer Programming (3)
- Mixed-integer linear programming (3)
Institute
- Mathematical Optimization (204)
- Mathematical Optimization Methods (159)
- AI in Society, Science, and Technology (66)
- Applied Algorithmic Intelligence Methods (33)
- Mathematical Algorithmic Intelligence (17)
- Digital Data and Information for Society, Science, and Culture (7)
- Mathematics of Transportation and Logistics (4)
- Computational Molecular Design (2)
- Mathematics of Telecommunication (2)
- Numerical Mathematics (2)
Shift-And-Propagate
(2013)
For mixed integer programming, recent years have seen a growing interest in the design of general purpose primal heuristics for use inside complete solvers. Many of these heuristics rely on an optimal LP solution. Finding this may itself take a significant amount of time.
The presented paper addresses this issue by the introduction of the Shift-And-Propagate heuristic. Shift-And-Propagate is a pre-root primal heuristic that does not require a previously found LP solution. It applies domain propagation techniques to quickly drive a variable assignment towards feasibility. Computational experiments indicate that this heuristic is a powerful supplement of existing rounding and propagation heuristics.