From e746824c07823d1905b51d0684d83a3fc38897b0 Mon Sep 17 00:00:00 2001
From: itsalissonsilva <silva.unb.phys@gmail.com>
Date: Thu, 26 Feb 2026 22:00:30 -0300
Subject: [PATCH] feat: least squares regression through the origin

---
 .../Algorithms/Lean/LeastSquares/Origin.lean  | 198 ++++++++++++++++++
 CslibTests/LeastSquaresOrigin.lean            |  13 ++
 2 files changed, 211 insertions(+)
 create mode 100644 Cslib/Algorithms/Lean/LeastSquares/Origin.lean
 create mode 100644 CslibTests/LeastSquaresOrigin.lean

diff --git a/Cslib/Algorithms/Lean/LeastSquares/Origin.lean b/Cslib/Algorithms/Lean/LeastSquares/Origin.lean
new file mode 100644
index 000000000..939681583
--- /dev/null
+++ b/Cslib/Algorithms/Lean/LeastSquares/Origin.lean
@@ -0,0 +1,198 @@
+/- Copyright (c) 2026 Alisson Matheus Silva. All rights reserved.
+Released under Apache 2.0 license as described in the file LICENSE.
+Authors: Alisson Matheus Silva -/
+module
+public import Cslib.Init
+public import Mathlib.Data.Real.Basic
+public import Mathlib.Algebra.BigOperators.Ring.Finset
+public import Mathlib.Tactic
+
+@[expose] public section
+set_option autoImplicit false
+
+namespace Cslib.Algorithms.Lean.LeastSquares.Origin
+
+variable {n : ℕ}
+
+/-- `Sxx = ∑ xᵢ²`. -/
+noncomputable def Sxx (x : Fin n → ℝ) : ℝ :=
+  (Finset.univ : Finset (Fin n)).sum (fun i => (x i) ^ 2)
+
+/-- `Sxy = ∑ xᵢ yᵢ`. -/
+noncomputable def Sxy (x y : Fin n → ℝ) : ℝ :=
+  (Finset.univ : Finset (Fin n)).sum (fun i => x i * y i)
+
+/-- Least-squares slope through the origin: `a* = Sxy / Sxx`. -/
+noncomputable def aStar (x y : Fin n → ℝ) : ℝ :=
+  (Sxy x y) / (Sxx x)
+
+/-- Squared loss: `loss(a) = ∑ (a xᵢ - yᵢ)²`. -/
+noncomputable def loss (x y : Fin n → ℝ) (a : ℝ) : ℝ :=
+  (Finset.univ : Finset (Fin n)).sum (fun i => (a * x i - y i) ^ 2)
+
+lemma Sxx_nonneg (x : Fin n → ℝ) : 0 ≤ Sxx x := by
+  classical
+  unfold Sxx
+  refine Finset.induction_on (s := (Finset.univ : Finset (Fin n))) ?base ?step
+  · simp
+  · intro a s ha hs
+    have hxa : 0 ≤ (x a) ^ 2 := sq_nonneg (x a)
+    simpa [Finset.sum_insert, ha] using add_nonneg hxa hs
+
+/-- Normal equation: the cross term vanishes at `aStar`. -/
+lemma crossTerm_eq_zero (x y : Fin n → ℝ) (h : Sxx x ≠ 0) :
+    (Finset.univ : Finset (Fin n)).sum
+        (fun i => x i * (aStar x y * x i - y i)) = 0 := by
+  classical
+  let U : Finset (Fin n) := Finset.univ
+  have hrewrite :
+      U.sum (fun i => x i * (aStar x y * x i - y i))
+        =
+      aStar x y * (U.sum (fun i => (x i) ^ 2))
+        - (U.sum (fun i => x i * y i)) := by
+    calc
+      U.sum (fun i => x i * (aStar x y * x i - y i))
+          =
+        U.sum (fun i => aStar x y * (x i) ^ 2 - x i * y i) := by
+          refine Finset.sum_congr rfl ?_
+          intro i hi
+          simp [pow_two]
+          ring_nf
+      _ =
