Learning to optimize(L2O)stands at the intersection of traditional optimization and machine learning,utilizing the capabilities of machine learning to enhance conventional optimization techniques.As real-world optimiz...Learning to optimize(L2O)stands at the intersection of traditional optimization and machine learning,utilizing the capabilities of machine learning to enhance conventional optimization techniques.As real-world optimization problems frequently share common structures,L2O provides a tool to exploit these structures for better or faster solutions.This tutorial dives deep into L2O techniques,introducing how to accelerate optimization algorithms,promptly estimate the solutions,or even reshape the optimization problem itself,making it more adaptive to real-world applications.By considering the prerequisites for successful applications of L2O and the structure of the optimization problems at hand,this tutorial provides a comprehensive guide for practitioners and researchers alike.展开更多
At present, most administrative institutions have been in accordance with the requirements of the construction of internal control system of the unit. To a certain extent, improve the efficiency of the unit and the ab...At present, most administrative institutions have been in accordance with the requirements of the construction of internal control system of the unit. To a certain extent, improve the efficiency of the unit and the ability to serve the public. But in recent years, administrative institutions still exposed many problems, the main reason lies in the unit's internal control construction is not perfect, and internal control effect is not good. From the perspective of adhering to the rule of law, promoting the modernization of national governance system and capacity, and strengthening the supervision and restriction of power, it is very important to strengthen and improve the construction of internal control of administrative institutions. Only real effective internal control can play its due role. Therefore, in this paper, the "internal control standards of administrative institutions (trial)" as the system standard, from the unit level and business level of the two levels of internal control of administrative institutions to promote the effective operation of internal control of administrative institutions.展开更多
Multi-objective bi-level optimization(MOBLO)addresses nested multi-objective optimization problems common in a range of applications.However,its multi-objective and hierarchical bi-level nature makes it notably comple...Multi-objective bi-level optimization(MOBLO)addresses nested multi-objective optimization problems common in a range of applications.However,its multi-objective and hierarchical bi-level nature makes it notably complex.Gradient-based MOBLO algorithms have recently grown in popularity,as they effectively solve crucial machine learning problems like meta-learning,neural architecture search,and reinforcement learning.Unfortunately,these algorithms depend on solving a sequence of approximation subproblems with high accuracy,resulting in adverse time and memory complexity that lowers their numerical efficiency.To address this issue,we propose a gradient-based algorithm for MOBLO,called gMOBA,which has fewer hyperparameters to tune,making it both simple and efficient.Additionally,we demonstrate the theoretical validity by accomplishing the desirable Pareto stationarity.Numerical experiments confirm the practical efficiency of the proposed method and verify the theoretical results.To accelerate the convergence of gMOBA,we introduce a beneficial L2O(learning to optimize)neural network(called L2O-gMOBA)implemented as the initialization phase of our gMOBA algorithm.Comparative results of numerical experiments are presented to illustrate the performance of L2O-gMOBA.展开更多
文摘Learning to optimize(L2O)stands at the intersection of traditional optimization and machine learning,utilizing the capabilities of machine learning to enhance conventional optimization techniques.As real-world optimization problems frequently share common structures,L2O provides a tool to exploit these structures for better or faster solutions.This tutorial dives deep into L2O techniques,introducing how to accelerate optimization algorithms,promptly estimate the solutions,or even reshape the optimization problem itself,making it more adaptive to real-world applications.By considering the prerequisites for successful applications of L2O and the structure of the optimization problems at hand,this tutorial provides a comprehensive guide for practitioners and researchers alike.
文摘At present, most administrative institutions have been in accordance with the requirements of the construction of internal control system of the unit. To a certain extent, improve the efficiency of the unit and the ability to serve the public. But in recent years, administrative institutions still exposed many problems, the main reason lies in the unit's internal control construction is not perfect, and internal control effect is not good. From the perspective of adhering to the rule of law, promoting the modernization of national governance system and capacity, and strengthening the supervision and restriction of power, it is very important to strengthen and improve the construction of internal control of administrative institutions. Only real effective internal control can play its due role. Therefore, in this paper, the "internal control standards of administrative institutions (trial)" as the system standard, from the unit level and business level of the two levels of internal control of administrative institutions to promote the effective operation of internal control of administrative institutions.
基金supported by the Major Program of National Natural Science Foundation of China(Grant Nos.11991020 and 11991024)supported by National Natural Science Foundation of China(Grant No.12371305)+2 种基金supported by National Natural Science Foundation of China(Grant No.12222106)Guangdong Basic and Applied Basic Research Foundation(Grant No.2022B1515020082)Shenzhen Science and Technology Program(Grant No.RCYX20200714114700072)。
文摘Multi-objective bi-level optimization(MOBLO)addresses nested multi-objective optimization problems common in a range of applications.However,its multi-objective and hierarchical bi-level nature makes it notably complex.Gradient-based MOBLO algorithms have recently grown in popularity,as they effectively solve crucial machine learning problems like meta-learning,neural architecture search,and reinforcement learning.Unfortunately,these algorithms depend on solving a sequence of approximation subproblems with high accuracy,resulting in adverse time and memory complexity that lowers their numerical efficiency.To address this issue,we propose a gradient-based algorithm for MOBLO,called gMOBA,which has fewer hyperparameters to tune,making it both simple and efficient.Additionally,we demonstrate the theoretical validity by accomplishing the desirable Pareto stationarity.Numerical experiments confirm the practical efficiency of the proposed method and verify the theoretical results.To accelerate the convergence of gMOBA,we introduce a beneficial L2O(learning to optimize)neural network(called L2O-gMOBA)implemented as the initialization phase of our gMOBA algorithm.Comparative results of numerical experiments are presented to illustrate the performance of L2O-gMOBA.
