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1
ACCELERATING SUBMODULAR MINIMIZATION
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ADAPTIVE COMPLEXITY MODEL
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EXPONENTIALLY FASTER
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LOTS OF PROGRESS ON ADAPTIVI
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ADAPTIVITY OF CONVEX
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ADAPTIVITY OF SUBMODULAR
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MAIN RESULT
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THE PARTITION OF ELEMENTS
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THE FUNCTION
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THE FULL FUNCTION
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THE MAIN LEMMA
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OTHER LEMMAS
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INDISTINGUISHABILITY
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CONCLUSION
Description:
Explore a groundbreaking lecture on parallel submodular minimization, delving into the acceleration of this computational process. Examine the adaptive complexity model and discover how it enables exponentially faster solutions. Investigate the significant progress made in adaptive approaches, focusing on the adaptivity of convex and submodular functions. Uncover the main result of the research, including the partition of elements, the function structure, and the full function analysis. Study the main lemma and other supporting lemmas that contribute to the proof. Analyze the concept of indistinguishability and its implications. Conclude with a comprehensive understanding of the lower bound for parallel submodular minimization and its significance in computational theory.

A Lower Bound for Parallel Submodular Minimization

Association for Computing Machinery (ACM)
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