The concept of submodularity has wide applications in data science, artificial intelligence, and machine learning, providing a boost to the investigation of new ideas, innovative techniques, and creative algorithms to...
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The concept of submodularity has wide applications in data science, artificial intelligence, and machine learning, providing a boost to the investigation of new ideas, innovative techniques, and creative algorithms to solve different submodular optimization problems arising from a diversity of applications. However pure submodular problems only represent a small portion of the problems we are facing in real life applications. In this paper, we further discuss the two-stage submodular maximization problem under a l-matroid constraint. We design an approximation algorithm with constant approximation ratio with respect to the curvature, which improves the previous bound. In addition, we generalize our algorithm to the two-stage submodular maximization problem under a l-exchange system constraint.
submodular function optimization has been widely studied in machine learning and economics, which is a relatively new research field in the context of big data and has attracted more attention. In this paper, we consi...
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ISBN:
(数字)9783030926816
ISBN:
(纸本)9783030926816;9783030926809
submodular function optimization has been widely studied in machine learning and economics, which is a relatively new research field in the context of big data and has attracted more attention. In this paper, we consider a two-stage submodular maximization problem subject to cardinality and p-matroid constraints, and propose an approximation algorithm with constant approximation ratio depends on the maximum curvature of the submodular functions involved, which generalizes the previous bound.
The concept of submodularity finds wide applications in data science, artificial intelligence, and machine learning, providing a boost to the investigation of new ideas, innovative techniques, and creative algorithms ...
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ISBN:
(纸本)9783031203497;9783031203503
The concept of submodularity finds wide applications in data science, artificial intelligence, and machine learning, providing a boost to the investigation of new ideas, innovative techniques, and creative algorithms to solve different submodular optimization problems arising from a diversity of applications. However pure submodular problems only represent a small portion of the problems we are facing in real life applications. To solve these optimization problems, an important research method is to describe the characteristics of the non-submodular functions. The non-submodular functions is a hot research topic in the study of nonlinear combinatorial optimizations. In this paper, we combine and generalize the curvature and the generic submodularity ratio to design an approximation algorithm for two-stage non-submodularmaximization under a matroid constraint.
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