Computing lower and upper bounds on the competitive ratio of online algorithms is a challenging question: For a minimization combinatorial problem, proving a competitive ratio for a given algorithm leads to an upper bound. However computing lower bounds requires a proof on all algorithms. This can be modeled as a 2-player game where a strategy for one of the players is a proof for the lower bound. The tree representing the proof can can be found computationally. This method has been used with success on the online bin stretching problem where a set of items must be packed online in $m$ bins. The items are guaranteed to fit into the $m$ bins. However, the online procedure might require to stretch the bins to a larger capacity in order to be able to pack all the items. This stretching factor is the objective to be minimized. We propose original ideas to strongly improve the speed of computer searches for lower bound: propagate the game states that can be pruned from the search and improve the speed and memory usage in the dynamic program which is used in the search. These improvements allowed to increase significantly the speed of the search and hence to prove new lower bounds for the bin stretching problem for 6, 7 and 8 bins.
翻译:在线算法竞争比率的下限和上界计算是一个具有挑战性的问题: 对于最小化组合问题, 证明特定算法的竞争性比率会导致上界。 但是, 计算下界需要所有算法的证明。 这可以模拟为二玩游戏, 其中一个玩家的策略是下界的证明。 代表该证据的树可以被计算找到。 这种方法在网上书包拉伸问题上得到了成功使用, 因为在网上书包中, 一组物品必须用美元包装在书箱中。 这些项目可以被保证安装在$m的书箱中。 然而, 在线程序可能需要将书箱拉伸到更大的容量, 才能包装所有项目。 这个拉伸系数是最小化的目标。 我们提出原始想法, 大力提高下界的计算机搜索速度 : 传播游戏状态, 可以在搜索中进行浏览, 并改进搜索中使用的动态程序的速度和记忆用量。 这些改进可以大大提高搜索速度, 从而证明在8 和 bin 6 和 bin 中显示新的打开问题 。