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Disciplined geometric programming

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Abstract

We introduce log-log convex programs, which are optimization problems with positive variables that become convex when the variables, objective functions, and constraint functions are replaced with their logs, which we refer to as a log-log transformation. This class of problems generalizes traditional geometric programming and generalized geometric programming, and it includes interesting problems involving nonnegative matrices. We give examples of log-log convex functions, some well-known and some less so, and we develop an analog of disciplined convex programming, which we call disciplined geometric programming. Disciplined geometric programming is a subclass of log-log convex programming generated by a composition rule and a set of functions with known curvature under the log-log transformation. Finally, we describe an implementation of disciplined geometric programming as a reduction in CVXPY 1.0.

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Funding

A. Agrawal was supported by an Electrical Engineering Departmental Stanford Fellowship while this research was conducted.

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Authors and Affiliations

  1. Department of Electrical Engineering, Stanford University, 450 Serra Mall, Stanford, CA, 94305, USA

    Akshay Agrawal & Stephen Boyd

  2. Department of Computer Science, Stanford University, 450 Serra Mall, Stanford, CA, 94305, USA

    Steven Diamond

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  1. Akshay Agrawal
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  2. Steven Diamond
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  3. Stephen Boyd
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Correspondence to Akshay Agrawal.

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Agrawal, A., Diamond, S. & Boyd, S. Disciplined geometric programming. Optim Lett 13, 961–976 (2019). https://doi.org/10.1007/s11590-019-01422-z

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