De Morgan in practice: NOT (A AND B) rewritten and verified
NOT (A AND B) rewritten as NOT A OR NOT B and checked: the equivalence holds on all four rows, the normal-form converter records the De Morgan step, and the NAND-only form is a single gate.
“Not both” is the everyday reading of NOT (A AND B): the pair fails as soon as either part fails. De Morgan's law turns that into an OR of the negations, NOT A OR NOT B, which says the same thing with the negation pushed onto the inputs. The engine's reading of the original is “true exactly when it is not the case that both A and B are true”. On the four-row table the output is true on rows 0, 1 and 2, Σm(0, 1, 2), and false only on the all-true row.
The equivalence check holds: negated_and and rewritten agree on every assignment. Not a tautology, counterexample A = TRUE, B = TRUE; satisfiable, witness A = FALSE, B = FALSE. The simplifier found no law that shortens the two-operator original, because De Morgan does not reduce the operator count. The normal-form converter applies it once, and both the CNF and the DNF come out as NOT A OR NOT B. The Karnaugh map draws NOT A and NOT B, two prime implicants, deriving the same expression. The NAND-only form is 1 gate, NAND(A, B); the NOR-only form needs 7.
This rewrite is the one programmers perform most often, usually to get rid of a negated compound condition: !(a && b) becomes !a || !b, and the companion page shows that refactor verified. It also underlies negation normal form, which the converters build by pushing every NOT to the leaves. The thing to watch is the operator flip. The De Morgan slip page shows what happens when the NOT is distributed without changing AND to OR: the formulas differ on two of the four rows.
negated_and := NOT (A AND B) · outputrewritten := NOT A OR NOT B · output
Exact checks
Truth table (4 rows)
| # | A | B | negated_and | rewritten |
|---|---|---|---|---|
| 0 | FALSE | FALSE | TRUE | TRUE |
| 1 | FALSE | TRUE | TRUE | TRUE |
| 2 | TRUE | FALSE | TRUE | TRUE |
| 3 | TRUE | TRUE | FALSE | FALSE |
Plain-language reading
Beginnernegated_and is true exactly when the following condition does not hold:
1. both A and B are true
In every other case, negated_and is false.
About the operators:
- NOT reverses a value: true becomes false and false becomes true.
- AND is true only when all of its parts are true.
Intermediatenegated_and is true exactly when the following condition does not hold:
1. both A and B are true
In every other case, negated_and is false.
Advancednegated_and is true exactly when the following condition does not hold [negation ¬]:
1. both A and B are true [conjunction ∧]
In every other case, negated_and is false.
Formal notation: ¬(A ∧ B)
Logic diagram
Simplification, step by step
The engine found no law that shortens this expression; it is already as short as its rewriting laws can make it.
NOT (A AND B)
Karnaugh map
- cells m0, m1
NOT A - cells m0, m2
NOT B
Minterms and maxterms
negated_and(A, B) = Σm(0, 1, 2)negated_and(A, B) = ΠM(3)Variable order: A, B · 4 rows
Normal forms
Conjunctive normal form (CNF)
NOT A OR NOT B1 clauses, 2 literals · complete · De Morgan's law ×1
Disjunctive normal form (DNF)
NOT A OR NOT B2 terms, 2 literals · complete · De Morgan's law ×1
NAND-only and NOR-only
NAND-only form
NAND(A, B)1 gates · verified equivalent on every row
NOR-only form
NOR(NOR(NOR(A, A), NOR(B, B)), NOR(NOR(A, A), NOR(B, B)))7 gates · verified equivalent on every row
The same rule as code
Python
# -*- coding: utf-8 -*-
# Generated by Tangibool (tangibool.python/1). Do not edit by hand; regenerate from the source revision.
# Model: not stated
# Source revision: reference
# Interpretation status: not stated
"""Tangibool evaluator (decision model).
Generated deterministically from the validated Boolean model; it evaluates exactly that
model. It does not establish that the model is a complete, legal, or safe statement of a
real-world rule, and evaluating examples does not verify the rule.
Source revision: reference
Interpretation status: not stated
Exporter: 1.0.0 (format tangibool.python/1; engine 1.0.0; semantics tangibool-semantics/1)
Logic digest: 7b693f496b218ae8279ef667431dce2777ca65803616e6a038686f16ec33468c
Assumptions digest: d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78
Inputs (dict keys, declared order) and generated parameter names:
'A' -> input_A
'B' -> input_B
Outputs (declared order) and their functions:
'negated_and' -> output_negated_and(inputs)
'rewritten' -> output_rewritten(inputs)
Assumptions (the declared scope, checked by in_scope(inputs)):
none; every complete assignment is in scope.
