Regularised p-Adic Linear Regression

An AI-Driven Mathematical Research Project

Project Overview

This research project investigates the effects of regularisation on p-adic linear regression. Building on the foundational work in arXiv:2510.00043v1, which established that an n-dimensional hyperplane minimizing p-adic sum of distances must pass through at least n+1 points, we explore what happens when regularisation is introduced.

Key Research Question

When we add regularisation to p-adic linear regression, how does the number of data points that the optimal hyperplane passes through change as a function of the regularisation strength?

Mathematical Background

p-Adic Valuation and Distance

For a prime p and rational number x = pk · (a/b) where gcd(p, ab) = 1:

Unregularised p-Adic Regression

The standard p-adic regression loss function is:

L(β) = Σi |yi − Xi·β|p = Σi p−vp(yi − Xi·β)

Regularised p-Adic Regression

We explore adding a regularisation term:

Lreg(β) = Σi |yi − Xi·β|p + λ · R(β)

where R(β) could be:

Current Status

Last Updated: 2026-01-04 AEDT

Current Focus: Goal 5 (Regularization threshold behavior) — formalizing the k(λ) function and proving SC8 characterization. Goals 1-4 (n+1 theorem) remain COMPLETE with 0 axioms in Lean.

Recent Progress:

Key Theorems Proven:

Next Steps:

  1. Formalize the margin polynomial: margin = Σ c_v·r^{-v} with c_{v_min} > 0.
  2. Prove c_{v_min} > 0: The min-loss FP selection forces the dominant coefficient positive.
  3. Key insight: c_{v_min} = (gains at v_min) + (orig R at v_min) - (new R at v_min) > 0.
  4. The minimization criterion avoids configurations where new R dominates at low valuations.

Formal Proofs Status (view all proofs)

TheoremStatusWhen
n+1 Theorem (very strong GP, k=n)AXIOM-FREEDec 10 18:02
n+1 Hyperplane Theorem (strong GP)PROVENDec 9 10:10
Optimal = Exactly n+1 (strong GP)PROVENDec 9 10:10
Product Formula (very strong GP)PROVENDec 9 23:05
Tau-Source Theorem (k=n)PROVENDec 10 14:23
Source Interpolant Beats hPROVENDec 10 12:30
R Decreases at TransitionsPROVENDec 6 03:06
Pareto Frontier MonotonicityPROVENDec 6 05:10
k_min PredictorPROVENDec 6 07:13
Threshold FormulaPROVENDec 6 09:07
Interpolating Hyperplane ExistsPROVENDec 6 20:09