PatternBoost: Using Transformers and Local Search to Find Interesting Constructions in Extremal Combinatorics

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PatternBoost, a novel algorithm combining transformer neural networks with local search methods, effectively discovers intricate patterns in mathematical constructions, particularly in extremal combinatorics, often surpassing the performance of traditional approaches and leading to new discoveries.

Samenvatting

Bibliographic Information:

Charton, F., Ellenberg, J., Wagner, A. Z., & Williamson, G. (2024). PatternBoost: Constructions in Mathematics with a Little Help from AI. arXiv preprint arXiv:2411.00566.

Research Objective:

This paper introduces PatternBoost, a new computational method for generating interesting constructions in pure mathematics, particularly in extremal combinatorics. The authors aim to demonstrate the effectiveness of this method in solving problems where optimal constructions are difficult to describe or discover using traditional approaches.

Methodology:

PatternBoost employs an iterative process alternating between "local" and "global" phases. The local phase utilizes a simple greedy algorithm for local search, while the global phase leverages a transformer neural network trained on the best constructions found so far. This iterative process allows the algorithm to learn from previous successful constructions and generate new ones with improved properties. The authors apply PatternBoost to several problems in extremal combinatorics, including finding triangle-free graphs with many edges, constructing binary matrices with large permanents avoiding specific patterns, and finding spanning subgraphs of hypercubes with minimal edges while maintaining diameter.

Key Findings:

The authors demonstrate that PatternBoost outperforms pure local search methods in various extremal combinatorial problems. In some cases, it even approaches the performance of handcrafted, problem-specific algorithms. For instance, PatternBoost discovers a counterexample to a 30-year-old conjecture about the minimum number of edges in a spanning subgraph of a hypercube with the same diameter.

Main Conclusions:

PatternBoost presents a promising approach for exploring complex mathematical constructions, particularly in extremal combinatorics. Its ability to learn from data and generate novel solutions makes it a valuable tool for mathematicians seeking to discover new patterns and solve challenging problems.

Significance:

This research highlights the potential of machine learning, specifically transformer networks, in advancing mathematical research. It provides a practical and accessible method for mathematicians to leverage the power of AI in their work.

Limitations and Future Research:

While PatternBoost shows significant promise, the authors acknowledge that its performance varies across different problems. Further research is needed to understand which mathematical problems are most amenable to this approach and to explore the impact of different tokenization schemes and transformer architectures on the algorithm's effectiveness. Additionally, investigating the potential of PatternBoost beyond extremal combinatorics and in other scientific domains could lead to further breakthroughs.

Statistieken

The best score achieved by randomly adding edges to a 20-vertex graph without creating triangles was 99, achieved in two out of 40,000 tries.
After five iterations of PatternBoost, the model primarily generated complete bipartite graphs, many with equal part sizes.
In the no-squares problem with 33 vertices, the maximum achievable score is 96.
The best construction found by PatternBoost for the 312-avoiding permanent problem with a 25x25 matrix had a permanent of 5,101,230, while the handcrafted method achieved 5,200,384.
For the no-isosceles triangle problem, a lower bound is f(n) ≥ cn, and an upper bound is f(n) ≤ e^(-c log^(1/9) n) * n^2.
The best known lower bound for the maximum number of edges deletable from a d-dimensional hypercube without increasing its diameter is 2d + c * 2^(d/2).

Citaten

"Mathematical objects are not bicycles. But human beings can abstract features of bicycles and develop new objects that we recognize as bicycles, despite their not being identical with any existing examples, and mathematicians do the same with mathematical objects."
"One of the main strengths of PatternBoost is its broad applicability. By adding a global step that uses transformers to suggest better starting points for the local search, PatternBoost can improve results across many optimization problems."
"Our hope for the methods described here is that techniques from machine learning (and in particular transformers) have at least some capabilities of this kind – that presented with a list of mathematical entities, they can produce further examples which are, at least in some respects, “of the same kind” as those observed."

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PatternBoost: Constructions in Mathematics with a Little Help from AI

by Fran... om arxiv.org 11-04-2024

https://arxiv.org/pdf/2411.00566.pdf

Diepere vragen

Can PatternBoost be adapted to work with other areas of mathematics beyond combinatorics, such as topology or number theory?

While the examples in the paper primarily focus on extremal combinatorics, the core principles of PatternBoost hold potential for applications in other areas of mathematics, including topology and number theory. However, some key challenges and considerations arise:
Representations: A key strength of PatternBoost lies in its ability to represent combinatorial structures (like graphs and matrices) as sequences suitable for transformer models. Adapting to other areas requires finding suitable representations:

Topology: Topological spaces could potentially be represented using simplicial complexes, persistent homology diagrams, or knot invariants, which can be encoded as sequences.
Number Theory: Number-theoretic objects like prime numbers, elliptic curves, or modular forms might be represented using their properties (e.g., coefficients of generating functions, values of L-functions) or relationships within specific algebraic structures.
Score Functions: Defining appropriate score functions that capture desirable properties within these areas is crucial.

