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Loop invariant

A loop invariant is a logical assertion or condition about the state of program variables that remains true immediately before the execution of a loop, after each complete iteration of the loop body, and upon the loop's termination, provided the loop terminates. Introduced as part of in the late 1960s, loop invariants provide a foundational tool for rigorously proving the correctness of iterative algorithms and programs using techniques from and axiomatic semantics. This approach, pioneered by C. A. R. Hoare, enables verification of both partial correctness (the program meets its specification if it terminates) and total correctness (the program terminates and meets its specification). In practice, loop invariants are essential for analyzing the behavior of loops in algorithms, such as those for or , by capturing properties like the partial ordering of array elements during execution. To qualify as a valid loop invariant, a property must satisfy three key conditions, often verified through Hoare triples {P} S {Q}, where P is the , S is the loop body, and Q is the postcondition. First, initialization: the invariant must hold true prior to the first of the loop, typically established by showing that the loop's implies the . Second, maintenance: if the invariant holds at the start of an and the loop condition is true, then after executing the loop body, the invariant must again hold true, preserving the property across iterations. Third, termination: upon loop exit (when the guard becomes false), the , combined with the negation of the guard, must imply the desired postcondition of the overall program or . These properties ensure that the loop behaves as intended, and termination often relies on demonstrating that a variant—a non-negative that strictly decreases with each —eventually reaches zero. Loop invariants extend beyond theoretical proofs to practical applications in , including tools, , and optimization, where they help identify subtle errors in iterative code that testing alone might miss. For instance, in the algorithm, the invariant "the subarray A[1..j] consists of the elements originally in A[1..j] but in sorted order" maintains partial correctness throughout the process.

Fundamentals

Definition

A loop invariant is a logical assertion or property concerning the variables within a that holds true immediately before the loop begins, at the start of each subsequent iteration, and after the loop terminates. This serves as an inductive invariant on the , remaining valid throughout the loop's execution provided the loop preserves it when the exit is false. In algorithm analysis, loop invariants play a crucial role in establishing the partial correctness of loops, meaning that if the loop terminates, the desired postcondition is satisfied assuming the holds. By maintaining this invariant across iterations, it enables rigorous that the loop's behavior aligns with the overall program's specifications. Within , loop invariants bridge the of the loop and its postcondition, facilitating the decomposition of program correctness proofs into manageable parts akin to Hoare triples. The concept of loop invariants was first introduced by in 1967 as a tool for through assertions placed along paths of .

Historical Context

The concept of loop invariants was first introduced by in his seminal 1967 paper, where he proposed using logical assertions to assign meanings to programs within the framework of axiomatic semantics, enabling of program correctness through on loop behaviors. This idea was significantly expanded by C. A. R. Hoare in 1969, who developed as an axiomatic system for reasoning about programs, explicitly formalizing loop invariants to establish partial correctness by ensuring that the invariant holds before and after each loop iteration. In the 1970s, loop invariants played a key role in the movement, influencing efforts to eliminate unstructured control flows like statements by emphasizing verifiable loop structures and assertions, as discussed in works by and Donald E. Knuth, which integrated invariants into broader for program design and proof. A major milestone came in 1976 with Dijkstra's adoption of loop invariants in his predicate transformer calculus outlined in A Discipline of Programming, providing a systematic approach to deriving programs from specifications while ensuring invariant preservation. By the , loop invariants saw modern extensions in , with techniques for invariant generation via constraint solving and abstraction enabling scalable verification of complex loops in tools like those based on predicate abstraction. Post-2015 developments further advanced their use in for concurrent systems. More recently, as of 2023, techniques leveraging large language models for inductive loop synthesis have emerged, aiding automated verification of complex .

Properties

Initialization

The initialization property of a loop requires that the holds true immediately prior to the execution of the loop's first . This ensures the enters the loop in a that satisfies the , serving as the starting point for subsequent verifications of correctness. In practice, the is often derived directly from the loop's , with any necessary assignments performed beforehand to establish this truth. Formally, for a loop construct of the form while B do S—where B is the condition and S is the body—the invariant I must satisfy the implication that the precondition pre entails I, denoted as {pre} ⇒ I. This relationship guarantees the invariant's validity at loop entry, independent of the number of iterations that follow. The importance of this property cannot be overstated, as it establishes a valid baseline state that allows the invariant to be preserved across iterations, ultimately contributing to the loop's overall correctness. Without proper initialization, the loop risks beginning in an inconsistent state, which may propagate errors through early iterations and lead to or incorrect outcomes. Common pitfalls include overlooking the precondition's implications or inadequately setting initial variable values, both of which undermine the invariant from the outset.

Maintenance

The property of a loop invariant requires that, after each execution of the loop body, the invariant remains true, provided it held true prior to the execution and the loop condition continues to hold. This preservation ensures the loop's reliability across multiple iterations by guaranteeing that the invariant's assertion about the program's state is upheld throughout the execution. Formally, the requirement is expressed in as the assertion \{I \land B\} \, S \, \{I\}, where I is the invariant, B is the loop condition, and S is the loop body; this states that if I and B are true before executing S, then I is true afterward. This rule forms the basis for the while-loop axiom in , enabling the inference that I holds upon loop termination when combined with the negation of B. In inductive reasoning, the maintenance property treats the loop as a series of repeated applications of the body S, where each iteration preserves the , allowing the proof of overall loop correctness to proceed by on the number of iterations. This approach mirrors , with the serving as the inductive hypothesis that is strengthened step by step. Challenges in ensuring maintenance arise particularly with side effects, such as updates to arrays or other mutable structures, which can alter program states in ways that complicate preservation unless the invariant explicitly accounts for changes (e.g., through "aging" clauses that track partial progress). Similarly, complex data structures like linked lists or trees introduce difficulties, as invariants must precisely capture relationships such as acyclicity or ordering while handling pointer manipulations or traversals that risk violating the property.

Termination

In the context of loop invariants, the termination property addresses the guarantees provided upon exit from the loop. When the loop condition B evaluates to false, signaling the end of iteration, the invariant I holds true alongside the negation of the condition \neg B. This conjunction I \land \neg B must logically imply the desired postcondition Q of the overall program segment, thereby completing the proof of the loop's behavioral correctness assuming it terminates. Formally, this is captured in Hoare logic's while rule for partial correctness: if \{I \land B\} \, S \, \{I\} (where S is the loop body), then \{I\} while B do S \{I \land \neg B\}, and further \{I \land \neg B\} \Rightarrow Q. This ensures that the postcondition is achieved upon exit. For total correctness, which guarantees both correct behavior and halting, the invariant must be paired with a separate termination argument, such as a loop variant—a quantity that decreases strictly in each iteration over a well-founded domain (e.g., the natural numbers)—to prove the loop executes finitely many times. Standard loop invariants in establish partial correctness, verifying the postcondition under the assumption of termination, but do not inherently prove the loop halts; dedicated termination proofs are required to address potential non-termination. Loop variants provide this supplementary evidence by demonstrating toward exit. A practical benefit of crafting strong invariants—those tightly linked to the postcondition—is their ability to expose termination issues early, as they highlight whether the loop makes sufficient to reach the exit condition.

Examples

Informal Example

A straightforward illustration of a loop invariant appears in a C program designed to find the maximum value in a non-empty array of integers. Consider the following code snippet, which initializes the maximum to the first element and iterates through the rest of the array: 
c
#include <stdio.h>

