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On the solution of mixed-integer nonlinear programming models for computer aided molecular design.

Guennadi M Ostrovsky1, Luke E K Achenie, Manish Sinha

  • 1Department of Chemical Engineering, University of Connecticut, Storrs 06269, USA.

Computers & Chemistry
|October 19, 2002
PubMed
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This study introduces an efficient branch-and-bound algorithm for computer-aided molecular design (CAMD) problems. The new method significantly reduces computational complexity for optimizing solvent design in lithographic printing.

Area of Science:

  • Chemical Engineering
  • Computational Chemistry
  • Optimization

Background:

  • Computer-aided molecular design (CAMD) problems are often formulated as mixed-integer nonlinear programming (MINLP) models.
  • These models frequently feature a high number of linear constraints and nonconvex terms, necessitating global optimization methods.
  • Efficiently solving these complex MINLP models is crucial for advancing molecular design.

Purpose of the Study:

  • To develop an efficient global optimization algorithm for CAMD problems.
  • To address the computational challenges posed by MINLP models with numerous linear constraints and nonconvex variables.
  • To specifically tackle the problem of designing optimal solvents for lithographic printing applications.

Main Methods:

  • A tailored branch-and-bound (BB) algorithm is proposed for solving CAMD problems.

Related Experiment Videos

  • A novel branching strategy is introduced, utilizing linear branching functions to reduce search space dimensionality.
  • Linear underestimators are constructed for a class of functions relevant to CAMD.

    • Main Results:

    • The proposed BB algorithm significantly reduces the number of node traversals compared to conventional methods.
    • The new branching strategy effectively decreases the dimensionality of the search space.
    • The method demonstrates efficiency in solving the specific CAMD problem of optimal solvent design.

    Conclusions:

    • The developed branch-and-bound algorithm offers an efficient solution for complex CAMD problems.
    • The novel branching strategy is key to overcoming the computational burden of traditional BB methods.
    • This approach provides a valuable tool for the design of optimal solvents in industrial applications like lithographic printing.