Search results
Results from the WOW.Com Content Network
The following example demonstrates dynamic loop unrolling for a simple program written in C. Unlike the assembler example above, pointer/index arithmetic is still generated by the compiler in this example because a variable (i) is still used to address the array element.
[1] The subset sum problem is a special case of the decision and 0-1 problems where each kind of item, the weight equals the value: =. In the field of cryptography, the term knapsack problem is often used to refer specifically to the subset sum problem. The subset sum problem is one of Karp's 21 NP-complete problems.
B will denote the best solution found so far, and will be used as an upper bound on candidate solutions. Initialize a queue to hold a partial solution with none of the variables of the problem assigned. Loop until the queue is empty: Take a node N off the queue. If N represents a single candidate solution x and f(x) < B, then x is the best ...
[9]: 52 With insertion as the code below shows, the adequate rotation immediately perfectly rebalances the tree. In figure 1, by inserting the new node Z as a child of node X the height of that subtree Z increases from 0 to 1. Invariant of the retracing loop for an insertion. The height of the subtree rooted by Z has increased by 1.
The question "does there exist a simple path in a given graph with at least k edges" is NP-complete. [ 2 ] In weighted complete graphs with non-negative edge weights, the weighted longest path problem is the same as the Travelling salesman path problem , because the longest path always includes all vertices.
Greedy algorithms fail to produce the optimal solution for many other problems and may even produce the unique worst possible solution. One example is the travelling salesman problem mentioned above: for each number of cities, there is an assignment of distances between the cities for which the nearest-neighbour heuristic produces the unique ...
The path from the root 1 to a number q in the Stern–Brocot tree may be found by a binary search algorithm, which may be expressed in a simple way using mediants. Augment the non-negative rational numbers to including a value 1 / 0 (representing +∞) that is by definition greater than all other rationals.
The left figure below shows a binary decision tree (the reduction rules are not applied), and a truth table, each representing the function (,,).In the tree on the left, the value of the function can be determined for a given variable assignment by following a path down the graph to a terminal.