Algorithms in Intersection Theory in the Plane
Bibliographic record
Abstract
This thesis presents an algorithm to find the local structure of intersections of plane curves. More precisely, we address the question of describing the scheme of the quotient ring of a bivariate zero-dimensional ideal $I\\subseteq \\mathbb K[x,y]$, \\textit{i.e.} finding the points (maximal ideals of $\\mathbb K[x,y]/I$) and describing the regular functions on those points. A natural way to address this problem is via Gr\\"obner bases as they reduce the problem of finding the points to a problem of factorisation, and the sheaf of rings of regular functions can be studied with those bases through the division algorithm and localisation. \nLet $I\\subseteq \\mathbb K[x,y]$ be an ideal generated by $\\mathcal F$, a subset of $\\mathbb A[x,y]$ with $\\mathbb A\\hookrightarrow\\mathbb K$ and $\\mathbb K$ a field. We present an algorithm that features a quadratic convergence to find a Gr\\"obner basis of $I$ or its primary component at the origin. \n \nWe introduce an $\\mathfrak m$-adic Newton iteration to lift the lexicographic Gr\\"obner basis of any finite intersection of zero-dimensional primary components of $I$ if $\\mathfrak m\\subseteq \\mathbb A$ is a \\textit{good} maximal ideal. It relies on a structural result about the syzygies in such a basis due to Conca \\textit{\\&} Valla (2008), from which arises an explicit map between ideals in a stratum (or Gr\\"obner cell) and points in the associated moduli space. We also qualify what makes a maximal ideal $\\mathfrak m$ suitable for our filtration. \n \nWhen the field $\\mathbb K$ is \\textit{large enough}, endowed with an Archimedean or ultrametric valuation, and admits a fraction reconstruction algorithm, we use this result to give a complete $\\mathfrak m$-adic algorithm to recover $\\mathcal G$, the Gr\\"obner basis of $I$. We observe that previous results of Lazard that use Hermite normal forms to compute Gr\\"obner bases for ideals with two generators can be generalised to a set of $n$ generators. We use this result to obtain a bound on the height of the coefficients of $\\mathcal G$ and to control the probability of choosing a \\textit{good} maximal ideal $\\mathfrak m\\subseteq\\mathbb A$ to build the $\\mathfrak m$-adic expansion of $\\mathcal G$. \nInspired by Pardue (1994), we also give a constructive proof to \ncharacterise a Zariski open set of $\\mathrm{GL}_2(\\mathbb K)$ (with action on $\\mathbb K[x,y]$) that changes coordinates in such a way as to ensure the initial term ideal of a zero-dimensional $I$ becomes Borel-fixed when $|\\mathbb K|$ is sufficiently large. This sharpens our analysis \nto obtain, when $\\mathbb A=\\mathbb Z$ or $\\mathbb A=k[t]$, a complexity less than cubic in terms of the dimension of $\\mathbb Q[x,y]/\\langle \\mathcal G\\rangle$ and softly linear in the height of the coefficients of $\\mathcal G$. \n \nWe adapt the resulting method and present the analysis to find the $\\langle x,y\\rangle$-primary component of $I$. We also discuss the transition towards other primary components via linear mappings, called \\emph{untangling} and \\emph{tangling}, introduced by van der Hoeven and Lecerf (2017). The two maps form one isomorphism to find points with an isomorphic local structure and, at the origin, bind them. We give a slightly faster tangling algorithm and discuss new applications of these techniques. We show how to extend these ideas to bivariate settings and give a bound on the arithmetic complexity for certain algebras.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".