Tagged Sentential Decision Diagrams: Combining Standard and Zero-suppressed Compression and Trimming Rules

Fang, Liangda; Fang, Biqing; Wan, Hai; Zheng, Z. P.; Chang, Liang; Qi, Yu

doi:10.1109/iccad45719.2019.8942114

Cited by 2 publications

(5 citation statements)

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“…trimming) rules can be applied to it. The canonicity of compressed and trimmed TSDDs was proved in [8].…”

Section: Canonicitymentioning

confidence: 99%

“…The main operations on TSDDs include conjunction (∧), disjunction (∨), and negation (x). The algorithm of the binary operations conjunction (∧) and disjunction (∨) was shown in [8]. Here, we show the negation algorithm on TSDDs in Algorithm 1.…”

Section: Operations On Tsddsmentioning

confidence: 99%

“…The binary operation is represented by Apply(F, G, •), where • represents ∧ or ∨, as shown in [8]. With these three operations, we can construct TSDDs to represent any Boolean function based on existing TSDDs.…”

Section: Algorithm 1: Negate(f)mentioning

confidence: 99%

“…To leverage the strengths of both SDDs and ZSDDs, ref. [8] devised a new decision diagram, the TSDD, which combines the standard and zero-suppressed trimming rules.…”

Section: Introductionmentioning

confidence: 99%

“…Ref. [8] proposed a related definition of TSDDs. However, the researchers used three different semantics to explain SDDs, ZSDDs, and TSDDs.…”

Section: Introductionmentioning

confidence: 99%

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Dictionary Encoding Based on Tagged Sentential Decision Diagrams

Zhong,

Fang,

Guan

2024

Algorithms

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Encoding a dictionary into another representation means that all the words can be stored in the dictionary in a more efficient way. In this way, we can complete common operations in dictionaries, such as (1) searching for a word in the dictionary, (2) adding some words to the dictionary, and (3) removing some words from the dictionary, in a shorter time. Binary decision diagrams (BDDs) are one of the most famous representations of such encoding and are widely popular due to their excellent properties. Recently, some people have proposed encoding dictionaries into BDDs and some variants of BDDs and showed that it is feasible. Hence, we further investigate the topic of encoding dictionaries into decision diagrams. Tagged sentential decision diagrams (TSDDs), as one of these variants based on structured decomposition, exploit both the standard and zero-suppressed trimming rules. In this paper, we first introduce how to use Boolean functions to represent dictionary files and then design an algorithm that encodes dictionaries into TSDDs with the help of tries and a decoding algorithm that restores TSDDs to dictionaries. We utilize the help of tries in the encoding algorithm, which greatly accelerates the encoding process. Considering that TSDDs integrate two trimming rules, we believe that using TSDDs to represent dictionaries would be more effective, and the experiments also show this.

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“…trimming) rules can be applied to it. The canonicity of compressed and trimmed TSDDs was proved in [8].…”

Section: Canonicitymentioning

confidence: 99%

Section: Operations On Tsddsmentioning

confidence: 99%

Section: Algorithm 1: Negate(f)mentioning

confidence: 99%

“…To leverage the strengths of both SDDs and ZSDDs, ref. [8] devised a new decision diagram, the TSDD, which combines the standard and zero-suppressed trimming rules.…”

Section: Introductionmentioning

confidence: 99%

“…Ref. [8] proposed a related definition of TSDDs. However, the researchers used three different semantics to explain SDDs, ZSDDs, and TSDDs.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations