On the Sum-of-Squares algorithm for bin packing

In this article we present a theoretical analysis of the online <i>Sum-of-Squares</i> algorithm (<i>SS</i>) for bin packing along with several new variants. <i>SS</i> is applicable to any instance of bin packing in which the bin capacity <i>B</i> and item sizes <i>s</i>(<i>a</i>) are integral (or can be scaled to be so), and runs in time <i>O</i>(<i>nB</i>). It performs remarkably well from an average case point of view: For any discrete distribution in which the optimal expected waste is sublinear, <i>SS</i> also has sublinear expected waste. For any discrete distribution where the optimal expected waste is bounded, <i>SS</i> has expected waste at most <i>O</i>(log <i>n</i>). We also discuss several interesting variants on <i>SS</i>, including a randomized <i>O</i>(<i>nB</i> log <i>B</i>)-time online algorithm <i>SS</i>* whose expected behavior is essentially optimal for all discrete distributions. Algorithm <i>SS</i>* depends on a new linear-programming-based pseudopolynomial-time algorithm for solving the NP-hard problem of determining, given a discrete distribution <i>F</i>, just what is the growth rate for the optimal expected waste.

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