作者: TIGER 時間: 2025-3-21 23:23
Branching,shed fast exponential time algorithms are branching algorithms. Furthermore, for many NP-hard problems the fastest known exact algorithm is a branching algorithm. Many of those algorithms have been developed during the last ten years by applying techniques like Measure & Conquer, quasiconvex analysi作者: Gobble 時間: 2025-3-22 02:08
Dynamic Programming,ning polynomial time algorithms as well as for designing exponential time algorithms. The main idea of dynamic programming is to start by solving small or trivial instances and then gradually resolving larger and harder subproblems by composing solutions from smaller subproblems. From this point of 作者: 容易生皺紋 時間: 2025-3-22 05:46 作者: 清唱劇 時間: 2025-3-22 09:39 作者: 裁決 時間: 2025-3-22 13:40
Subset Convolution,algorithm one relies on repeated use of dynamic programming, and in particular on the so-called fast zeta transform. In the latter sections we present various algorithmic applications of fast subset convolution. In this chapter the algorithms (may) operate with large numbers and thus we use the log-作者: 裁決 時間: 2025-3-22 18:04
Local Search and SAT,based on performing local search in balls in the Hamming space around some assignments. The first algorithm randomly chooses an assignment and performs a random walk of short length (in Hamming distance) to search for the solution. The second algorithm is deterministic and uses a similar idea; but i作者: Myelin 時間: 2025-3-23 00:33
Split and List,ply these algorithms on hard problems, we (exponentially) enlarge the size of a hard problem and apply fast polynomial time algorithm on an input of exponential size. The common way to enlarge the problem is to split the input into parts, and for each part to enumerate (or list) all possible solutio作者: fatty-acids 時間: 2025-3-23 03:39 作者: 廚房里面 時間: 2025-3-23 08:07 作者: BET 時間: 2025-3-23 11:17
Introduction, is a branching algorithm to compute a maximum independent set of a graph. The main idea of this algorithm can be traced back to the work of Miller and Muller [155] and Moon and Moser [161] from the nineteen sixties.作者: 分期付款 時間: 2025-3-23 13:58 作者: eczema 時間: 2025-3-23 18:13
Inclusion-Exclusion,n particular when direct counting is not possible. This counting principle is the main tool when designing inclusionexclusion algorithms. It seems that this algorithm design paradigm is suited very well to constructing fast exponential time algorithms since it naturally produces exponential time algorithms.作者: bacteria 時間: 2025-3-24 02:15 作者: 馬具 時間: 2025-3-24 05:37
Split and List,xponential size. The common way to enlarge the problem is to split the input into parts, and for each part to enumerate (or list) all possible solutions to subproblems corresponding to the part. Then we combine solutions of subproblems to solutions of the input of the original problem by making use of a fast polynomial time algorithm.作者: 太空 時間: 2025-3-24 10:06
Federated Learning for Wireless Networksof this chapter we discuss such an interpolation between the two extremes of space complexity for dynamic programming algorithms. In the second section we discuss an opposite technique to gain time by using more space, in particular for branching algorithms.作者: 可觸知 時間: 2025-3-24 13:32
Time Versus Space,of this chapter we discuss such an interpolation between the two extremes of space complexity for dynamic programming algorithms. In the second section we discuss an opposite technique to gain time by using more space, in particular for branching algorithms.作者: 危機 時間: 2025-3-24 14:50
Textbook 2010ynomial time, which means that the number of steps required for the algorithm to solve a problem is bounded by some polynomial in the length of the input. All other algorithms are slow (or bad). The running time of slow algorithms is usually exponential. This book is about bad algorithms. There are 作者: packet 時間: 2025-3-24 21:38 作者: 營養(yǎng) 時間: 2025-3-25 03:02 作者: 松緊帶 時間: 2025-3-25 07:18 作者: hyperuricemia 時間: 2025-3-25 08:56 作者: 單色 時間: 2025-3-25 12:15 作者: GRATE 時間: 2025-3-25 17:37
Federated Learning for IoT Devices,xponential size. The common way to enlarge the problem is to split the input into parts, and for each part to enumerate (or list) all possible solutions to subproblems corresponding to the part. Then we combine solutions of subproblems to solutions of the input of the original problem by making use of a fast polynomial time algorithm.作者: Cubicle 時間: 2025-3-25 20:08 作者: arousal 時間: 2025-3-26 03:22
