2010
DOI: 10.1007/978-3-642-11503-5_12
|View full text |Cite
|
Sign up to set email alerts
|

Automating Mathematical Program Transformations

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
7
0

Year Published

2011
2011
2023
2023

Publication Types

Select...
6
2

Relationship

0
8

Authors

Journals

citations
Cited by 14 publications
(7 citation statements)
references
References 11 publications
0
7
0
Order By: Relevance
“…We can simplify the objective function by omitting item x 0 iL and x 0 iR , which are constants for a specific hit point combination. In addition, we also need to convert constraints (1g)-(1i) to linear constraints based on the big-M transformation [29].…”
Section: Sadp-aware Pin Accessmentioning
confidence: 99%
“…We can simplify the objective function by omitting item x 0 iL and x 0 iR , which are constants for a specific hit point combination. In addition, we also need to convert constraints (1g)-(1i) to linear constraints based on the big-M transformation [29].…”
Section: Sadp-aware Pin Accessmentioning
confidence: 99%
“…We can simplify the objective function by omitting item x 0 iL and x 0 iR , which are constants for a specific hit point combination. In addition, we also need to convert constraints (1g)-(1i) to linear constraints based on the big-M transformation [18].…”
Section: Milp Formulationmentioning
confidence: 99%
“…The implication symbol “⇒” in () means that the constraint is only enforced when the corresponding binary variable is equal to one. This implication and the subsequent ones may be implemented in a MIQP framework using the “big‐M” technique 43 . A brief tutorial on how to implement implications in a mixed‐integer programming framework through “big‐M” is presented in Appendix E.…”
Section: Optimal Control Formulationmentioning
confidence: 99%