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.

Abstract: Methods and supporting systems for allocating a resource among multiple agents are disclosed. Multipoint-to-multipoint communication is established among the agents, with each agent using an amount of a resource. A first agent receives information associated with the resource usage of a second agent and determines the difference in resource usage between itself and the second agent. Based in part on the resource usage difference, the first agent is controlled to modify its use of the resource.

Abstract: Methods and supporting systems for allocating a resource among multiple agents are disclosed. Multipoint-to-multipoint communication is established among the agents, with each agent using an amount of a resource. A first agent receives information associated with the resource usage of a second agent and determines the difference in resource usage between itself and the second agent. Based in part on the resource usage difference, the first agent is controlled to modify its use of the resource.

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.

Abstract: Methods and supporting systems for allocating a resource among multiple agents are disclosed. Multipoint-to-multipoint communication is established among the agents, with each agent using an amount of a resource. A first agent receives information associated with the resource usage of a second agent and determines the difference in resource usage between itself and the second agent. Based in part on the resource usage difference, the first agent is controlled to modify its use of the resource.

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.

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.

Abstract: Methods and supporting systems for allocating a resource among multiple agents are disclosed. Multipoint-to-multipoint communication is established among the agents, with each agent using an amount of a resource. A first agent receives information associated with the resource usage of a second agent and determines the difference in resource usage between itself and the second agent. Based in part on the resource usage difference, the first agent is controlled to modify its use of the resource.