Despite significant advances on distributed continuous-time optimization of multi-agent networks, there is still lack of an efficient algorithm to achieve the goal of distributed optimization at a pre-specified time. Herein, we design a specified-time distributed optimization algorithm for connected agents with directed topologies to collectively minimize the sum of individual objective functions subject to an equality constraint. With the designed algorithm, the settling time of distributed optimization can be exactly predefined. The specified selection of such a settling time is independent of not only the initial conditions of agents, but also the algorithm parameters and the communication topologies. Furthermore, the proposed algorithm can realize specified-time optimization by exchanging information among neighbours only at discrete sampling instants and thus reduces the communication burden. In addition, the equality constraint is always satisfied during the whole process, which makes the proposed algorithm applicable to online solving distributed optimization problems such as economic dispatch. For the special case of undirected communication topologies, a reduced-order algorithm is also designed. Finally, the effectiveness of the theoretical analysis is justified by numerical simulations.
CGPOPS: A C++ Software for Solving Multiple-Phase Optimal Control Problems Using Adaptive Gaussian Quadrature Collocation and Sparse Nonlinear Programming
Solving a class of stochastic optimal control problems by physics-informed neural networks
School of Mathematics and Statistics（数学与统计学学院）；MOE Key Laboratory for Complexity Science in Aerospace（复杂科学在航空航天中的关键实验室）；State Key Laboratory of Fluid Power and Mechatronic Systems, Department of Mechanics（流体动力与机械系统国家重点实验室，力学系）
Quasi-Newton Methods for Machine Learning: Forget the Past, Just Sample
University of Michigan（密歇根大学）；Lehigh University（利哈伊大学）；KAUST（阿卜杜拉国王科技大学）；Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学）
