Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a network entity may generate a data flow model for a domain associated with the network entity, the domain being part of a multi-domain network. The network entity may obtain a set of measured flow rates and a set of expected flow rates that are calculated based at least in part on the data flow model. The network entity may selectively update the data flow model based at least in part on an accuracy of the data flow model, with the accuracy determined based at least in part on the set of measured flow rates and the set of expected flow rates. Numerous other aspects are described.

Abstract: A processor-implemented method includes receiving a network topology describing a network. The method also includes receiving a set of traffic patterns for the network, and a set of network upgrade plans for the network. The method obtains a set of performance parameters from a list of bottleneck structures based on the set of traffic patterns and the network topology, for each upgrade plan. The method then selects a preferred network upgrade plan from the set of network upgrade plans based on the performance parameters.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a network entity may generate a first data flow model for a first domain associated with the network entity. The network entity may receive an indication of a second data flow model for a second domain that is different from the first domain. The network entity may selectively update the first data flow model based at least in part on whether the indication of the second data flow model indicates an error in the first data flow model. Numerous other aspects are described.

Abstract: A processor-implemented method includes receiving, as input, an array of values characterizing a polynomial feasibility problem representing a physical system for solving a computational problem. The method also includes reducing dimensions of the polynomial feasibility problem by transforming the polynomial feasibility problem into a high-dimensional linear feasibility problem and a non-linear feasibility problem. The method further includes solving the high-dimensional linear feasibility problem to obtain a first set of interim solutions. The method includes solving the non-linear feasibility problem based on the first set of interim solutions to obtain a result of the non-linear feasibility problem. The method also includes outputting parameters characterizing the physical system, with a ground state corresponding to an output solution of the computational problem based on the result obtained from solving the non-linear feasibility problem.