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Algorithmically solving the Tadpole Problem

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TLDR
In this article, the Tadpole Conjecture was used to show that the minimal charge grows linearly with the dimension of the lattice and this charge is larger than allowed by tadpole cancelation.
Abstract
The extensive computer-aided search applied in [arXiv:2010.10519] to find the minimal charge sourced by the fluxes that stabilize all the (flux-stabilizable) moduli of a smooth K3xK3 compactification uses differential evolutionary algorithms supplemented by local searches. We present these algorithms in detail and show that they can also solve our minimization problem for other lattices. Our results support the Tadpole Conjecture: The minimal charge grows linearly with the dimension of the lattice and, for K3xK3, this charge is larger than allowed by tadpole cancelation. Even if we are faced with an NP-hard lattice-reduction problem at every step in the minimization process, we find that differential evolution is a good technique for identifying the regions of the landscape where the fluxes with the lowest tadpole can be found. We then design a "Spider Algorithm," which is very efficient at exploring these regions and producing large numbers of minimal-tadpole configurations.

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F-theory flux vacua at large complex structure

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References
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Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
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TL;DR: The Julia programming language as mentioned in this paper combines expertise from the diverse fields of computer science and computational science to create a new approach to numerical computing, which is designed to be easy and fast and questions notions generally held to be “laws of nature" by practitioners of numerical computing.
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