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FOCS2020顶会

Bipartite Matching in Nearly-linear Time on Moderately Dense Graphs

Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Aaron Sidford, Zhao Song, Di Wang

2020年份
72被引次数
38顶会引用

摘要

We write [n] for the interval 1, 2, ..., n. For a set I ⊂ [n] we also use I as 0/1-vector with I i = 1 when i ∈ I and I i = 0 otherwise. We write e i for the i-th standard unit vector. We use O(•) notation to hide (log log W ) O(1) and (log n) O(1) factors, where W typically denotes the largest absolute value used for specifying any value in the problem (e.g. demands and edge weights) and n denotes the number of nodes.

When we write with high probability (or w.h.p), we mean with probability 1 -n c for any constant c > 0.

For x ∈ R n , we use x i to denote the i-th coordinate of vector x if the symbol x is simple. If the symbol is complicated, we use (x) i or [x] i to denote the i-th coordinate of vector x (e.g. (δ s ) i ).

We write 1 condition for the indicator variable, which is 1 if the condition is true and 0 otherwise.

Given a vector v ∈ R d for some d, we write Diag(v) for the d×d diagonal matrix with Diag(v) i,i = v i . For vectors x, s, s, x, x t , s t , w, w, w t , τ, g we let X def = Diag(x), S def = Diag(s), and define X, S, X t , S t , W, W, W t , T, G analogously.

Given vectors u, v ∈ R d for some d, we perform arithmetic operations •, +, -, /, √ • element-wise. For example (u•v

For the inner product we will write u, v and u v instead. For a vector v ∈ R d and a scalar α ∈ R we have (αv) i = αv i and we extend this notation to other arithmetic operations, e.g.

For symmetric matrices A, B ∈ R n×n we write A B to indicate that x Ax ≤ x Bx for all x ∈ R n and define , ≺, and analogously. We let S n×n >0 ⊆ R n×n denote the set of n × n symmetric positive definite matrices. We call any matrix (not necessarily symmetric) non-degenerate if its rows are all non-zero and it has full column rank.

We use a ≈ b to denote that exp(-)b ≤ a ≤ exp( )b entrywise and A ≈ B to denote that exp(-)B A exp( )B. Note that this notation implies a ≈ b ≈ δ c ⇒ a ≈ +δ c, and a ≈ b ⇒ a x ≈ •x b x for x ≥ 0.

For any matrix A over reals, let nnz(A) denote the number of non-zero entries in A.

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