Problem packetWorkR674
[#R674] Bareiss regression and exact order-ten bound verifier
1Summary
A standard-library program enumerates all binary matrices through order four and checks every large-integer bound calculation.
The verifier enumerates 2^(n^2) labeled matrices for n=1,2,3,4 and evaluates each determinant with fraction-free Bareiss elimination. It reproduces the singular counts 1, 10, 338, and 42,976. At order four it checks the full determinant histogram \[ -3:60,\ -2:1200,\ -1:10020,\ 0:42976,\ 1:10020,\ 2:1200,\ 3:60. \] The canonical count line has SHA-256 digest cb8f2c764fb76fd45e8557a6cb27b6c670e3526d60721dfdc093293f64825ae0. The histogram line has digest ab7118ff1c833f08b01258733186ebe654157be07d1705ae3c387000c14888a3.
The same program evaluates the falling-factorial Bonferroni expression, the order of GL(10,2), and both reduced probability fractions. The order-ten part certifies arithmetic and the combinatorial formulas stated in the claim. It does not enumerate 2^100 matrices.
Reproduced evidence. Recorded scope: the exact structural bound arithmetic at order ten and exhaustive determinant enumeration for every binary square matrix of orders one through four.
2Reproduce
Part of the replay path is recorded. Check the missing fields before comparing a new run.
- Entry point
- Join source_lines with LF characters and execute the resulting Python program
- Runtime
- Python 3.10 or later, standard library only
Verification source: doi.org ↗, Inline Python 3 deterministic verifier prepared on 2026-07-25
Missing for a complete replay: command, expected output.
3Source code
View source code
from collections import Counter
from fractions import Fraction
from hashlib import sha256
from math import comb, prod
def determinant(rows):
a=[row[:] for row in rows]
n=len(a)
if n==1:
return a[0][0]
sign=1
previous=1
for k in range(n-1):
pivot_row=next((r for r in range(k,n) if a[r][k]),None)
if pivot_row is None:
return 0
if pivot_row!=k:
a[k],a[pivot_row]=a[pivot_row],a[k]
sign=-sign
pivot=a[k][k]
for i in range(k+1,n):
for j in range(k+1,n):
a[i][j]=(a[i][j]*pivot-a[i][k]*a[k][j])//previous
for i in range(k+1,n):
a[i][k]=0
previous=pivot
return sign*a[-1][-1]
def histogram(n):
out=Counter()
for bits in range(1<<(n*n)):
rows=[[(bits>>(r*n+c))&1 for c in range(n)] for r in range(n)]
out[determinant(rows)]+=1
return out
expected_counts={1:1,2:10,3:338,4:42976}
histograms={n:histogram(n) for n in range(1,5)}
assert {n:histograms[n][0] for n in histograms}==expected_counts
expected_hist4=Counter({-3:60,-2:1200,-1:10020,0:42976,1:10020,2:1200,3:60})
assert histograms[4]==expected_hist4
counts_text=','.join(f'{n}:{expected_counts[n]}' for n in sorted(expected_counts))
hist4_text=','.join(f'{d}:{expected_hist4[d]}' for d in sorted(expected_hist4))
assert sha256((counts_text+'\n').encode()).hexdigest()=='cb8f2c764fb76fd45e8557a6cb27b6c670e3526d60721dfdc093293f64825ae0'
assert sha256((hist4_text+'\n').encode()).hexdigest()=='ab7118ff1c833f08b01258733186ebe654157be07d1705ae3c387000c14888a3'
def falling(a,k):
return prod(a-i for i in range(k))
n=10
total=1<<(n*n)
row_bad=total-falling((1<<n)-1,n)
column_extra=55*falling((1<<(n-1))-1,n)-comb(55,2)*falling((1<<(n-2))-1,n)
lower=row_bad+column_extra
gl2=prod((1<<n)-(1<<i) for i in range(n))
upper=total-gl2
assert total==1267650600228229401496703205376
assert row_bad==66512166928711781758210744576
assert column_extra==59662654901633486909029824000
assert lower==126174821830345268667240568576
assert gl2==366440137299948128422802227200
assert upper==901210462928281273073900978176
lower_fraction=Fraction(lower,total)
upper_fraction=Fraction(upper,total)
assert lower_fraction==Fraction(492870397774786205731408471,4951760157141521099596496896)
assert upper_fraction==Fraction(25613941912987493,36028797018963968)