Input contract:
Every required input must be exactly True or False (type(value) is bool). Missing
inputs, None, strings, integers (including 0 and 1), and keys that are not declared
inputs raise InputError before anything is evaluated, including inputs that a
short-circuiting operator would never read. Results are always bool.
Scope:
Decision functions raise OutOfScopeError when the inputs violate an assumption; an
out-of-scope assignment is never turned into a False result. Pass check_scope=False
to evaluate without the scope check, and use in_scope(inputs) to test it separately.
Usage:
import tangibool_model as model
model.evaluate({'A': False, 'B': False})
# -> {'negated_and': True, 'rewritten': True}
model.output_negated_and({'A': False, 'B': False})
# -> True
model.evaluate({'A': 1, 'B': False})
# raises InputError: 1 is an int, not a bool
"""
import collections.abc as _abc
from typing import Dict, Mapping, Tuple
__all__ = [
"MODEL_INFO",
"IDENTIFIER_MAP",
"INPUT_NAMES",
"OUTPUT_NAMES",
"ASSUMPTIONS",
"InputError",
"OutOfScopeError",
"minterm_index",
"in_scope",
"evaluate",
"output_negated_and",
"output_rewritten",
]
MODEL_INFO = {
"format": "tangibool.python/1",
"mode": "decision",
"model": None,
"revision": "reference",
"interpretation": None,
"exporter": "1.0.0",
"engine_version": "1.0.0",
"semantics_version": "tangibool-semantics/1",
"logic_digest": "7b693f496b218ae8279ef667431dce2777ca65803616e6a038686f16ec33468c",
"assumptions_digest": "d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78",
"primary_output": "negated_and",
"unused_inputs": (),
"subcircuits": (),
}
# Stable ID -> generated identifier (reversible mapping; names are exact, escaped strings).
IDENTIFIER_MAP = {
"v_e602fc4a5450": {"kind": "input", "name": "A", "identifier": "input_A"},
"v_a7bc6cef16e8": {"kind": "input", "name": "B", "identifier": "input_B"},
"f_f9171e4fac30": {"kind": "output", "name": "negated_and", "identifier": "output_negated_and"},
"f_a7cbd6eae6eb": {"kind": "output", "name": "rewritten", "identifier": "output_rewritten"},
}
INPUT_NAMES = ("A", "B")
OUTPUT_NAMES = ("negated_and", "rewritten")
ASSUMPTIONS = ()
_DECLARED_INPUTS = frozenset(INPUT_NAMES)
_SCOPE_INPUTS = ()
_ASSUMPTION_FORMULAS = {entry["name"]: entry["formula"] for entry in ASSUMPTIONS}
class InputError(ValueError):
"""Raised when inputs are missing, undeclared, or not exactly True/False.
Every required input must satisfy type(value) is bool. Missing inputs, None, strings,
integers (including 0 and 1), and keys that are not declared inputs are rejected before
anything is evaluated. A missing input is never treated as False. The individual problems
are listed in the problems attribute.
"""
def __init__(self, problems):
self.problems = tuple(problems)
super().__init__("Invalid inputs: " + "; ".join(self.problems))
class OutOfScopeError(ValueError):
"""Raised when the inputs violate the model's assumptions (its declared scope).
Outside its assumptions the rule has no decision: this error is never a False result.
The names of the violated assumptions are in the violated attribute.
"""
def __init__(self, violated):
self.violated = tuple(violated)
details = ", ".join(name + " (" + _ASSUMPTION_FORMULAS.get(name, "?") + ")" for name in self.violated)
super().__init__(
"Out of scope: the inputs violate "
+ ("assumption " if len(self.violated) == 1 else "assumptions ")
+ details
+ ". The rule has no decision outside its assumptions; this is not a False result."