int main() {
    int arr[] = {3, 1, 4, 1, 5, 9, 2};
    int n = sizeof(arr) / sizeof(arr[0]);
    if (n == 0) {
        // Handle empty array case, e.g., return error or undefined
        printf("Array is empty\n");
        return 1;
    }
    int max_val = arr[0];
    for (int i = 1; i < n; i++) {
        if (arr[i] > max_val) {
            max_val = arr[i];
        }
    }
    printf("Maximum value: %d\n", max_val);
    return 0;
}
The loop invariant here states that, at the beginning of each iteration (just before the if statement), max_val holds the maximum value among the elements arr[0] through arr[i-1]. This property provides an intuitive way to track the algorithm's progress without delving into formal verification. To see how the invariant holds step by step, examine the execution with the array {3, 1, 4, 1, 5, 9, 2} (where n=7). Before the loop starts, i=1 and max_val=3, which is the maximum of the subarray arr[0..0] (just the first element), satisfying the . In the first (i=1), arr[1]=1 is compared to max_val=3; since 1 < 3, max_val remains 3, now the maximum of arr[0..1]. In the third (i=3), arr[3]=1 < 4 (updated from previous), so no change, maintaining the for arr[0..3]. By the fifth (i=5), arr[5]=9 > current max_val=5, updating it to 9, which becomes the maximum of arr[0..5]. After the final (i=6), the exits with i=n, and max_val=9 is the maximum of the entire arr[0..6], as the implies upon termination. For , the code checks if the array is empty (n==0) before the loop, avoiding or incorrect results in such cases; otherwise, initializing max_val to a like the minimum integer could handle empty arrays by setting the maximum to that sentinel post-loop. This adjustment ensures robustness, as an empty array has no maximum, and the program signals an error accordingly. This informal example relies on intuitive walkthroughs of the code's execution to demonstrate the invariant's role in ensuring correctness, aiding beginners by building conceptual understanding without relying on axioms or formal logic. It shares conceptual similarities with Hoare-style proofs, which are explored more rigorously in the formal example section.

Formal Example

A formal example of a loop invariant in the context of demonstrates how to verify partial correctness of a loop using a , an , and a postcondition. Consider a simple that initializes variables such that y = 10 - x (with x < 10 and y \geq 0), then executes the loop: \begin{align*} &\{ y = 10 - x \} \\ &\text{while } (x < 10) \text{ do} \\ &\quad x := x + 1; \\ &\quad y := y - 1 \\ &\text{end} \\ &\{ x = 10 \} \end{align*} The loop invariant is I: x + y = 10. To verify partial correctness using Hoare logic, three conditions must hold: initialization, maintenance, and termination implication. Initialization: The invariant holds before the loop begins. Given the precondition y = 10 - x, it follows that x + y = x + (10 - x) = 10, so I is true. Maintenance: The loop body preserves the invariant. Assume I holds and the loop condition x < 10 is true. After executing x := x + 1 followed by y := y - 1, the new values satisfy x' + y' = (x + 1) + (y - 1) = x + y = 10, so I holds after the body. Termination implication: Upon loop exit, the condition x < 10 is false, so x \geq 10. Combined with the invariant I, we have x + y = 10 and x \geq 10. Assuming y \geq 0 (preserved by the body since initial y \geq 0 and decrements only while y > 0 implicitly via the loop progress), it follows that x = 10 and y = 0, satisfying the postcondition x = 10. This establishes partial correctness: if the loop terminates, the postcondition holds.

Theoretical Foundations

Floyd–Hoare Logic

Floyd–Hoare logic provides an axiomatic framework for reasoning about program correctness, particularly through the use of logical assertions to verify the behavior of imperative programs. In this approach, program semantics are defined via triples of the form {P} S {Q}, where P is a precondition (an assertion true before executing statement S), S is the program statement, and Q is a postcondition (an assertion true after S terminates). This notation, introduced by C. A. R. Hoare, enables deductive proofs of program properties by composing rules for basic constructs like assignments, conditionals, and loops. The foundational ideas trace back to Robert W. Floyd's 1967 work, which proposed assigning meanings to programs using assertions placed at points in flowcharts to establish predicates that hold along execution paths, laying the groundwork for of loops via invariants. Hoare built upon this in 1969 by formalizing the triple notation and providing a complete set of axioms and inference rules for partial correctness, focusing on ensuring that if a program starts in a satisfying P and terminates, it ends in a satisfying Q—without guaranteeing termination itself. Central to handling loops in this logic is the loop rule, which relies on an invariant I: if {I ∧ B} S {I} holds (where B is the loop and S the loop body), then {I} while B do S {I ∧ ¬B} follows, allowing about the loop's effect by showing the preserved across iterations and implying the postcondition upon exit. This rule supports partial correctness proofs for loops, as the captures a maintained throughout execution if the loop terminates. For total correctness, which requires both behavioral correctness and guaranteed termination, the logic extends partial correctness by incorporating loop variants—typically a non-negative expression that strictly decreases with each while remaining bounded below. Invariants alone suffice for partial correctness, but combining them with variants proves termination, ensuring the loop cannot run indefinitely.