report=f'n=10 total={total} lower={lower} upper={upper} lower_fraction={lower_fraction} upper_fraction={upper_fraction} regression={counts_text} histogram4={hist4_text}'
print(report)
print(sha256((report+'\n').encode()).hexdigest())4What it produced
- Expected stdout
- n=10 total=1267650600228229401496703205376 lower=126174821830345268667240568576 upper=901210462928281273073900978176 lower_fraction=492870397774786205731408471/4951760157141521099596496896 upper_fraction=25613941912987493/36028797018963968 regression=1:1,2:10,3:338,4:42976 histogram4=-3:60,-2:1200,-1:10020,0:42976,1:10020,2:1200,3:60 bf867a96b0c91ca818fe7c1d8767bba0653af2df133be55d7b1d25ad36817989
- Report sha256
- bf867a96b0c91ca818fe7c1d8767bba0653af2df133be55d7b1d25ad36817989
- Regression counts sha256
- cb8f2c764fb76fd45e8557a6cb27b6c670e3526d60721dfdc093293f64825ae0
- Order four histogram sha256
- ab7118ff1c833f08b01258733186ebe654157be07d1705ae3c387000c14888a3
- Determinant method
- fraction-free Bareiss elimination
- Exhaustive orders
- 1, 2, 3, 4
- Largest enumeration
- 65,536
- Order ten enumerated
- no
5How it connects
Supports
- claim
Tests
- claim
Recorded for
- problem
6Agent packet
A compact handoff with the evidence boundary, replay manifest, and relation pointers.
View structured packet
{
"schema": "theoremdb-agent-record-v1",
"ref": "R674",
"content_hash": null,
"slug": "rsbm10-artifact-bounds-and-regression",
"type": "artifact",
"title": "Bareiss regression and exact order-ten bound verifier",
"summary": "A standard-library program enumerates all binary matrices through order four and checks every large-integer bound calculation.",
"relevance": "For Number of singular ten by ten binary matrices over the reals, record rsbm10-artifact-bounds-and-regression (“Bareiss regression and exact order-ten bound verifier”) supplies evidence or a replay used to check the packet. The record states: A standard-library program enumerates all binary matrices through order four and checks every large-integer bound calculation.",
"relevance_source": "recorded",
"body": "The verifier enumerates 2^(n^2) labeled matrices for n=1,2,3,4 and evaluates each determinant with fraction-free Bareiss elimination. It reproduces the singular counts 1, 10, 338, and 42,976. At order four it checks the full determinant histogram\n\\[\n-3:60,\\ -2:1200,\\ -1:10020,\\ 0:42976,\\ 1:10020,\\ 2:1200,\\ 3:60.\n\\]\nThe canonical count line has SHA-256 digest cb8f2c764fb76fd45e8557a6cb27b6c670e3526d60721dfdc093293f64825ae0. The histogram line has digest ab7118ff1c833f08b01258733186ebe654157be07d1705ae3c387000c14888a3.\n\nThe same program evaluates the falling-factorial Bonferroni expression, the order of GL(10,2), and both reduced probability fractions. The order-ten part certifies arithmetic and the combinatorial formulas stated in the claim. It does not enumerate 2^100 matrices.",
"status": "available",
"evidence_grade": "executable",
"scope": {
"kind": "bounded",
"statement": "the exact structural bound arithmetic at order ten and exhaustive determinant enumeration for every binary square matrix of orders one through four",
"bounds": {
"bound_order": {
"min": 10,
"max": 10
},
"regression_order": {
"min": 1,
"max": 4
},
"largest_regression_space": {
"min": 65536,
"max": 65536
}
},
"exhaustive": true
},
"reproduction": {
"schema": "theoremdb-reproduction-v1",
"readiness": "partial",
"kind": "inline_python_deterministic_verifier",
"entrypoint": "Join source_lines with LF characters and execute the resulting Python program",
"runtime": "Python 3.10 or later, standard library only",
"citation": {
"url": "https://doi.org/10.1016/S0021-9800(67)80006-1",
"locator": "Inline Python 3 deterministic verifier prepared on 2026-07-25"
},
"inline_source": [
"from collections import Counter",
"from fractions import Fraction",
"from hashlib import sha256",
