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Computational complexity of the landscape I

High Energy Physics - Theory · Submitted 7 Feb 2006

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We study the computational complexity of the physical problem of finding vacua of string theory which agree with data, such as the cosmological constant, and show that such problems are typically NP hard.

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arXiv IDhep-th/0602072 (version 2)
Pith IDNZDFL3MX, Pith's permanent record of this paper and its review
CitationFrederik Denef (KU Leuven), IHES), Michael R. Douglas (Rutgers. "Computational complexity of the landscape I." arXiv:hep-th/0602072 (2006). https://pith.science/paper/NZDFL3MX
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Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,603 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.70 · human participants 0.01 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.22 · compares with prior results 0.08 · mentions code or data availability 0.02.

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arXiv listing abstract v2. Results and the main claim are underlined; .

We study the computational complexity of the physical problem of finding vacua of string theory which agree with data, such as the cosmological constant, and show that such problems are typically NP hard. In particular, we prove that in the Bousso-Polchinski model, the problem is NP complete. We discuss the issues this raises and the possibility that, even if we were to find compelling evidence that some vacuum of string theory describes our universe, we might never be able to find that vacuum explicitly. In a companion paper, we apply this point of view to the question of how early cosmology might select a vacuum.

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#sentencerolethe authors' own workstates the main conclusionstrength
s0 We study the computational complexity of the physical problem of finding vacua of string theory which agree with data, such as the cosmological constant, and show that such problems are typically NP hard. contribution 0.59 0.93 0.92 asserted 0.99
s1 In particular, we prove that in the Bousso-Polchinski model, the problem is NP complete. result 0.98 0.94 0.25 asserted 1.00
s2 We discuss the issues this raises and the possibility that, even if we were to find compelling evidence that some vacuum of string theory describes our universe, we might never be able to find that vacuum explicitly. interpretation 1.00 0.21 0.10 speculative 0.57
s3 In a companion paper, we apply this point of view to the question of how early cosmology might select a vacuum. future work 0.98 0.42 0.02 asserted 0.87

What kind of paper the classifier reports this is: theoretical result 1.00 · evidence from proof or derivation 1.00.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We study the computational complexity of the physical problem of finding vacua of string theory which agree with data, such as the cosmological constant, and show that such problems are typically NP hard. In particular, we prove that in the Bousso-Polchinski model, the problem is NP complete. We discuss the issues this raises and the possibility that, even if we were to find compelling evidence that some vacuum of string theory describes our universe, we might never be able to find that vacuum explicitly. In a companion paper, we apply this point of view to the question of how early cosmology might select a vacuum.

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References
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43 arXiv papers cite this in 57 sentences

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers citing it · 45 sentences without a confident label

Compares with it 1 paper
Cites it as support 2 papers
  • Moreover, for deterministic algorithms, difficulties due to the large size of the landscape are exacerbated by computational complexity, as can be seen in several toy models [ref] .

    Searching the Landscape of Flux Vacua with Genetic Algorithms · Introduction · 0.96

  • For instance, the search for small cosmological constants in simplified idealized models is already NP-hard [ref] , although the success in finding explicit solutions with small cosmological constant in F-theory [ref] suggests that the structure of the landscape may enable efficient solutions to this problem.

    Snowmass White Paper: String Theory and Particle Physics · Machine Learning and Computational Complexity · 0.96

Cites it as background 8 papers

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