DrAmpl

Dr. Ampl is an AMPL-based optimization problem analyzer which may be used either as a stand-alone tool or hooked to a server such as NEOS. The capabilities of this analyzer include classification of the problem at hand, analysis of the objective and constraint functions and their variables, the providing of upper and lower bounds on the values of these functions over the feasible set and assessment of convexity. Two antagonistic approaches are considered for the latter point; namely a convexity disprover and a convexity prover.

Keywords for this software

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References in zbMATH (referenced in 9 articles , 1 standard article )

Showing results 1 to 9 of 9.
Sorted by year (citations)

  1. Ceccon, Francesco; Siirola, John D.; Misener, Ruth: SUSPECT: MINLP special structure detector for Pyomo (2020)
  2. Khajavirad, Aida; Sahinidis, Nikolaos V.: A hybrid LP/NLP paradigm for global optimization relaxations (2018)
  3. Lubin, Miles; Yamangil, Emre; Bent, Russell; Vielma, Juan Pablo: Polyhedral approximation in mixed-integer convex optimization (2018)
  4. Miles Lubin, Emre Yamangil, Russell Bent, Juan Pablo Vielma: Polyhedral approximation in mixed-integer convex optimization (2016) arXiv
  5. Lubin, Miles; Dunning, Iain: Computing in operations research using Julia (2015)
  6. Gay, David M.: Using expression graphs in optimization algorithms (2012)
  7. Fourer, Robert; Maheshwari, Chandrakant; Neumaier, Arnold; Orban, Dominique; Schichl, Hermann: Convexity and concavity detection in computational graphs: tree walks for convexity assessment (2010)
  8. Fourer, Robert; Orban, Dominique: DrAmpl: A meta solver for optimization problem analysis (2010)
  9. Grant, Michael; Boyd, Stephen; Ye, Yinyu: Disciplined convex programming (2006)