Radial Extremality for LRU Caching and the Fill--Holst Conjecture
LRU缓存的径向极值性与Fill-Holst猜想
Christopher D. Long
AI总结 本文证明在独立参考模型中,均匀流行度向量是LRU缓存命中率的唯一全局最小化器,并沿均匀向量出发的射线严格递增,从而验证了Fill-Holst关于移动至前端规则的Schur-凹性猜想的径向部分。
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对于具有流行度向量$p\in\Delta_N^\circ$的独立参考模型,令$H_C(p)$表示容量为$C$的LRU缓存的精确稳态命中率。我们证明,对于每个$1\le C<N$,均匀流行度向量是内部单纯形上$H_C$的唯一全局最小化器。更尖锐地,沿着从均匀向量到内部点的每个非常数线段,LRU命中率严格递增。证明使用了稳态LRU缓存的标准指数年龄表示,并给出了径向导数的显式正对平方公式。等价地,对于移动至前端规则,沿远离均匀的每条非常数射线,稳态搜索成本分布在通常的随机序下严格改善。这证明了Fill-Holst关于移动至前端搜索成本尾部的Schur-凹性猜想的径向限制。特别地,所有LRU未命中概率和所有非常数非递减栈深度成本沿此类射线严格递减。该结果是径向的而非Schur-凸的:已知LRU的全优序单调性不成立,而证明识别了在均匀向量出发的射线上存续的特殊正性。
For the independent reference model with popularity vector $p\inΔ_N^\circ$, let $H_C(p)$ denote the exact stationary hit rate of an LRU cache of capacity $C$. We prove that, for every $1\le C<N$, the uniform popularity vector is the unique global minimizer of $H_C$ on the interior simplex. More sharply, along every nonconstant segment from the uniform vector to an interior point, the LRU hit rate is strictly increasing. The proof uses the standard exponential-age representation of the stationary LRU cache and gives an explicit positive pair-square formula for the radial derivative. Equivalently, for the move-to-front rule, the stationary search-cost distribution improves strictly in the usual stochastic order along every nonconstant ray away from uniform. This proves the radial restriction of the Fill--Holst Schur-concavity conjecture for move-to-front search-cost tails. In particular, all LRU miss probabilities and all nonconstant nondecreasing stack-depth costs decrease strictly along such rays. The result is radial rather than Schur-convex: full majorization monotonicity for LRU is known to fail, and the proof identifies the special positivity that survives on rays from the uniform vector.