Distributed Optimization Over Time-Varying Graphs With Imperfect Sharing of Information

Distributed Convex Optimization with Inequality Constraints over Time-varying Unbalanced Digraphs

This paper considers a distributed convex optimization problem with inequality constraints over time-varying unbalanced digraphs, where the cost function is a sum of local objectives, and each node of the graph only knows its local objective and inequality constraints. Although there is a vast literature on distributed optimization, most of them require the graph to be balanced, which is quite ...

Data-driven inverse optimization with imperfect information

In data-driven inverse optimization an observer aims to learn the preferences of an agent who solves a parametric optimization problem depending on an exogenous signal. Thus, the observer seeks the agent’s objective function that best explains a historical sequence of signals and corresponding optimal actions. We focus here on situations where the observer has imperfect information, that is, wh...

Capacity of time-varying channels with imperfect CSI

Mobile channels suffer from multipath fading and necessitate transmitter adaptation to achieve Shannon capacity. The capacity achieving transmitter adaptation strategy depends on how much channel side information (CSI) is available at the transmitter (CSIT) and the receiver (CSIR). When CSI is perfectly known at both sides, it is shown in [10] that for AWGN channels with flat fading, the optima...

On the linear convergence of distributed optimization over directed graphs

This paper develops a fast distributed algorithm, termed DEXTRA, to solve the optimization problem when n agents reach agreement and collaboratively minimize the sum of their local objective functions over the network, where the communication between the agents is described by a directed graph. Existing algorithms solve the problem restricted to directed graphs with convergence rates of O(ln k/...

Imperfect Inspection Games Over Time