Role in Proving Correctness

Loop invariants play a central role in establishing the correctness of iterative programs through an inductive proof structure. This approach mirrors and involves verifying three key steps for a loop with precondition P, body S, condition B, and postcondition Q: first, initialization, confirming that I (the ) holds upon entry under P; second, maintenance, showing that if I and B hold before an , then I holds after executing S; and third, termination, demonstrating that when the loop exits (I ∧ ¬B), Q follows. This framework ensures partial correctness: if the loop terminates, the postcondition is satisfied. In broader program verification, loop invariants integrate seamlessly with procedure-level preconditions and postconditions, enabling modular proofs that chain across components. By treating the loop's postcondition (derived from I ∧ ¬B) as the precondition for subsequent code and vice versa for preceding segments, invariants facilitate compositional reasoning, where the correctness of the whole program emerges from verified parts without re-proving interactions. This chaining supports scalable analysis in environments. For programs with nested loops, invariants compose hierarchically to manage . The of an inner contributes to the outer loop's , often by abstracting the inner loop's as a refined postcondition that preserves the outer . This layered structure allows to proceed inward-outward, ensuring each nesting level upholds the overall program's properties without conflating scopes. A key limitation of loop invariants is their focus on partial correctness; they verify outcomes assuming termination but do not prove the loop halts. To address total correctness, invariants must pair with a decreasing variant—a non-negative that strictly decreases each and reaches zero at exit, such as a bounded by input size—ruling out infinite loops. Without this, non-termination remains possible even with a valid . This inductive use of invariants extends into modern calculi, notably Dijkstra's weakest , where help compute the precise initial states ensuring postconditions via backward propagation through bodies. Here, the approximates the weakest liberal precondition of the , refining proofs for complex control flows while maintaining rigor.

Programming Language Support

Eiffel

Eiffel, conceived in 1985 by as part of the methodology, provides built-in support for loop invariants directly in its loop syntax to facilitate the specification and verification of loop correctness. The language's loop construct, introduced from the outset, includes an optional invariant clause that expresses a boolean assertion holding true just after the loop's initialization and preserved by each iteration of the body, before the exit condition is evaluated. This feature integrates seamlessly with Eiffel's object-oriented design principles, allowing developers to embed formal correctness properties within the code itself. The basic syntax for a from-until loop (the standard form since Eiffel's early versions) is from initialization invariant assertion until condition loop body end, where the assertion can reference loop-local variables and class attributes. For instance, a simple traversal loop might be written as: 
from
    i := low
invariant
    min <= i <= max + 1
until
    i > max
loop
    -- Process a[i]
    i := i + 1
end
Here, the invariant ensures the index i remains within the intended bounds throughout the loop's execution. During compilation, loop invariants are treated as assertions and checked at runtime only if assertion monitoring is enabled (via options like -all in the EiffelStudio compiler), triggering a Loop invariant violated exception if falsified to aid debugging. This runtime verification supports Design by Contract by enforcing that loops adhere to their specified behavior, promoting reliable software construction without compromising performance in production builds where assertions are disabled. A classic example is the on a sorted t of size n seeking value x, where the captures the search properties: 
from
    i := 1
    j := n + 1
    Result := 0
invariant
    1 <= i and i <= j and j <= n + 1;
    0 <= Result and Result <= n;
    across 1 |..| (i - 1) as k all t[k.item] < x end;
    across j |..| n as k all t[k.item] > x end;
    (Result > 0) implies t[Result] = x
until
    i >= j or Result > 0
loop
    m := i + (j - i) // 2
    if t[m] < x then
        i := m + 1
    elseif t[m] > x then
        j := m
    else
        Result := m
    end
end
This guarantees that x is in the subarray from i to j-1 if anywhere, all elements before i are less than x, all after j-1 are greater, and Result holds a valid index if set. By specifying such invariants, Eiffel enables design-time enforcement of loop semantics, enhancing reliability, , and refactoring safety through automated checks during development.