)
def _short_repr(value: object) -> str:
text = repr(value)
return text if len(text) <= 60 else text[:57] + "..."
def _checked_inputs(inputs: Mapping[str, bool], required: Tuple[str, ...], check_scope: bool) -> Dict[str, bool]:
"""Validate inputs strictly before anything is evaluated and return a plain copy."""
if type(check_scope) is not bool:
raise TypeError("check_scope must be True or False")
if not isinstance(inputs, _abc.Mapping):
raise InputError(["inputs must be a mapping from input names to True/False, got " + type(inputs).__name__])
problems = []
for key in inputs:
if type(key) is not str or key not in _DECLARED_INPUTS:
declared = ", ".join(repr(name) for name in INPUT_NAMES) or "none"
problems.append("undeclared input " + _short_repr(key) + " (declared inputs: " + declared + ")")
needed = tuple(required) + (_SCOPE_INPUTS if check_scope else ())
for name in needed:
if name not in inputs:
problems.append("missing required input " + repr(name))
for key, value in inputs.items():
if type(key) is str and key in _DECLARED_INPUTS and type(value) is not bool:
problems.append(
"input " + repr(key) + " must be True or False (type bool), got "
+ type(value).__name__ + " " + _short_repr(value)
)
if problems:
raise InputError(list(dict.fromkeys(problems)))
return dict(inputs.items())
def _require_bool_args(function: str, pairs: Tuple[Tuple[str, object], ...]) -> None:
problems = [
"argument " + repr(name) + " of " + function + " must be True or False (type bool), got "
+ type(value).__name__ + " " + _short_repr(value)
for name, value in pairs
if type(value) is not bool
]
if problems:
raise InputError(problems)
def minterm_index(*values: bool) -> int:
"""Row index of an assignment over a declared variable order.
The first value is the most significant bit, so all-False is row 0. Used for indexed
minterm (sum) and maxterm (product) forms.
"""
index = 0
for value in values:
if type(value) is not bool:
raise InputError(["minterm_index() takes True/False values, got " + type(value).__name__ + " " + _short_repr(value)])
index = index * 2 + (1 if value else 0)
return index
def _scope_values(values: Dict[str, bool]) -> Tuple[Tuple[str, bool], ...]:
"""Value of every enabled, confirmed assumption for validated inputs, in declared order."""
return ()
def _require_in_scope(values: Dict[str, bool]) -> None:
violated = tuple(name for name, holds in _scope_values(values) if not holds)
if violated:
raise OutOfScopeError(violated)
def in_scope(inputs: Mapping[str, bool]) -> bool:
"""True when the inputs satisfy every assumption (the model's declared scope).
Requires the inputs the assumptions read; raises InputError for invalid inputs.
"""
values = _checked_inputs(inputs, (), True)
return all(holds for _, holds in _scope_values(values))
# ---- Formula bodies (inputs are validated by the public functions below) ----
def _compute_output_negated_and(input_A: bool, input_B: bool) -> bool:
"""Decision output 'negated_and': NOT (A AND B)"""
return not (input_A and input_B)
def _compute_output_rewritten(input_A: bool, input_B: bool) -> bool:
"""Decision output 'rewritten': NOT A OR NOT B"""
return (not input_A) or (not input_B)
# ---- Public API ----
def output_negated_and(inputs: Mapping[str, bool], *, check_scope: bool = True) -> bool:
"""Decision output 'negated_and' (stable ID f_f9171e4fac30).
'negated_and' := NOT (A AND B)
Required inputs: 'A', 'B'.
Raises InputError for missing, undeclared, or non-bool inputs, and OutOfScopeError
when check_scope is True and the inputs violate an assumption.
"""
values = _checked_inputs(inputs, ("A", "B"), check_scope)
if check_scope:
_require_in_scope(values)
output_negated_and_value = _compute_output_negated_and(values["A"], values["B"])
return output_negated_and_value
def output_rewritten(inputs: Mapping[str, bool], *, check_scope: bool = True) -> bool:
"""Decision output 'rewritten' (stable ID f_a7cbd6eae6eb).
'rewritten' := NOT A OR NOT B
Required inputs: 'A', 'B'.
Raises InputError for missing, undeclared, or non-bool inputs, and OutOfScopeError
when check_scope is True and the inputs violate an assumption.
"""
values = _checked_inputs(inputs, ("A", "B"), check_scope)
if check_scope:
_require_in_scope(values)
output_rewritten_value = _compute_output_rewritten(values["A"], values["B"])
return output_rewritten_value
def evaluate(inputs: Mapping[str, bool], *, check_scope: bool = True) -> Dict[str, bool]:
"""Evaluate every output (declared order); each output is computed once.
Required inputs: 'A', 'B'.
Raises InputError for missing, undeclared, or non-bool inputs, and OutOfScopeError
when check_scope is True and the inputs violate an assumption.
