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Abstract

A framework is presented for designing a class of distributed, asynchronous optimization algorithms realized as signal processing architectures utilizing various conservation principles. The architectures are specifically based on stationary conditions pertaining to primal and dual variables in a class of generally nonconvex optimization problems. The stationary conditions, which are closely related to the principles of stationary content and co-content that naturally arise from Tellegen's theorem in electrical networks, are transformed via a linear change of coordinates to obtain a set of linear and nonlinear maps that form the basis for implementation. The resulting algorithms can operate by processing a linear superposition of primal and dual decision variables using the associated maps, coupled using synchronous or asynchronous delay elements to form a distributed system. Tables are provided containing example elements that can be assembled to form various optimization algorithms directly from the associated problem statements.

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