+        U.sum (fun i => aStar x y * (x i) ^ 2)
+          - U.sum (fun i => x i * y i) := by
+          -- direct lemma; no simp/simpa needed
+          exact Finset.sum_sub_distrib
+            (s := U)
+            (f := fun i => aStar x y * (x i) ^ 2)
+            (g := fun i => x i * y i)
+      _ =
+        aStar x y * (U.sum (fun i => (x i) ^ 2))
+          - U.sum (fun i => x i * y i) := by
+          have hm :
+              U.sum (fun i => aStar x y * (x i) ^ 2)
+                =
+              aStar x y * (U.sum (fun i => (x i) ^ 2)) :=
+            (Finset.mul_sum
+              (s := U)
+              (f := fun i => (x i) ^ 2)
+              (a := aStar x y)).symm
+          simp [hm]
+  calc
+    (Finset.univ : Finset (Fin n)).sum
+          (fun i => x i * (aStar x y * x i - y i))
+        =
+      aStar x y * (U.sum (fun i => (x i) ^ 2))
+        - (U.sum (fun i => x i * y i)) := by
+          simpa [U] using hrewrite
+    _ = 0 := by
+      -- Clear denominators: (Sxy/Sxx)*Sxx - Sxy = 0
+      simp [aStar, Sxx, Sxy, U] at h ⊢
+      field_simp [h]
+      ring_nf
+
+/-- Loss decomposition around `aStar` (regression through the origin). -/
+lemma loss_decomp (x y : Fin n → ℝ) (h : Sxx x ≠ 0) (a : ℝ) :
+    loss x y a =
+      loss x y (aStar x y) + (Sxx x) * (a - aStar x y) ^ 2 := by
+  classical
+  let U : Finset (Fin n) := Finset.univ
+  let a0 : ℝ := aStar x y
+  let d : ℝ := a - a0
+  have hterm : ∀ i : Fin n, a * x i - y i = d * x i + (a0 * x i - y i) := by
+    intro i
+    simp [d, sub_eq_add_neg, a0]
+    ring_nf
+  have hcross : U.sum (fun i => x i * (a0 * x i - y i)) = 0 := by
+    simpa [a0, U] using crossTerm_eq_zero (x := x) (y := y) h
+  have hexpand :
+      U.sum (fun i => (a * x i - y i) ^ 2)
+        =
+      U.sum (fun i =>
+        (d ^ 2) * (x i) ^ 2
+        + (a0 * x i - y i) ^ 2
+        + (2 * d) * (x i * (a0 * x i - y i))) := by
+    refine Finset.sum_congr rfl ?_
+    intro i hi
+    have ht := hterm i
+    simp [ht, pow_two]
+    ring_nf
+  let A : Fin n → ℝ := fun i => (d ^ 2) * (x i) ^ 2
+  let B : Fin n → ℝ := fun i => (a0 * x i - y i) ^ 2
+  let C : Fin n → ℝ := fun i => (2 * d) * (x i * (a0 * x i - y i))
+  have hsplit :
+      U.sum (fun i => A i + B i + C i)
+        =
+      (U.sum A + U.sum B) + U.sum C := by
+    have h1 :
+        U.sum (fun i => (A i + B i) + C i)
+          =
+        U.sum (fun i => A i + B i) + U.sum C := by
+      simpa using
+        (Finset.sum_add_distrib
+          (s := U)
+          (f := fun i => A i + B i)
+          (g := C))
+    have h2 :
+        U.sum (fun i => A i + B i) = U.sum A + U.sum B := by
+      simpa using (Finset.sum_add_distrib (s := U) (f := A) (g := B))
+    calc
+      U.sum (fun i => A i + B i + C i)
+          = U.sum (fun i => (A i + B i) + C i) := by rfl
+      _ = U.sum (fun i => A i + B i) + U.sum C := h1