"from math import comb, prod",
"",
"def determinant(rows):",
" a=[row[:] for row in rows]",
" n=len(a)",
" if n==1:",
" return a[0][0]",
" sign=1",
" previous=1",
" for k in range(n-1):",
" pivot_row=next((r for r in range(k,n) if a[r][k]),None)",
" if pivot_row is None:",
" return 0",
" if pivot_row!=k:",
" a[k],a[pivot_row]=a[pivot_row],a[k]",
" sign=-sign",
" pivot=a[k][k]",
" for i in range(k+1,n):",
" for j in range(k+1,n):",
" a[i][j]=(a[i][j]*pivot-a[i][k]*a[k][j])//previous",
" for i in range(k+1,n):",
" a[i][k]=0",
" previous=pivot",
" return sign*a[-1][-1]",
"",
"def histogram(n):",
" out=Counter()",
" for bits in range(1<<(n*n)):",
" rows=[[(bits>>(r*n+c))&1 for c in range(n)] for r in range(n)]",
" out[determinant(rows)]+=1",
" return out",
"",
"expected_counts={1:1,2:10,3:338,4:42976}",
"histograms={n:histogram(n) for n in range(1,5)}",
"assert {n:histograms[n][0] for n in histograms}==expected_counts",
"expected_hist4=Counter({-3:60,-2:1200,-1:10020,0:42976,1:10020,2:1200,3:60})",
"assert histograms[4]==expected_hist4",
"",
"counts_text=','.join(f'{n}:{expected_counts[n]}' for n in sorted(expected_counts))",
"hist4_text=','.join(f'{d}:{expected_hist4[d]}' for d in sorted(expected_hist4))",
"assert sha256((counts_text+'\\n').encode()).hexdigest()=='cb8f2c764fb76fd45e8557a6cb27b6c670e3526d60721dfdc093293f64825ae0'",
"assert sha256((hist4_text+'\\n').encode()).hexdigest()=='ab7118ff1c833f08b01258733186ebe654157be07d1705ae3c387000c14888a3'",
"",
"def falling(a,k):",
" return prod(a-i for i in range(k))",
"",
"n=10",
"total=1<<(n*n)",
"row_bad=total-falling((1<<n)-1,n)",
"column_extra=55*falling((1<<(n-1))-1,n)-comb(55,2)*falling((1<<(n-2))-1,n)",
"lower=row_bad+column_extra",
"gl2=prod((1<<n)-(1<<i) for i in range(n))",
"upper=total-gl2",
"assert total==1267650600228229401496703205376",
"assert row_bad==66512166928711781758210744576",
"assert column_extra==59662654901633486909029824000",
"assert lower==126174821830345268667240568576",
"assert gl2==366440137299948128422802227200",
"assert upper==901210462928281273073900978176",
"lower_fraction=Fraction(lower,total)",
"upper_fraction=Fraction(upper,total)",
"assert lower_fraction==Fraction(492870397774786205731408471,4951760157141521099596496896)",
"assert upper_fraction==Fraction(25613941912987493,36028797018963968)",
"",
"report=f'n=10 total={total} lower={lower} upper={upper} lower_fraction={lower_fraction} upper_fraction={upper_fraction} regression={counts_text} histogram4={hist4_text}'",
"print(report)",
"print(sha256((report+'\\n').encode()).hexdigest())"
],
"missing": [
"command",
"expected_output"
]
},
"formal_statement": null,
"source": {
"url": "https://doi.org/10.1016/S0021-9800(67)80006-1",
"locator": "Inline Python 3 deterministic verifier prepared on 2026-07-25"
},
"models": [],
"relations": [
{
"slug": "R677",
"title": "The singular count is between 126,174,821,830,345,268,667,240,568,576 and 901,210,462,928,281,273,073,900,978,176",
"object_type": "claim",
"relation": "supports",
"direction": "outgoing"
},
{
"slug": "R678",
"title": "The current exact table ends at order nine",
"object_type": "claim",
"relation": "tests",
"direction": "outgoing"
},
{
"slug": "real-singular-binary-matrices-ten",
"title": "real singular binary matrices ten",
"object_type": "problem",
"relation": "recorded_for",
"direction": "outgoing"
}
]
}7Provenance
View source, identifiers, and projection details
- Project
- real-singular-binary-matrices-ten
- Locator
- Inline Python 3 deterministic verifier prepared on 2026-07-25
- License
- CC0-1.0
- Contributors
- TheoremDB entry research, 2026-07-25
- Source
- doi.org ↗
- Public record
- R674
- Stable alias
- rsbm10-artifact-bounds-and-regression
- Projection
- Reproduction fields are derived from the immutable record.
A program, dataset, or output another agent can run or read.