mlsanityData to 7 Oct 2026

CLuP practically achieves ~ 1.77 positive and ~ 0.33 negative Hopfield model ground state free energy

arXiv 2507.22396 ↗First posted 30 Jul 2025cond-mat.dis-nn cs.IT math.ITcited by 3

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Abstract (arXiv)

We study algorithmic aspects of finding n-dimensional positive and negative Hopfield (±Hop) model ground state free energies. This corresponds to classical maximization of random positive/negative semi-definite quadratic forms over binary left ± frac1√n right n vectors. The key algorithmic question is whether these problems can be computationally efficiently approximated within a factor ≈ 1. Following the introduction and success of Controlled Loosening-up (CLuP-SK) algorithms in finding near ground state energies of closely related Sherrington-Kirkpatrick (SK) models [82], we here propose a CLuP±Hop counterparts for ±Hop models. Fully lifted random duality theory (fl RDT) [78] is utilized to characterize CLuP±Hop typical dynamics. An excellent agreement between practical performance and theoretical predictions is observed. In particular, for n as small as few thousands CLuP±Hop achieve ~ 1.77 and ~ 0.33 as the ground state free energies of the positive and negative Hopfield models. At the same time we obtain on the 6th level of lifting (6-spl RDT) corresponding theoretical thermodynamic (nrightarrow∞) limits ≈ 1.7784 and ≈ 0.3281. This positions determining Hopfield models near ground state energies as typically easy problems. Moreover, the very same 6th lifting level evaluations allow to uncover a fundamental intrinsic difference between two models: +Hop's near optimal configurations are typically close to each other whereas the -Hop's are typically far away.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 17 of its 86 references that are in the map. First posted on arXiv 30 Jul 2025.

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17 of 86 references.

References · 17

  1. 2016Dense Associative Memory for Pattern Recognitioncited by 230
  2. 2022Sampling from the Sherrington-Kirkpatrick Gibbs measure via algorithmic stochastic localizationcited by 48
  3. 2015Subdominant Dense Clusters Allow for Simple Learning and High Computational Performance in Neural Networks with Discrete Synapsescited by 39
  4. 2013Limits of local algorithms over sparse random graphscited by 23
  5. 2015Local entropy as a measure for sampling solutions in Constraint Satisfaction Problemscited by 20
  6. 2018Optimization of the Sherrington-Kirkpatrick Hamiltoniancited by 20
  7. 2023Fully lifted random duality theorycited by 17
  8. 2013Lifting/lowering Hopfield models ground state energiescited by 13
  9. 2023Binary perceptrons capacity via fully lifted random duality theorycited by 13
  10. 2019Clustering of solutions in the symmetric binary perceptroncited by 12
  11. 2023Fl RDT based ultimate lowering of the negative spherical perceptron capacitycited by 11
  12. 2021Tight Lipschitz Hardness for Optimizing Mean Field Spin Glassescited by 9
5 more
  1. 2025Rare dense solutions clusters in asymmetric binary perceptrons -- local entropy via fully lifted RDTcited by 5
  2. 2025A CLuP algorithm to practically achieve ~ 0.76 SK--model ground state free energycited by 5
  3. 2013Bounding ground state energy of Hopfield modelscited by 2
  4. 2018The algorithmic hardness threshold for continuous random energy modelscited by 2
  5. 2024Capacity of the Hebbian-Hopfield network associative memorycited by 1

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Citing papers per year: 2025: 1; 2026: 2.

3 papers.