algorithm

问题 The "Treiber Stack" is generally one of the simplest lock-free data structures, and so it is often used when teaching introductions to lock-free algorithms. I've seen many implementations of Treiber Stacks using C++ atomics. The algorithm itself is trivial, so the real challenge is handling all the other incidental details of lock-free data-structures, such as providing some way of performing safe memory reclamation, avoiding the ABA problem, and allocating nodes in a lock-free manner. This

问题 Given the data set below: a | b | c | d 1 | 3 | 7 | 11 1 | 5 | 7 | 11 1 | 3 | 8 | 11 1 | 5 | 8 | 11 1 | 6 | 8 | 11 Perform a reverse Cartesian product to get: a | b | c | d 1 | 3,5 | 7,8 | 11 1 | 6 | 8 | 11 I am currently working with scala, and my input/output data type is currently: ListBuffer[Array[Array[Int]]] I have come up with a solution (seen below), but feel it could be optimized. I am open to optimizations of my approach, and completely new approaches. Solutions in scala and c# are

问题 Given the data set below: a | b | c | d 1 | 3 | 7 | 11 1 | 5 | 7 | 11 1 | 3 | 8 | 11 1 | 5 | 8 | 11 1 | 6 | 8 | 11 Perform a reverse Cartesian product to get: a | b | c | d 1 | 3,5 | 7,8 | 11 1 | 6 | 8 | 11 I am currently working with scala, and my input/output data type is currently: ListBuffer[Array[Array[Int]]] I have come up with a solution (seen below), but feel it could be optimized. I am open to optimizations of my approach, and completely new approaches. Solutions in scala and c# are

问题 Given the data set below: a | b | c | d 1 | 3 | 7 | 11 1 | 5 | 7 | 11 1 | 3 | 8 | 11 1 | 5 | 8 | 11 1 | 6 | 8 | 11 Perform a reverse Cartesian product to get: a | b | c | d 1 | 3,5 | 7,8 | 11 1 | 6 | 8 | 11 I am currently working with scala, and my input/output data type is currently: ListBuffer[Array[Array[Int]]] I have come up with a solution (seen below), but feel it could be optimized. I am open to optimizations of my approach, and completely new approaches. Solutions in scala and c# are

问题 List the following functions in non-descending order of asymptotic growth rate. If two or more functions have the same asymptotic growth rate then group them together. g1(n) = n g2(n) = n^3 +4n g3(n) = 2n log(base 2) n g4(n) = 2^n g5(n) = 3 ^ (3 * log(base 3) n) g6(n) = 10^n I've been looking through several examples online but I have no idea how to do this, it just seems like a completely foreign concept to me. If anyone could help me, that would be much appreciated. how do I even calculate

问题 List the following functions in non-descending order of asymptotic growth rate. If two or more functions have the same asymptotic growth rate then group them together. g1(n) = n g2(n) = n^3 +4n g3(n) = 2n log(base 2) n g4(n) = 2^n g5(n) = 3 ^ (3 * log(base 3) n) g6(n) = 10^n I've been looking through several examples online but I have no idea how to do this, it just seems like a completely foreign concept to me. If anyone could help me, that would be much appreciated. how do I even calculate

问题 Directed Graph (|V|=a, |E|=b) is given. each vertexes has specific weight. we want for each vertex (1..a) find a vertex with maximum weight that can be reachable from that vertex. Update 1: one nice answer is prepare by @Paul in O(b + a log a). but I search for O(a + b) algorithms, if any? Is there any different efficient or fastest any other ways for doing it? 回答1: Yes, it's possible to modify Tarjan's SCC algorithm to solve this problem in linear time. Tarjan's algorithm uses two node

问题 I need a data structure that supports two operations - delete and search. Now, the delete operation should run in amortized O(1) time, while search should run in O(log n) time. Search operation should work as follows: look for value specified and if it is here, return value itself. Otherwise, return the nearest greater value (return inorder successor). What could this data structure be? 回答1: It could be a pair of data structures: binary search tree, holding values hash table, holding pointers

问题 Suppose that we wish to keep track of a point of maximum overlap in a set of intervals—a point that has the largest number of intervals in the database overlapping it. a. Show that there will always be a point of maximum overlap which is an endpoint of one of the segments. b. Design a data structure that efficiently supports the operations INTERVAL-INSERT, INTERVAL-DELETE, and FIND-POM, which returns a point of maximum overlap. (Hint: Keep a red-black tree of all the endpoints. Associate a

问题 I trying to generate all possible unique combination of items. Ex: item1, item2, item3 Combinations: item1+item2+item3 item1+item2 item1+item3 item2+item3 item1 item2 item3 I am unable to get an idea on how to solve this? for(int i=0;i<size;i++){ for(int j=i+1;j<size;j++){ System.out.println(list.item(i)+list.item(j)); } } The above code certainly works for all unique combination of two elements. But not for 3 element pair and more.. 回答1: If you have N items, count from 1 to 2^N-1. Each