+      _ = (U.sum A + U.sum B) + U.sum C := by simp [h2]
+  have hA : U.sum A = (d ^ 2) * (U.sum (fun i => (x i) ^ 2)) := by
+    -- unfold A, then it matches `mul_sum` exactly
+    dsimp [A]
+    exact (Finset.mul_sum (s := U) (f := fun i => (x i) ^ 2) (a := d ^ 2)).symm
+  have hC :
+      U.sum C
+        =
+      (2 * d) * (U.sum (fun i => x i * (a0 * x i - y i))) := by
+    dsimp [C]
+    exact (Finset.mul_sum
+      (s := U)
+      (f := fun i => x i * (a0 * x i - y i))
+      (a := 2 * d)).symm
+  unfold loss
+  calc
+    (Finset.univ : Finset (Fin n)).sum (fun i => (a * x i - y i) ^ 2)
+        = U.sum (fun i => (a * x i - y i) ^ 2) := by rfl
+    _ = U.sum (fun i =>
+          (d ^ 2) * (x i) ^ 2
+          + (a0 * x i - y i) ^ 2
+          + (2 * d) * (x i * (a0 * x i - y i))) := hexpand
+    _ = U.sum (fun i => A i + B i + C i) := by rfl
+    _ = (U.sum A + U.sum B) + U.sum C := hsplit
+    _ = ((d ^ 2) * (U.sum (fun i => (x i) ^ 2)) + U.sum B)
+          + (2 * d) * (U.sum (fun i => x i * (a0 * x i - y i))) := by
+          simp [hA, hC]
+    _ = ((d ^ 2) * (U.sum (fun i => (x i) ^ 2)) + U.sum B) + (2 * d) * 0 := by
+          simp [hcross]
+    _ = U.sum B + (U.sum (fun i => (x i) ^ 2)) * (d ^ 2) := by
+          -- just algebraic rearrangement
+          ring_nf
+    _ = (Finset.univ : Finset (Fin n)).sum (fun i => (a0 * x i - y i) ^ 2)
+          + (Sxx x) * (a - a0) ^ 2 := by
+          simp [U, Sxx, d, B]
+    _ = loss x y a0 + (Sxx x) * (a - a0) ^ 2 := by
+          simp [loss]
+    _ = loss x y (aStar x y) + (Sxx x) * (a - aStar x y) ^ 2 := by
+          simp [a0]
+
+/-- `aStar` minimizes the squared loss (regression through the origin). -/
+theorem aStar_minimizes (x y : Fin n → ℝ) (h : Sxx x ≠ 0) :
+    ∀ a : ℝ, loss x y (aStar x y) ≤ loss x y a := by
+  classical
+  intro a
+  have hdecomp := loss_decomp (x := x) (y := y) h a
+  have hnonneg : 0 ≤ (Sxx x) * (a - aStar x y) ^ 2 :=
+    mul_nonneg (Sxx_nonneg (x := x)) (sq_nonneg (a - aStar x y))
+  calc
+    loss x y (aStar x y)
+        ≤ loss x y (aStar x y) + (Sxx x) * (a - aStar x y) ^ 2 := by
+          exact le_add_of_nonneg_right hnonneg
+    _ = loss x y a := by
+          exact hdecomp.symm
+
+end Cslib.Algorithms.Lean.LeastSquares.Origin
diff --git a/CslibTests/LeastSquaresOrigin.lean b/CslibTests/LeastSquaresOrigin.lean
new file mode 100644
index 000000000..ee1e0b241
--- /dev/null
+++ b/CslibTests/LeastSquaresOrigin.lean
@@ -0,0 +1,13 @@
+import Cslib.Algorithms.Lean.LeastSquares.Origin
+
+namespace CslibTests
+
+open Cslib.Algorithms.Lean.LeastSquares.Origin
+
+variable {n : ℕ} (x y : Fin n → ℝ)
+
+example (h : Sxx (n := n) x ≠ 0) (a : ℝ) :
+    loss (n := n) x y (aStar (n := n) x y) ≤ loss (n := n) x y a := by
+  simpa using aStar_minimizes (n := n) (x := x) (y := y) h a
+
+end CslibTests