"""
values = _checked_inputs(inputs, ("A", "B"), check_scope)
if check_scope:
_require_in_scope(values)
output_negated_and_value = _compute_output_negated_and(values["A"], values["B"])
output_rewritten_value = _compute_output_rewritten(values["A"], values["B"])
return {
"negated_and": output_negated_and_value,
"rewritten": output_rewritten_value,
}
PostgreSQL
-- Tangibool PostgreSQL export (tangibool.postgresql/1)
-- Target: PostgreSQL 16 or later with typed boolean inputs; tested with PostgreSQL 18.3 (PGlite 0.5.8).
-- Not a claim of compatibility with other SQL dialects.
-- Model: not stated
-- Source revision: reference
-- Interpretation status: not stated
-- Exporter: 1.0.0 (engine 1.0.0; semantics tangibool-semantics/1)
-- Logic digest: 7b693f496b218ae8279ef667431dce2777ca65803616e6a038686f16ec33468c
-- Assumptions digest: d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78
-- Mode: decision
--
-- Read-only: this file contains only WITH / VALUES / SELECT statements. No DDL, DML,
-- procedures, credentials, network access, or database changes. The expressions are pure
-- Boolean expressions, so their result does not depend on SQL evaluation order.
--
-- NON-NULL INPUT PRECONDITION: NULL is not a truth value of this rule. Every fragment below
-- is defined only when every input column it reads is a non-NULL boolean. The guard template
-- (section 2) marks any NULL input as 'invalid_input' and any row that violates the
-- assumptions as 'out_of_scope', and fills output columns only for 'ok' rows; a NULL in an
-- output column of another row is an envelope marker, not a third truth value. Never wrap
-- the fragments in COALESCE(..., FALSE) or otherwise choose a meaning for unknown inputs.
-- This evaluates the supplied model; it does not show that the model is a complete, legal,
-- or safe statement of a real-world rule.
--
-- Input columns (stable ID: name -> column):
-- v_e602fc4a5450: A -> "in_A"
-- v_a7bc6cef16e8: B -> "in_B"
-- Output columns:
-- f_f9171e4fac30: negated_and -> "out_negated_and"
-- f_a7cbd6eae6eb: rewritten -> "out_rewritten"
-- Assumptions (scope K; exported separately and never folded into an output):
-- none; every complete input row is in scope.
-- ----------------------------------------------------------------------------------------------
-- 1. Expression fragments (parenthesised; non-NULL boolean inputs required)
-- ----------------------------------------------------------------------------------------------
--
-- [out_negated_and] output negated_and (stable ID f_f9171e4fac30)
-- negated_and := NOT (A AND B)
-- PRECONDITION: "in_A", "in_B" are non-NULL booleans.
-- (NOT ("in_A" AND "in_B"))
--
-- [out_rewritten] output rewritten (stable ID f_a7cbd6eae6eb)
-- rewritten := NOT A OR NOT B
-- PRECONDITION: "in_A", "in_B" are non-NULL booleans.
-- ((NOT "in_A") OR (NOT "in_B"))
--
-- ----------------------------------------------------------------------------------------------
-- 2. Guard template (read-only SELECT over an inputs list)
-- ----------------------------------------------------------------------------------------------
-- Replace the example rows between the markers with your own rows, or replace the inputs
-- CTE with a SELECT that provides the boolean columns listed in the header. status is
-- 'invalid_input' when any input is NULL, 'out_of_scope' when the assumptions are false, and
-- 'ok' otherwise; output columns are filled only for 'ok' rows (NULL elsewhere is an
-- envelope marker). Assumption columns are shown for rows with complete inputs.