Textbook 2010blem is solvable in ?nite time by enumerating all possi ble solutions, i. e. by brute force search. But is brute force search always unavoid able? De?nitely not. Already in the nineteen sixties and seventies it was known that some NP complete problems can be solved signi?cantly faster than by brute 作者: 是貪求 時間: 2025-3-26 04:27
Fedor V. Fomin,Dieter KratschTextbook has been class-tested by the authors and their collaborators.Text is supported throughout with exercises and notes for further reading.Comprehensive introduction for researchers.Includes supp作者: 道學(xué)氣 時間: 2025-3-26 10:35 作者: 極大的痛苦 時間: 2025-3-26 16:27
https://doi.org/10.1007/978-3-030-96896-0The treewidth of a graph is one of the most fundamental notions in graph theory and graph algorithms. In this chapter, we give several applications of treewidth in exact algorithms.We also provide an exact algorithm computing the treewidth of a graph.作者: 混亂生活 時間: 2025-3-26 19:16 作者: cardiopulmonary 時間: 2025-3-27 00:28
Treewidth,The treewidth of a graph is one of the most fundamental notions in graph theory and graph algorithms. In this chapter, we give several applications of treewidth in exact algorithms.We also provide an exact algorithm computing the treewidth of a graph.作者: Indelible 時間: 2025-3-27 04:05
Conclusions, Open Problems and Further Directions,We conclude with a number of open problems. Some of them are of a fundamental nature and some of them can serve as starting points for newcomers in the field.作者: 物質(zhì) 時間: 2025-3-27 06:25 作者: 蜈蚣 時間: 2025-3-27 12:29 作者: Forsake 時間: 2025-3-27 13:39
https://doi.org/10.1007/978-3-030-63076-8ning polynomial time algorithms as well as for designing exponential time algorithms. The main idea of dynamic programming is to start by solving small or trivial instances and then gradually resolving larger and harder subproblems by composing solutions from smaller subproblems. From this point of 作者: slow-wave-sleep 時間: 2025-3-27 18:44 作者: 調(diào)整校對 時間: 2025-3-28 00:01 作者: Nucleate 時間: 2025-3-28 05:18 作者: 不可比擬 時間: 2025-3-28 07:38
Studies in Computational Intelligencebased on performing local search in balls in the Hamming space around some assignments. The first algorithm randomly chooses an assignment and performs a random walk of short length (in Hamming distance) to search for the solution. The second algorithm is deterministic and uses a similar idea; but i作者: 雀斑 時間: 2025-3-28 13:02 作者: Lacunar-Stroke 時間: 2025-3-28 18:17
Federated Learning for Wireless Networksms. On the other hand, there are exponential time algorithms needing exponential space, among them in particular the dynamic programming algorithms. In real life applications polynomial space is definitely preferable to exponential space. However, often a “moderate” usage of exponential space can be作者: 名義上 時間: 2025-3-28 21:14 作者: onlooker 時間: 2025-3-28 23:35
https://doi.org/10.1007/978-3-642-16533-7Branching; Combinatorics; Dynamic programming; Exact algorithms; Exponential algorithms; Graph; Hard optim作者: 改變立場 時間: 2025-3-29 06:17
978-3-642-26566-2Springer-Verlag Berlin Heidelberg 2010作者: 殺死 時間: 2025-3-29 08:09 作者: ascetic 時間: 2025-3-29 11:53
https://doi.org/10.1007/978-1-4615-1395-7shed fast exponential time algorithms are branching algorithms. Furthermore, for many NP-hard problems the fastest known exact algorithm is a branching algorithm. Many of those algorithms have been developed during the last ten years by applying techniques like Measure & Conquer, quasiconvex analysis and related ones.作者: 我說不重要 時間: 2025-3-29 17:36
Yaochu Jin,Hangyu Zhu,Yang Chenranching algorithms that seem hard or even impossible to establish by the simple analysis of branching algorithms studied in Chap. 2. The main difference is that the measure for the size of an instance of a subproblem and thus also the measure for the progress during the branching algorithm’s execution will be chosen with much more freedom.作者: DRAFT 時間: 2025-3-29 23:19
https://doi.org/10.1007/978-3-031-07838-5algorithm one relies on repeated use of dynamic programming, and in particular on the so-called fast zeta transform. In the latter sections we present various algorithmic applications of fast subset convolution. In this chapter the algorithms (may) operate with large numbers and thus we use the log-cost RAM model to analyze their running times.
