Keywords space complexityLower boundsThis paper explores the power of catalytic computation when the catalytic space (c(n), the full memory whose content needs to be restored to the original by the end of compu
This question can be done, in O(N*W) Time and Space complexity using both top-down and bottom-up approach, and I can further reduce the space to O(2*W) and time O(N*W) using bottom-up approach [link to the bottom-up approach : Link], but I'm unable to think of a similar ...
In Section 3, our new heuristic is presented along with a complexity analysis, which shows how the algorithm can run in O(n3c) time and use O(n3c) of memory space. A reduced-memory version that will only require O(nc) of memory space is then presented in Section 4 along with our ...
Due to the large scale and complexity of such NP- hard problems, classical algorithms typically either require unrealistic running times or fail to produce optimal solutions. In light of these limitations, quantum computing offers an attractive alternative for more efficient solutions to combinatorial ...
complexity of O(n) is optimal. 2. Are there general techniques that apply uniformly to a range of synopsis structures? 3. Can we demonstrate that the working space is the total space for many relevant applications? 4. Do such techniques allow “more”, i.e., improve ...
Attempts to characterise such search spaces faces increasing the computational complexity of most learning algorithms - for which the number of input features and sample size are critical parameters. To reduce space and computational complexities, the number of fea- tures of a given problem should be...
She moved with the coordinated complexity of a spider, all four limbs grasping at the rock and ice. To an observer, she was a comical sight. She looked like a barbell with arms and legs and a bulge at the top that just might be a head. There were no creases or sharp lines ...
Recently, in =-=[5]-=-, Chang and Chen improved Karnin's idea. By allowing more power to the processing units, they can solve the knapsack problem with S 2 P = O(2 s=2 ). However, the time complexity of their algorithm is ......
fine-grained complexityspace-time trade-offexponential time algorithmsWe present randomized algorithms that solve subset sum and knapsack instances with n items in O*((20.86n)) time, where the O* (.) notation suppresses factors polynomial in the input size, and polynomial space, assuming random ...
Knapsack problemParallel algorithmsDynamic programmingUpper-bound complexityThree new parallel scalable algorithms for solving the Subset-Sum Problem in O(n/p(C-w{sub}(min))) time and O(n + c) space in the PRAM model are presented, where n is the number of objects, c is the capacity, w...