Whiley

Whiley is a verification-oriented programming language developed by David J. Pearce starting in 2009, emphasizing static checking of program correctness through integrated formal methods. One of its core features is mandatory support for loop invariants in while loops, which are declared using where clauses to specify properties that must hold true at the beginning of each iteration and upon loop exit (when the condition becomes false). These invariants are expressed as boolean expressions and can be combined via logical conjunction if multiple where clauses are provided. The syntax for a in Whiley follows the form while <condition> where <invariant> : <body>, where the aids in proving correctness without runtime evaluation. For instance, invariants are required in loops to ensure bounds checking and prevent errors like array overflows. The language's verifying , including tools such as the Whiley File Checker, statically verifies these invariants using and constraint solving, ensuring they hold across all possible executions and eliminating the need for runtime checks. This verification process integrates invariants into the , enabling flow-sensitive typing that adapts to loop state changes. A representative example is a to elements in an while maintaining an on the : 
int [sum](/page/Sum)([int] xs) -> (int r)
requires all { i in 0 .. |xs| | xs[i] >= 0 }:
    int i = 0
    int total = 0
    while i < |xs|
        where i >= 0:
        total = total + xs[i]
        i = i + 1
    return total
Here, the invariant i >= 0 ensures the index remains non-negative throughout the , which the verifier confirms holds on entry (since i = 0) and is preserved by the body (i = i + 1). This prevents potential underflow errors and contributes to proving the method's postcondition that the returned equals the array total. A key unique aspect of Whiley's approach is the absence of runtime overhead for invariants, as they are fully resolved during compilation via exhaustive static analysis rather than dynamic assertion testing. This design positions Whiley as particularly suitable for safety-critical applications, where verification guarantees correctness without performance penalties.

Dafny

Dafny is a verification-oriented programming language developed by Microsoft Research since 2009, designed for writing and verifying functionally correct programs. It provides built-in support for loop invariants using the invariant keyword within while loops, enabling static verification of loop correctness alongside preconditions, postconditions, and termination metrics. The syntax for a in is while condition invariant assertion decrease variant { body }, where the is a that must hold before the loop, after each , and on exit. The optional decrease specifies a variant (e.g., a non-negative that decreases) to prove termination. Dafny's verifier uses (via Z3 SMT solver) to check that the is maintained and implies the postcondition upon exit, integrating seamlessly with the language's for ghost variables and specifications. For example, a simple sum function might use: 
method Sum(xs: [array](/page/Array)<[int](/page/INT)>) returns (r: [int](/page/INT))
    ensures r == [Sum](/page/Sum)Of(xs);
{
    var i := 0;
    var total := 0;
    while i < xs.Length
        [invariant](/page/Invariant) 0 <= i <= xs.Length;
        [invariant](/page/Invariant) total == SumOf(xs[0..i]);
        decrease xs.Length - i;
    {
        total := total + xs[i];
        i := i + 1;
    }
    r := total;
}
Here, the invariants ensure the partial sum is correct up to index i, and the decrease clause guarantees termination. verifies these properties statically, making it ideal for safety-critical software like OS kernels or cryptographic code, with no runtime overhead for verified code.

