We study robust convex quadratic programs where the uncertain problem parameters can contain both continuous and integer components. Under the natural boundedness assumption on the uncertainty set, we show that the generic problems are amenable to exact copositive programming reformulations of polynomial size. These convex optimization problems are NP-hard but admit a conservative semidefinite programming (SDP) approximation that can be solved efficiently. We prove that the popular approximate S-lemma method—which is valid only in the case of continuous uncertainty—is weaker than our approximation. We also show that all results can be extended to the two-stage robust quadratic optimization setting if the problem has complete recourse. We assess the effectiveness of our proposed SDP reformulations and demonstrate their superiority over the state-of-the-art solution schemes on instances of least squares, project management, and multi-item newsvendor problems.
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Efficient Representation of Numerical Optimization Problems for SNARKs
This paper introduces Otti, a general-purpose com-
piler for (zk)SNARKs that provides support for numerical op-
timization problems. Otti produces efficient arithmetizations
of programs that contain optimization problems including
linear programming (LP), semi-definite programming (SDP),
and a broad class of stochastic gradient descent (SGD) instances.
Numerical optimization is a fundamental algorithmic
building block: applications include scheduling and resource
allocation tasks, approximations to NP-hard problems, and
training of neural networks. Otti takes as input arbitrary programs
written in a subset of C that contain optimization problems
specified via an easy-to-use API. Otti then automatically
produces rank-1 constraint satisfiability (R1CS) instances that
express a succinct transformation of those programs. Correct
execution of the transformed program implies the optimality
of the solution to the original optimization problem. Our
evaluation on real benchmarks shows that Otti, instantiated
with the Spartan proof system, can prove the optimality of
solutions in zero-knowledge in as little as 100 ms—over 4
orders of magnitude faster than existing approaches.
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- NSF-PAR ID:
- 10392347
- Date Published:
- Journal Name:
- USENIX Security Symposium
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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