WITH inputs ("row_id", "in_A", "in_B") AS (
-- tangibool:example-rows:begin
VALUES
(1, FALSE, FALSE),
(2, FALSE, TRUE),
(3, TRUE, FALSE),
(4, TRUE, TRUE),
(5, NULL::boolean, FALSE)
-- tangibool:example-rows:end
),
checked AS (
SELECT
inputs.*,
CASE
WHEN "in_A" IS NULL OR "in_B" IS NULL THEN 'invalid_input'
ELSE 'ok'
END AS "status"
FROM inputs
)
SELECT
"row_id",
"in_A",
"in_B",
"status",
CASE WHEN "status" = 'ok' THEN (NOT ("in_A" AND "in_B")) END AS "out_negated_and",
CASE WHEN "status" = 'ok' THEN ((NOT "in_A") OR (NOT "in_B")) END AS "out_rewritten"
FROM checked
ORDER BY "row_id";
SMT-LIB
; Tangibool SMT-LIB export (tangibool.smtlib/1) ; Profile: SMT-LIB 2.6 Boolean subset (QF_UF): Bool constants, define-fun, let, push/pop, assert, check-sat, echo. ; Tested with Z3 5.1.0 (z3-solver 5.2.0, WebAssembly build). ; Model: not stated ; Source revision: reference ; Interpretation status: not stated ; Exporter: 1.0.0 (engine 1.0.0; semantics tangibool-semantics/1) ; Logic digest: 7b693f496b218ae8279ef667431dce2777ca65803616e6a038686f16ec33468c ; Assumptions digest: d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78 ; Mode: decision ; ; Reading results: each query block states what sat and unsat mean for its question. A sat ; answer comes with a witness or counterexample (ask for it with (get-model) after sat only); ; unsat settles that scoped query and is solver-checked, not an independently checked proof ; certificate; unknown settles nothing. Blocks are scoped with push/pop, so assertions never ; accumulate across questions. This checks the supplied model, not whether it is a complete, ; legal, or safe statement of a real-world rule. ; ; Symbols (stable ID: name -> symbol): ; v_e602fc4a5450: A -> in_A ; v_a7bc6cef16e8: B -> in_B ; f_f9171e4fac30: negated_and -> out_negated_and ; f_a7cbd6eae6eb: rewritten -> out_rewritten (set-option :produce-models true) (set-info :smt-lib-version 2.6) (set-logic QF_UF) ; ---- Inputs (declared order) (declare-const in_A Bool) ; A (declare-const in_B Bool) ; B ; ---- Outputs (dependencies first) ; output negated_and (stable ID f_f9171e4fac30): NOT (A AND B) (define-fun out_negated_and () Bool (not (and in_A in_B))) ; output rewritten (stable ID f_a7cbd6eae6eb): NOT A OR NOT B (define-fun out_rewritten () Bool (or (not in_A) (not in_B))) ; ---- Assumptions (the scope K; never folded into an output) ; K: conjunction of the enabled, confirmed assumptions (true when there are none) (define-fun K () Bool true) ; ---- Definitions only: nothing is asserted and no check is run. ; Add (assert ...) and (check-sat) inside (push 1) ... (pop 1) to ask a question.
LaTeX
negated_and = \overline{A \cdot B}
rewritten = \overline{A} + \overline{B}The common slip
Pushing the NOT inside and keeping the AND: NOT A AND NOT B. That is “neither”, not “not both”. The two differ on the rows A = TRUE, B = FALSE and A = FALSE, B = TRUE, where “not both” is true and “neither” is false. The slip page runs that comparison.
Questions
Why is the NAND count 1 here when most pages need many gates?
Because NOT (A AND B) is what a NAND gate computes. The converter recognises the shape and emits NAND(A, B). The NOR builder has no such shortcut and constructs the same function from inverters and a NOR, 7 gates in its mechanical form.
Which side of the law should I write in code?
Whichever reads better in context; the table proves they are the same. NOT A OR NOT B is usually clearer when the two conditions are checked separately, and NOT (A AND B) when “both” is the natural concept. The equivalence checker settles any doubt after an edit.
Related
Operators and gates
- NOT gate (inverter): truth table, symbol and the NOT operator
- AND gate: truth table for 2, 3 and 4 inputs, symbol and expression
- OR gate: truth table for 2, 3 and 4 inputs, inclusive or explained
Tools
- Truth table generator: any Boolean expression, up to 12 variables, with intermediate columns
- Boolean algebra calculator and simplifier with steps (every law named and verified)
- Karnaugh map solver: 2 to 4 variables, groups, SOP and POS, don’t-cares from assumptions
Practice
See also
- De Morgan's laws: NOT (A AND B) = NOT A OR NOT B, with proof and examples
- The De Morgan slip: NOT (A AND B) vs NOT A AND NOT B
- De Morgan in code: !(a && b) refactored to !a || !b, verified
- NOT (A AND B) = NOT A OR NOT B, not NOT A AND NOT B
- Lesson 6: De Morgan’s laws (intermediate, 12 min)
- Negation normal form: meaning and examples
Worked examples
Generated from the Tangibool engine, semantics tangibool-semantics/1, engine 1.0.0, build . Every table, map, simplification and verdict on these pages was computed by the Tangibool engine when the site was built, and every claim was re-checked by the same exact checker the app uses. Limits are stated where they apply. © 2026 Foreranger LTD.