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• W2969523654 abstract "Interior-point or barrier methods handle nonlinear programs by sequentially solving barrier subprograms with a decreasing sequence of barrier parameters. The specific barrier update rule strongly influences the theoretical convergence properties as well as the practical efficiency. While many global and local convergence analyses consider a monotone update that decreases the barrier parameter for every approximately solved subprogram, computational studies show a superior performance of more adaptive strategies. In this paper we interpret the adaptive barrier update as a reinforcement learning task. A deep Q-learning agent is trained by both imitation and random action selection. Numerical results based on an implementation within the nonlinear programming solver WORHP show that the agent successfully learns to steer the barrier parameter and additionally improves WORHP’s performance on the CUTEst test set." @default.
• W2969523654 created "2019-08-29" @default.
• W2969523654 creator A5078992848 @default.
• W2969523654 date "2019-12-01" @default.
• W2969523654 modified "2023-09-26" @default.
• W2969523654 title "Learning to steer nonlinear interior-point methods" @default.
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• W2969523654 doi "https://doi.org/10.1007/s13675-019-00118-4" @default.
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