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Absorption in practice: A OR (A AND B) simplified

A OR (A AND B) is just A. The simplifier shows the single absorption step, the normal forms and the Karnaugh map agree, and the four-row table proves it.

The formula says: A, or else both A and B. If A is true the first disjunct already settles it; if A is false the second disjunct is false too. So B never matters. The engine's reading lists the two conditions, A alone or A with B, and the table shows the output true on rows 2 and 3, Σm(2, 3), which is exactly the column of A. The function is contingent: not a tautology, counterexample A = FALSE, B = FALSE; satisfiable, witness A = TRUE, B = FALSE.

Here the simplifier changes something. It applies the absorption law, recorded as A OR (A AND B) = A, and in one step takes the expression from two operators to none: the result is A, labelled simplified and exactly equivalent on every assignment. The normal-form converters reach the same place: the DNF applies absorption once and the CNF applies distribution and then absorption, both returning A. The Karnaugh map draws one group on cells 2 and 3 and derives A, one prime implicant. The gate converters work on the original expression: 3 NAND gates, NAND(NAND(A, A), NAND(A, B)), and 9 NOR gates.

Absorption is the law behind every “redundant condition” lint warning. A rule such as “allow if admin, or if admin and verified” allows every admin; the second clause documents an intention that the logic does not enforce. In code it shows up as x || (x && y), often after a refactor removed the case that made the second branch different. The thing to watch is the reverse direction: if the rule was meant to require verification, the fix is A AND B, not a tidier spelling of A. The table tells you what the rule does; only the author knows what it was for.

f := A OR (A AND B) · output

Exact checks

Always true (tautology)? No Counterexample: A = F, B = F · Exhaustively checked and solver cross-checked · 1 rows
Always false (contradiction)? No Counterexample: A = T, B = F · Exhaustively checked and solver cross-checked · 3 rows
Can be true? Yes Example assignment: A = T, B = F · Exhaustively checked and solver cross-checked · 3 rows

Truth table (4 rows)

Absorption in practice: A OR (A AND B) simplified
#ABf
0FALSEFALSEFALSE
1FALSETRUEFALSE
2TRUEFALSETRUE
3TRUETRUETRUE

Plain-language reading

Beginnerf is true exactly when at least one of the following holds (both may hold):
1. A is true
2. both A and B are true
In every other case, f is false.
About the operators:
- OR is inclusive: it is true when at least one part is true, including when several are.
- AND is true only when all of its parts are true.
Intermediatef is true exactly when at least one of the following holds (both may hold):
1. A is true
2. both A and B are true
In every other case, f is false.
Advancedf is true exactly when at least one of the following holds (both may hold) [inclusive disjunction ∨]:
1. A is true
2. both A and B are true [conjunction ∧]
In every other case, f is false.
Formal notation: A ∨ (A ∧ B)

Logic diagram

Absorption in practice: A OR (A AND B) simplified Absorption in practice: A OR (A AND B) simplified A B f OR AND
Absorption in practice: A OR (A AND B) simplified Absorption in practice: A OR (A AND B) simplified A B f OR AND

Simplification, step by step

A OR (A AND B)
  1. Absorption · A OR (A AND B) = A
    A OR (A AND B) → AResult: A
A · verified equivalent on every row · simplified, not minimal

Karnaugh map

A \ B
0
1
0
0m0
0m1
1
1m2
1m3
  1. cells m2, m3A
A · Derived A. Exactly equivalent on every assignment.

Minterms and maxterms

f(A, B) = Σm(2, 3)f(A, B) = ΠM(0, 1)

Variable order: A, B · 4 rows

Normal forms

Conjunctive normal form (CNF)

A

1 clauses, 1 literals · complete · Distribution ×1, Absorption ×1

Disjunctive normal form (DNF)

A

1 terms, 1 literals · complete · Absorption ×1

NAND-only and NOR-only

NAND-only form

NAND(NAND(A, A), NAND(A, B))

3 gates · verified equivalent on every row

NOR-only form

NOR(NOR(A, NOR(NOR(A, A), NOR(B, B))), NOR(A, NOR(NOR(A, A), NOR(B, B))))

9 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: e8ba70c86d7f7aedffe08c45b0b6630b0671835f759f2823d7be9f4650cfa19c
Assumptions digest: d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78

Inputs (dict keys, declared order) and generated parameter names:
    'A' -> input_A
    'B' -> input_B

Outputs (declared order) and their functions:
    'f' -> output_f(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})
    # -> {'f': False}
    model.output_f({'A': False, 'B': False})
    # -> False
    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_f",
]

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": "e8ba70c86d7f7aedffe08c45b0b6630b0671835f759f2823d7be9f4650cfa19c",
    "assumptions_digest": "d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78",
    "primary_output": "f",
    "unused_inputs": (),
    "subcircuits": (),
}

# Stable ID -> generated identifier (reversible mapping; names are exact, escaped strings).
IDENTIFIER_MAP = {
    "v_d5ea99f8885d": {"kind": "input", "name": "A", "identifier": "input_A"},
    "v_4fee4d3266ea": {"kind": "input", "name": "B", "identifier": "input_B"},
    "f_34dcbf4005e9": {"kind": "output", "name": "f", "identifier": "output_f"},
}

INPUT_NAMES = ("A", "B")
OUTPUT_NAMES = ("f",)
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_f(input_A: bool, input_B: bool) -> bool:
    """Decision output 'f': A OR (A AND B)"""
    return input_A or (input_A and input_B)


# ---- Public API ----


def output_f(inputs: Mapping[str, bool], *, check_scope: bool = True) -> bool:
    """Decision output 'f' (stable ID f_34dcbf4005e9).

    'f' := A OR (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_f_value = _compute_output_f(values["A"], values["B"])
    return output_f_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_f_value = _compute_output_f(values["A"], values["B"])
    return {
        "f": output_f_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: e8ba70c86d7f7aedffe08c45b0b6630b0671835f759f2823d7be9f4650cfa19c
-- 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_d5ea99f8885d: A -> "in_A"
--   v_4fee4d3266ea: B -> "in_B"
-- Output columns:
--   f_34dcbf4005e9: f -> "out_f"
-- 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_f] output f (stable ID f_34dcbf4005e9)
--   f := A OR (A AND B)
--   PRECONDITION: "in_A", "in_B" are non-NULL booleans.
--   ("in_A" OR ("in_A" AND "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 ("in_A" OR ("in_A" AND "in_B")) END AS "out_f"
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: e8ba70c86d7f7aedffe08c45b0b6630b0671835f759f2823d7be9f4650cfa19c
; 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_d5ea99f8885d: A -> in_A
;   v_4fee4d3266ea: B -> in_B
;   f_34dcbf4005e9: f -> out_f

(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 f (stable ID f_34dcbf4005e9): A OR (A AND B)
(define-fun out_f () Bool (or in_A (and in_A 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
f = A + \left(A \cdot B\right)

The common slip

Treating A OR (A AND B) as “A, but checked more carefully when B holds”. The table has no such row: the output equals A on all four rows, and B changes nothing. If B was supposed to restrict A, the formula is wrong, and the simplifier's one-step result is the quickest way to notice.

Questions

Does the simplifier claim A is the shortest possible form?

It reports the result as simplified, with the law it used, and the equivalence is exact. For this example A plainly cannot be shortened further, but in general the simplifier labels its output simplified rather than shortest, because it stops when no named law applies.

Related

Laws

Operators and gates

Tools

See also

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.