SAMPL
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| SAMPL | |
|---|---|
| Paradigm | multi-paradigm: declarative, imperative |
| Designed by | Gautam Mitra, Enza Messina, Valente Patrick |
| First appeared | 2001 |
| Template:Infobox software/simple | |
| OS | Cross-platform (multi-platform) |
| License | Proprietary |
| Filename extensions | .mod .dat .run .sampl |
| Website | www |
| Influenced by | |
| AMPL | |
SAMPL, which stands for "Stochastic AMPL", is an algebraic modeling language resulting by expanding the well-known language AMPL with extended syntax and keywords. It is designed specifically for representing stochastic programming problems[1] and, through recent extensions, problems with chance constraints, integrated chance constraints and robust optimization problems. It can generate the deterministic equivalent version of the instances, using all the solvers AMPL connects to,[2] or generate an SMPS representation and use specialized decomposition based solvers, like FortSP.
Language Features
SAMPL shares all language features with AMPL, and adds some constructs specifically designed for expressing scenario based stochastic programming and robust optimization.
Stochastic programming features and constructs
To express scenario-based SP problems, additional constructs describe the tree structure and group the decision variable into stages. Moreover, it is possible to specify which parameter stores the probabilities for each branch of the tree and which set represents the scenario set. Other constructs to easily define chance constraints and integrated chance constraint in an SP problem are available as well. Using these language constructs allows to retain the structure of the problem, hence making it available to the solvers, which might exploit it using specialized decomposition methods like Benders' decomposition to speed-up the solution.
Robust optimization constructs
SAMPL supports constructs to describe three types of robust optimization formulations:
Availability
SAMPL is currently available as a part of the software AMPLDev (distributed by www.optirisk-systems.com). It supports many popular 32- and 64-bit platforms including Windows, Linux and Mac OS X. A free evaluation version with limited functionality is available.[6]
A stochastic programming sample model
The following is the SAMPL version of a simple problem (Dakota[7]), to show the SP related constructs. It does not include the data file, which follows the normal AMPL syntax (see the example provided in the AMPL Wikipedia page for further reference).
Template:Codett Template:Codett Template:Codett scenarioset Scen; Template:Codett tree Tree := twostage; Template:Codett random param Demand{Prod, Scen}; Template:Codett probability P{Scen}; Template:Codett Template:Codett Template:Codett Template:Codett Template:Codett Template:Codett Template:Codett Template:Codett Template:Codett Template:Codett suffix stage 1; Template:Codett Template:Codett suffix stage 2; Template:Codett Template:Codett suffix stage 2; Template:Codett Template:Codett Template:Codett Template:Codett Template:Codett Template:Codett Template:Codett Template:Codett Template:Codett Template:Codett Template:Codett Template:Codett Template:Codett
Solvers connectivity
SAMPL instance level format for SP problems is SMPS, and therefore the problem can be solved by any solver which supports that standard. One of such solvers (FortSP) is included in the standard SAMPL distribution. Regarding robust optimization problems, the needed solver depend on the specific formulation used, as Ben-Tal and Nemirovski formulation need a second-order cone capable solver.
See also
- Algebraic modeling language
- AIMMS
- AMPL
- FortSP
- GAMS – General Algebraic Modeling System
- GLPK – free open source system based on a subset of AMPL
- HiGHS - HiGHS is high performance serial and parallel software for solving large-scale sparse linear programming (LP), mixed-integer programming (MIP) and quadratic programming (QP) models
- MPS (format)
- Robust optimization
- Stochastic programming
References
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Page Module:Citation/CS1/styles.css has no content.Christian Valente, Gautam Mitra, Mustapha Sadki and Robert Fourer (2009). "Extending algebraic modelling languages for stochastic programming". INFORMS Journal on Computing. 21 (1): 107–122. doi:10.1287/ijoc.1080.0282.
{{cite journal}}: CS1 maint: multiple names: authors list (link) - ^ Page Module:Citation/CS1/styles.css has no content."Solvers".
- ^ Page Module:Citation/CS1/styles.css has no content.Allen L Soyster (1974). "Technical Note—Convex Programming with Set-Inclusive Constraints and Applications to Inexact Linear Programming". Operations Research. 21 (5): 1154–1157. doi:10.1287/opre.21.5.1154.
- ^ Page Module:Citation/CS1/styles.css has no content.Bertsimas, Dimitris; Sim, Melvyn (2004). "The Price of Robustness". Operations Research. 52 (1): 35–53. doi:10.1287/opre.1030.0065. hdl:2268/253225. S2CID 8946639.
- ^ Page Module:Citation/CS1/styles.css has no content.Aharon Ben-Tal & Arkadi Nemirovski (1998). "Robust convex optimization". Mathematics of Operations Research. 23 (4): 769–805. CiteSeerX 10.1.1.135.798. doi:10.1287/moor.23.4.769.
- ^ Page Module:Citation/CS1/styles.css has no content."Products: AMPLDev SP". optirisk-systems.com. Archived from the original on 2012-11-11.
- ^ Page Module:Citation/CS1/styles.css has no content.Higle, Julia L, Wallace, Stein W (2003). "Sensitivity analysis and uncertainty in linear programming" (PDF). Interfaces. 33 (4): 53–60. doi:10.1287/inte.33.4.53.16370.
{{cite journal}}: CS1 maint: multiple names: authors list (link)
