全同态加密中经常用到准多项式时间、对数多项式时间、亚指数时间等等。下面这张表详细列出了这些概念的意义及范例。表中 poly( x ) = x O (1) ,表示关于x的一个多项式。 Name Complexity class Running time ( T ( n )) Examples of running times Example algorithms constant time O (1) 10 Determining if an integer (represented in binary) is even or odd inverse Ackermann time O (α(n)) Amortized time per operation using a disjoint set iterated logarithmic time O ( log * n ) Distributed coloring of cycles log-logarithmic O (log log n ) Amortized time per operation using a bounded priority queue logarithmic time DLOGTIME O (log n ) log n , log( n 2 ) Binary search polylogarithmic time poly(log n ) (log n ) 2 fractional power O ( n c ) where 0 c 1 n 1/2 , n 2/3 Searching in a kd-tree linear time O ( n ) n Finding the smallest item in an unsorted array n log star n time O ( n log * n ) Seidel 's polygon triangulation algorithm. linearithmic time O ( n log n ) n log n , log n ! Fastest possible comparison sort quadratic time O ( n 2 ) n 2 Bubble sort ; Insertion sort ; Direct convolution cubic time O ( n 3 ) n 3 Naive multiplication of two n × n matrices. Calculating partial correlation . polynomial time P 2 O (log n ) = poly( n ) n , n log n , n 10 Karmarkar's algorithm for linear programming ; AKS primality test quasi-polynomial time QP 2 poly(log n ) n log log n , n log n Best-known O(log 2 n )- approximation algorithm for the directed Steiner tree problem . sub-exponential time (first definition) SUBEXP O (2 n ε ) for all ε 0 O (2 log n log log n ) Assuming complexity theoretic conjectures, BPP is contained in SUBEXP. sub-exponential time (second definition) 2 o ( n ) 2 n 1/3 Best-known algorithm for integer factorization and graph isomorphism exponential time E 2 O ( n ) 1.1 n , 10 n Solving the traveling salesman problem using dynamic programming factorial time O ( n !) n ! Solving the traveling salesman problem via brute-force search exponential time EXPTIME 2 poly( n ) 2 n , 2 n 2 double exponential time 2-EXPTIME 2 2 poly( n ) 2 2 n Deciding the truth of a given statement in Presburger arithmetic
引子:黑莓手机,加密过度无法监听 神经元编码规则,加密以去除干扰,典型isi频段差别。 ISI是形态决定还是化学通道决定,SP快慢决定峰值位置,——位置信息 分布函数的劈峰: 气体chotic,液体phasic, 晶体 tonic的SP 稳态模型,律动的吸引子?多稳态,多吸引子? brain like do LOG coding! “数字感”的收获 Dr. Eugene M. Izhikevich