Applications

Documentation and Design

Loop invariants play a crucial role in by serving as explanatory comments that outline the persistent conditions of a loop, thereby clarifying its intended behavior for developers and reviewers. In or annotation systems like , these invariants can be expressed as inline remarks, such as noting that "the sum of elements from the start to the current index remains constant after each ," which helps convey the algorithm's logic without delving into details. As a aid, loop invariants enable refinement of loop constructs during the by allowing of their feasibility before begins. Developers can derive an appropriate invariant from the postcondition and initial state, ensuring it can be established at the loop's outset and maintained through each iteration while supporting progress toward completion; this systematic approach, often starting with the weakest suitable , streamlines the creation of robust loops. Incorporating loop invariants into design and documentation yields significant benefits, including improved code readability through explicit articulation of key assumptions, enhanced team collaboration by reducing ambiguity in shared codebases, and simplified maintenance by preserving insights into the loop's unchanging properties. These advantages promote long-term without relying on runtime mechanisms. Effective practices for employing loop invariants emphasize beginning with natural language descriptions to capture conceptual intent, such as "all processed elements are correctly partitioned," before formalizing them into logical expressions for precision. Invariants should be revisited and updated during refactoring to reflect modifications in loop behavior, ensuring their ongoing utility in design documentation.

Runtime Checking

Runtime checking of loop invariants involves dynamically evaluating these properties during program execution to detect violations that could indicate bugs or incorrect behavior. In languages supporting built-in contract mechanisms, such as Eiffel, loop invariants are checked automatically when assertion monitoring is enabled via compile-time flags; the checks occur after loop initialization and following each of the body to ensure the invariant holds throughout execution. Similarly, in , developers can insert assert statements at the end of the body to verify invariants post-iteration, with these assertions enabled or disabled at runtime using command-line flags like -ea. This approach offers significant advantages in , particularly for catching subtle errors early during testing and phases, where invariants serve as sentinels for maintaining consistency in complex data structures like graphs or that evolve across iterations. By providing immediate on invariant breaches, runtime checks facilitate quicker diagnosis and correction of issues that might otherwise propagate and cause failures in later program stages. However, runtime evaluation imposes a non-trivial performance overhead due to the repeated computation and testing of potentially expensive predicates within tight loops, which can slow execution by factors depending on invariant complexity and frequency of checks. To mitigate this, such mechanisms are conventionally disabled in production environments— for instance, Java's assert statements are off by default, and Eiffel's monitoring can be toggled off post-development—prioritizing efficiency over redundant verification in deployed software. Since 2015, advancements have enhanced runtime checking integration with development tools; for example, the E-ACSL plug-in for Frama-C generates efficient runtime monitors for C programs annotated with ACSL specifications, including loop invariants.

Formal Verification

Formal verification leverages loop invariants to mathematically prove the correctness of programs, particularly those containing loops, by establishing properties that hold throughout execution. This approach, rooted in deductive verification, enables exhaustive analysis without runtime execution, contrasting with testing methods. Developers annotate source code with loop invariants—assertions that remain true before the loop, after each iteration, and upon termination—alongside preconditions and postconditions. These annotations are then translated into logical verification conditions, which are checked by automated theorem provers or satisfiability modulo theories (SMT) solvers such as Z3. The solver determines whether the conditions are satisfiable under the program's semantics, confirming that the invariants preserve program properties like absence of errors or adherence to specifications. Key tools for this process include , developed by since 2010, which integrates loop invariants directly into its verification-aware language. In , invariants are specified in while loops to prove functional correctness, with Z3 discharging the resulting obligations to ensure termination and safety. , an extension of Ada for high-assurance software, uses GNATprove to verify invariants, automatically generating frame conditions for arrays and records while requiring manual invariants for complex loops to prove initialization, preservation, and post-loop effects. CBMC, a bounded model checker for C and C++, handles loops primarily through unrolling but incorporates invariants in k-induction modes to prove unbounded properties, reducing verification to queries solvable by Z3 or similar. The benefits of this verification are pronounced in safety-critical domains, providing machine-checked guarantees against failures. In , has verified autonomous systems like a Mars rover's safety monitor, proving loop behaviors to eliminate errors in remote-control modes. For blockchain smart contracts, tools like those for Move and use invariants to ensure loop termination and asset preservation, mitigating vulnerabilities in applications. These proofs enable certification compliance, such as for , by exhaustively covering infinite execution paths. Recent developments in the 2020s integrate to assist , addressing the challenge of manual . The theorem prover, enhanced by projects like LeanDojo and LeanCopilot, employs large language models for real-time proof suggestions and invariant generation in pipelines. Similarly, LLM-based rankers evaluate and refine auto-generated invariants for tools like , improving automation for complex loops in autonomous systems. These advancements fill gaps in verifying modern applications, such as in self-driving vehicles, by combining neural with symbolic provers.