Topology: Score functions could measure properties like the genus of a surface, the Betti numbers of a space, or the knot invariant values.
Number Theory: Scores could be based on criteria like the number of prime factors, the rank of an elliptic curve, or special values of L-functions.
Local Search: The effectiveness of PatternBoost relies on the availability of efficient local search methods.

Topology: Local moves could involve operations like attaching handles, performing surgery, or modifying knot diagrams.
Number Theory: Local search might involve manipulating equations, applying transformations, or exploring nearby points in a lattice.
Challenges:

Complexity: Topological and number-theoretic objects can be significantly more complex than combinatorial structures, potentially requiring more sophisticated representations and computationally expensive local search methods.
Interpretability: Interpreting the output of the transformer and understanding the underlying mathematical reasons for the generated constructions might be more challenging in these areas.
In conclusion, while adapting PatternBoost to areas like topology and number theory presents challenges, the potential for discovery is significant. Finding suitable representations, score functions, and local search methods are crucial steps. The success of such adaptations will likely depend on the specific problem and the creativity in designing these components.

Could the reliance on local search methods within PatternBoost be a limiting factor in its ability to find truly novel solutions that deviate significantly from the training data?

Yes, the reliance on local search methods within PatternBoost could potentially limit its ability to discover truly novel solutions that deviate significantly from the training data. Here's why:

Local Optima: Local search methods, by their nature, are prone to getting stuck in local optima. If the training data primarily consists of solutions clustered around a particular local optimum, the transformer might primarily generate variations within that region, hindering the exploration of radically different solutions.
Bias Towards Training Data: The transformer learns patterns and structures present in the training data. While it can extrapolate to some extent, the local search phase might further reinforce the existing biases, guiding the search towards solutions similar to those already encountered.
Mitigating the Limitations:

Diverse Training Data: Starting with a diverse set of initial constructions, potentially generated through different methods or with varying constraints, can help broaden the search space and reduce bias towards a specific region.
Global Search Elements: Incorporating elements of global search within PatternBoost could prove beneficial. This might involve periodically restarting the local search from randomly generated solutions or using techniques like simulated annealing or genetic algorithms to escape local optima.
Curriculum Learning: Gradually increasing the complexity of the problem or relaxing constraints during training might allow the model to explore a wider range of solutions progressively.
Balancing Exploration and Exploitation:
The challenge lies in striking a balance between exploration (searching for novel solutions) and exploitation (refining existing patterns). While local search excels at exploitation, incorporating mechanisms for greater exploration is crucial for discovering truly groundbreaking solutions.

What are the ethical implications of using AI to assist in mathematical research, particularly regarding issues of authorship, originality, and the potential for bias in the algorithms?

The use of AI in mathematical research, while promising, raises important ethical considerations:
Authorship and Originality:

Credit Allocation: Determining authorship when AI contributes significantly to a proof or discovery is complex. Should the AI be listed as a co-author? How do we acknowledge the role of the researchers who designed, trained, and interpreted the AI's output?
Novelty and Plagiarism: AI systems learn from existing data, raising concerns about the originality of their outputs. How do we ensure that AI-generated results are not simply replicating or subtly plagiarizing existing work?
Bias and Fairness:

Data Bias: AI models are trained on data, which can reflect existing biases in the field. If the training data is skewed or incomplete, the AI might perpetuate or even amplify these biases, potentially leading to unfair or inaccurate results.
Transparency and Explainability: Understanding the reasoning behind AI-generated proofs or constructions is crucial for verifying their validity and ensuring fairness. However, many AI models, especially deep learning models, are often opaque, making it difficult to discern the underlying logic.
Impact on the Field:

Accessibility and Equity: Access to powerful AI tools could exacerbate existing inequalities in research, favoring institutions or individuals with greater resources.
Changing Nature of Research: The use of AI might shift the focus of mathematical research, potentially prioritizing problems amenable to AI solutions over those requiring deeper human intuition or creativity.
Addressing Ethical Concerns:

Developing Guidelines: The mathematical community needs to establish clear guidelines for authorship, originality, and the responsible use of AI in research.
Promoting Transparency: Researchers should strive for transparency in their methods, making code and data publicly available whenever possible to enable scrutiny and reproducibility.
Addressing Bias: Efforts should be made to identify and mitigate bias in training data and to develop methods for evaluating fairness in AI systems.
Fostering Dialogue: Open discussions about the ethical implications of AI in mathematics are crucial, involving mathematicians, computer scientists, ethicists, and the broader public.
By proactively addressing these ethical considerations, we can harness the power of AI to advance mathematical knowledge while upholding the integrity and values of the field.