In largescale applications including medical imaging, collocation differential equation solvers, and estimation with differential privacy, the underlying linear inverse problem can be reformulated as a streaming problem. In theory, the streaming problem can be effectively solved using memoryefficient, exponentiallyconverging streaming solvers. In special cases when the underlying linear inverse problem is finitedimensional, streaming solvers can periodically evaluate the residual norm at a substantial computational cost. When the underlying system is infinite dimensional, streaming solver can only access noisy estimates of the residual. While such noisy estimates are computationally efficient, they are useful only when their accuracy is known. In this work, we rigorously develop a general family of computationallypractical residual estimators and their uncertainty sets for streaming solvers, and we demonstrate the accuracy of our methods on a number of largescale linear problems. Thus, we further enable the practical use of streaming solvers for important classes of linear inverse problems.
Given the costs and a feasible solution for a minimum cost flow problem on a countably infinite network, inverse optimization involves finding new costs that are close to the original ones and that make the given solution optimal. We study this problem using the weighted absolute sum metric to quantify closeness of cost vectors. We provide sufficient conditions under which known results from inverse optimization in minimum cost flow problems on finite networks extend to the countably infinite case. These conditions ensure that recent duality results on countably infinite linear programs can be applied to our setting. Specifically, they enable us to prove that the inverse optimization problem can be reformulated as a capacitated, minimum cost circulation problem on a countably infinite network. Finite‐dimensional truncations of this problem can be solved in polynomial time when the weights equal one, which yields an efficient solution method. The circulation problem can also be solved via the shadow simplex method, where each finite‐dimensional truncation is tackled using the usual network Simplex algorithm that is empirically known to be computationally efficient. We illustrate these results on an infinite horizon shortest path problem.
more » « less NSFPAR ID:
 10462398
 Publisher / Repository:
 Wiley Blackwell (John Wiley & Sons)
 Date Published:
 Journal Name:
 Networks
 Volume:
 73
 Issue:
 3
 ISSN:
 00283045
 Page Range / eLocation ID:
 p. 292305
 Format(s):
 Medium: X
 Sponsoring Org:
 National Science Foundation
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