Parallel Depth-Bounded Discrepancy Search

Moisan, Thierry; Quimper, Claude-Guy; Gaudreault, Jonathan

doi:10.1007/978-3-319-07046-9_27

Cited by 7 publications

(10 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Evidently, that the heuristic misses less often as depth increases. This is the base for PDDS algorithm [1]. The similar heuristic for problem (5) performs worse, as one can see in Fig.…”

Section: Algorithm Performancementioning

confidence: 94%

“…Initial problem (1) (2) (3) (4) (5) (6) (7) ... Figure 1: Search tree structure example. Each tree node is essentially a partial sequence {...}.…”

Section: Leafsmentioning

confidence: 99%

“…One may see that the mean net open shutter time is proportional to the mean total yield when ( ) ≡ −1 · ( ) for any , where constant may be chosen as max , ( ). The run times with different parameters (the time constraint constant , amount of input tasks , and parameter [1]) are demonstrated in Fig. 13.…”

Section: Algorithm Performancementioning

confidence: 99%

“…From the papers [10,11] it follows that the problems belong to NP-hard class and can't be solved in polynomial time whenever ̸ = [12]. To solve the problems, classical branch-and-bound technique [13] built on top of parallel depth-bounded discrepancy search (PDDS) algorithm [1] is used.…”

Section: Introductionmentioning

confidence: 99%

“…The short-term online scheduling is treated as the discrete optimisation problems which are stated using several abstraction levels. The optimisation problems are solved using the parallel depth-bounded discrepancy search (PDDS) algorithm [1]. Some aspects of the algorithm performance are discussed.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Astronomical observation tasks short-term scheduling using PDDS algorithm

Kornilov¹

2016

Astronomy and Computing

View full text Add to dashboard Cite

A concept of the ground-based optical astronomical observation efficiency is considered in this paper. We believe that a telescope efficiency can be increased by properly allocating observation tasks with respect to the current environment state and probability to obtain the data with required properties under the current conditions. An online observations scheduling is assumed to be an essential part for raising the efficiency. The short-term online scheduling is treated as the discrete optimisation problems which are stated using several abstraction levels. The optimisation problems are solved using the parallel depth-bounded discrepancy search (PDDS) algorithm [1]. Some aspects of the algorithm performance are discussed. The presented algorithm is a core of open-source chelyabinsk C++ library which is planned to be used at 2.5 m telescope of Sternberg Astronomical Institude of Lomonosov Moscow State University.

show abstract

“…Evidently, that the heuristic misses less often as depth increases. This is the base for PDDS algorithm [1]. The similar heuristic for problem (5) performs worse, as one can see in Fig.…”

Section: Algorithm Performancementioning

confidence: 94%

“…Initial problem (1) (2) (3) (4) (5) (6) (7) ... Figure 1: Search tree structure example. Each tree node is essentially a partial sequence {...}.…”

Section: Leafsmentioning

confidence: 99%

Section: Algorithm Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Astronomical observation tasks short-term scheduling using PDDS algorithm

Kornilov¹

2016

Astronomy and Computing

View full text Add to dashboard Cite

show abstract

Sequential and Parallel Solution-Biased Search for Subgraph Algorithms

Archibald

Dunlop

Hoffmann

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

The current state of the art in subgraph isomorphism solving involves using degree as a value-ordering heuristic to direct backtracking search. Such a search makes a heavy commitment to the first branching choice, which is often incorrect. To mitigate this, we introduce and evaluate a new approach, which we call "solution-biased search". By combining a slightly-random value-ordering heuristic, rapid restarts, and nogood recording, we design an algorithm which instead uses degree to direct the proportion of search effort spent in different subproblems. This increases performance by two orders of magnitude on satisfiable instances, whilst not affecting performance on unsatisfiable instances. This algorithm can also be parallelised in a very simple but effective way: across both satisfiable and unsatisfiable instances, we get a further speedup of over thirty from thirty-six cores, and over one hundred from ten distributed-memory hosts. Finally, we show that solution-biased search is also suitable for optimisation problems, by using it to improve two maximum common induced subgraph algorithms.

show abstract

Insights into Parallelism with Intensive Knowledge Sharing

Sabharwal

Samulowitz

2014

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Parallel Depth-Bounded Discrepancy Search

Cited by 7 publications

References 14 publications

Astronomical observation tasks short-term scheduling using PDDS algorithm

Astronomical observation tasks short-term scheduling using PDDS algorithm

Sequential and Parallel Solution-Biased Search for Subgraph Algorithms

Insights into Parallelism with Intensive Knowledge Sharing

Contact Info

Product

Resources

About