Abstract
The ε-suboptimal set \(\mathcal{X}_{\epsilon}\) for an optimization problem is the set of feasible points with objective value within ε of optimal. In this paper we describe some basic techniques for quantitatively characterizing \(\mathcal{X}_{\epsilon}\) , for a given value of ε, when the original problem is convex, by solving a modest number of related convex optimization problems. We give methods for computing the bounding box of \(\mathcal{X}_{\epsilon}\) , estimating its diameter, and forming ellipsoidal approximations.
Quantitative knowledge of \(\mathcal{X}_{\epsilon}\) can be very useful in applications. In a design problem, where the objective function is some cost, large \(\mathcal{X}_{\epsilon}\) is good: It means that there are many designs with nearly minimum cost, and we can use this design freedom to improve a secondary objective. In an estimation problem, where the objective function is some measure of plausibility, large \(\mathcal{X}_{\epsilon}\) is bad: It means that quite different parameter values are almost as plausible as the most plausible parameter value.
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This material is based upon work supported by the Focus Center Research Program Center for Circuit & System Solutions award #2003-CT-888, by JPL award #I291856, by Army award #W911NF-07-1-0029, by NSF award #ECS-0423905, by NSF award #0529426, by DARPA award #N66001-06-C-2021, by NASA award #NNX07AEIIA, by AFOSR award #FA9550-06-1-0514, and by AFOSR award #FA9550-06-1-0312.
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Skaf, J., Boyd, S. Techniques for exploring the suboptimal set. Optim Eng 11, 319–337 (2010). https://doi.org/10.1007/s11081-009-9101-7
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DOI: https://doi.org/10.1007/s11081-009-9101-7