Related Concepts

Distinction from Loop-Invariant Code

Loop-invariant code refers to computations or expressions within a body whose values remain constant across all iterations, allowing them to be relocated outside the loop to improve execution efficiency without altering program semantics. This optimization, known as (LICM), targets side-effect-free operations such as arithmetic expressions where operands are either constants or variables defined outside the loop. For instance, in a loop that iterates a fixed number of times, calculating the loop bound or a constant offset once before the loop avoids redundant evaluations. In contrast, a loop invariant in program verification is a or logical assertion about variables that holds true immediately before the loop begins, after each iteration, and upon termination, serving as a for proving overall correctness. While loop-invariant code focuses on syntactic restructuring for performance—identifying hoistable expressions based on —the logical loop invariant emphasizes behavioral guarantees, such as maintaining a partial in a , which is neither constant nor movable in the same way. This distinction arises because verification invariants capture abstract relations (true or false) that evolve predictably with loop progress, whereas invariant code targets unchanging computations detectable through control-flow and reaching definitions analysis. Consider a C example illustrating this difference: 
c
int n = 10;
int sum = 0;
for (int i = 0; i < n; i++) {
    int offset = n - 5;  // Loop-invariant code: constant value, hoistable for optimization
    sum += i;            // Maintains invariant: sum equals partial sum of 0 to i-1 before addition
}
Here, offset is loop-invariant code, as its value does not depend on i and can be computed once outside the loop by a compiler pass like LICM in LLVM, reducing overhead. Conversely, the property "sum is the sum of integers from 0 to i-1" is a logical loop invariant used in verification to ensure the final sum equals the total (0 to n-1), but it cannot be "hoisted" as code since it changes per iteration. The terms are often confused due to the shared "" nomenclature, yet one pertains to methodologies for correctness (as in ), while the other is a compiler optimization technique applied in tools like to eliminate redundancy. This overlap in terminology can mislead when discussing loop analysis, but clarifying the semantic (verification) versus syntactic (optimization) natures resolves the ambiguity.

Comparison to Other Invariants

Loop invariants differ from class invariants primarily in scope and application within . A is a property that must hold for all instances of a class throughout their lifetime, ensuring the of the object's state across all method executions, such as in Eiffel's design-by-contract paradigm where it is checked before and after routine calls. In contrast, a loop invariant is a condition specific to the iterations of a loop, holding true before the loop begins, after each iteration, and upon exit, focusing on algorithmic progress rather than overall object integrity. For example, in verifying sorting algorithms, a loop invariant might ensure that all processed elements are correctly ordered, while a provides a broader guarantee on the data structure's properties, often as a weaker of the loop invariant excluding partial states during computation. Preconditions and postconditions, foundational to , specify the state required upon entry to a () and the expected state upon exit (postcondition), enabling of the entire 's correctness. Loop s, however, operate intra-procedurally within loops, bridging the precondition to the postcondition by maintaining a persistent property across iterations; the precondition implies the initial , the body preserves it when the loop guard holds, and the combined with the negated guard implies the postcondition. This distinction ensures that while preconditions and postconditions frame the overall computation, loop s address the inductive step for repetitive structures. Loop variants complement loop invariants by addressing termination rather than preservation. A loop variant is a non-negative quantity that strictly decreases with each iteration, proving that the loop cannot run indefinitely and thus establishing total correctness when paired with an invariant for partial correctness. Unlike invariants, which remain unchanged to guarantee state consistency (e.g., a sum accumulator equaling the partial result), variants measure progress without preserving the same property, such as a counter decrementing toward zero. These concepts overlap in advanced verification: loop invariants within object methods can strengthen or imply class invariants by ensuring that iterative operations respect broader object constraints, while variants ensure such loops terminate without violating the class state. In formal systems like extended to objects, this integration allows loop invariants to contribute to overall program proofs without conflating their iterative focus with structural or entry/exit assertions.