
doi: 10.1137/0218063
Summary: Consider a probability measure \(\mu\) on [0,1] and an independent sequence of random variables \(X_ 1,...,X_ n,..\). distributed according to \(\mu\). No regularity assumptions are made on \(\mu\). Denote by \(B(X_ 1,...,X_ n)\) the number of unit-size bins that are used by Best Fit Decreasing to pack items of size \(X_ 1,...,X_ n\). The existence of a constant b(\(\mu)\) is proven such that for each \(\epsilon >0\), \[ \sum_{n\geq 1}P(| n^{-1}B(X_ 1,...,X_ n)-b(\mu)| \geq \epsilon)<\infty. \]
Combinatorial optimization, Analysis of algorithms and problem complexity, complete convergence, probabilistic analysis, independent sequence of random variables, Operations research and management science, Numerical mathematical programming methods, Applications of queueing theory (congestion, allocation, storage, traffic, etc.), bin packing, probability measure, Edge subsets with special properties (factorization, matching, partitioning, covering and packing, etc.), unit-size bins, Best Fit Decreasing, basic matching, approximation algorithm
Combinatorial optimization, Analysis of algorithms and problem complexity, complete convergence, probabilistic analysis, independent sequence of random variables, Operations research and management science, Numerical mathematical programming methods, Applications of queueing theory (congestion, allocation, storage, traffic, etc.), bin packing, probability measure, Edge subsets with special properties (factorization, matching, partitioning, covering and packing, etc.), unit-size bins, Best Fit Decreasing, basic matching, approximation algorithm
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