Quenched invariance principle for a class of random conductance models with long-range jumps

被引:10
|
作者
Biskup, Marek [1 ]
Chen, Xin [2 ]
Kumagai, Takashi [3 ]
Wang, Jian [4 ,5 ,6 ]
机构
[1] UCLA, Dept Math, Los Angeles, CA 90024 USA
[2] Shanghai Jiao Tong Univ, Dept Math, Shanghai, Peoples R China
[3] Kyoto Univ, Res Inst Math Sci, Kyoto, Japan
[4] Fujian Normal Univ, Coll Math & Informat, Fuzhou, Peoples R China
[5] Fujian Normal Univ, Fujian Key Lab Math Anal Applicat, Fuzhou, Peoples R China
[6] Fujian Normal Univ, Ctr Appl Math Fujian Prov FDIU, Fuzhou, Peoples R China
基金
美国国家科学基金会; 中国国家自然科学基金;
关键词
SIMPLE RANDOM-WALK; HEAT-KERNEL DECAY; REVERSIBLE MARKOV-PROCESSES; UPPER-BOUNDS; CHEMICAL DISTANCE; PERCOLATION; CLUSTERS;
D O I
10.1007/s00440-021-01059-z
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
We study random walks on Z(d) (with d >= 2) among stationary ergodic random conductances {C-x,C-y : x, y is an element of Z(d)} that permit jumps of arbitrary length. Our focus is on the quenched invariance principle (QIP) which we establish by a combination of corrector methods, functional inequalities and heat-kernel technology assuming that the p-th moment of Sigma(x is an element of Zd) C-0,C-x vertical bar x vertical bar(2) and q-th moment of 1/C-0,C-x for x neighboring the origin are finite for some p, q >= 1 with p(-1)+q(-1) < 2/d. In particular, a QIP thus holds for random walks on long-range percolation graphs with connectivity exponents larger than 2d in all d >= 2, provided all the nearest-neighbor edges are present. Although still limited by moment conditions, our method of proof is novel in that it avoids proving everywhere-sublinearity of the corrector. This is relevant because we show that, for long-range percolation with exponents between d + 2 and 2d, the corrector exists but fails to be sublinear everywhere. Similar examples are constructed also for nearest-neighbor, ergodic conductances in d >= 3 under the conditions complementary to those of the recent work of Bella and Schaffner (Ann Probab 48(1):296-316, 2020). These examples elucidate the limitations of elliptic-regularity techniques that underlie much of the recent progress on these problems.
引用
收藏
页码:847 / 889
页数:43
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