CLuP practically achieves ~ 1.77 positive and ~ 0.33 negative Hopfield model ground state free energy
Not on the map. Why?
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.
Builds on
17 of 86 references.
References · 17
- 2016Dense Associative Memory for Pattern Recognitioncited by 230
- 2022Sampling from the Sherrington-Kirkpatrick Gibbs measure via algorithmic stochastic localizationcited by 48
- 2015Subdominant Dense Clusters Allow for Simple Learning and High Computational Performance in Neural Networks with Discrete Synapsescited by 39
- 2013Limits of local algorithms over sparse random graphscited by 23
- 2015Local entropy as a measure for sampling solutions in Constraint Satisfaction Problemscited by 20
- 2018Optimization of the Sherrington-Kirkpatrick Hamiltoniancited by 20
- 2023Fully lifted random duality theorycited by 17
- 2013Lifting/lowering Hopfield models ground state energiescited by 13
- 2023Binary perceptrons capacity via fully lifted random duality theorycited by 13
- 2019Clustering of solutions in the symmetric binary perceptroncited by 12
- 2023Fl RDT based ultimate lowering of the negative spherical perceptron capacitycited by 11
- 2021Tight Lipschitz Hardness for Optimizing Mean Field Spin Glassescited by 9
5 more
- 2025Rare dense solutions clusters in asymmetric binary perceptrons -- local entropy via fully lifted RDTcited by 5
- 2025A CLuP algorithm to practically achieve ~ 0.76 SK--model ground state free energycited by 5
- 2013Bounding ground state energy of Hopfield modelscited by 2
- 2018The algorithmic hardness threshold for continuous random energy modelscited by 2
- 2024Capacity of the Hebbian-Hopfield network associative memorycited by 1
Built on by
Citing papers per year: 2025: 1; 2026: 2.
3 papers.