Generalized Ellipsoids
We introduce a family of symmetric convex bodies called generalized ellipsoids of degree d (GE-ds), with ellipsoids corresponding to the case of [Formula: see text]. Generalized ellipsoids (GEs) retain many geometric, algebraic, and algorithmic properties of ellipsoids. We show that the conditions that the parameters of a GE must satisfy can be checked in strongly polynomial time and that one can search for GEs of a given degree by solving a semidefinite program whose size grows only linearly with dimension. We give an example of a GE that does not have a second-order cone representation, but we show that every GE has a semidefinite representation whose size depends linearly on both its dimension and its degree. In terms of expressiveness, we prove that for any integer [Formula: see text], every symmetric full-dimensional polytope with 2m facets and every intersection of m cocentered ellipsoids can be represented exactly as a GE-d with [Formula: see text]. Using this result, we show that every symmetric convex body can be approximated arbitrarily well by a GE-d, and we quantify the quality of the approximation as a function of the degree d. Finally, we present applications of GEs to several areas, such as time-varying portfolio optimization, stability analysis of switched linear systems, robust-to-dynamics optimization, and robust polynomial regression. Funding: A. A. Ahmadi was partially supported by the Air Force Office of Scientific Research [Grant MURI], the Sloan Fellowship, and Princeton [AI Lab Seed Grant and SEAS Innovation Grant]. A. Chaudhry was partially supported by the Air Force Office of Scientific Research [Grant MURI].
Authors
- Abraar Chaudhry
- Cemil Dibek (ORCID: https://orcid.org/0000-0002-7162-9581)
- Amir Ali Ahmadi
Institutions
- Georgia Institute of Technology (US)
- Koç University (TR)
- Princeton University (US)
Publication Details
- Journal
- Mathematics of Operations Research
- Published
- 2026-01-30
- DOI
- https://doi.org/10.1287/moor.2024.0643
- Primary Topic
- Advanced Optimization Algorithms Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00