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Mathematics > Optimization and Control

arXiv:1812.04074 (math)
[Submitted on 10 Dec 2018 (v1), last revised 20 Mar 2019 (this version, v2)]

Title:Disciplined Geometric Programming

Authors:Akshay Agrawal, Steven Diamond, Stephen Boyd
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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.
Comments: Fix typos: p2, changed "g_i log-log concave" to "g_i log-log affine"; p6, max of log-log convex functions is log-log convex, not concave
Subjects: Optimization and Control (math.OC); Mathematical Software (cs.MS)
Cite as: arXiv:1812.04074 [math.OC]
  (or arXiv:1812.04074v2 [math.OC] for this version)
  https://doi.org/10.48550/arXiv.1812.04074

Submission history

From: Akshay Agrawal [view email]
[v1] Mon, 10 Dec 2018 20:43:14 UTC (232 KB)
[v2] Wed, 20 Mar 2019 23:50:14 UTC (232 KB)
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