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Partial correctness means that, if the algorithm terminates on a permitted input, its output meets the formal specification. Total correctness additionally requires algorithm termination on every permitted input.

A precondition $P$ describes permitted starting circumstances; a postcondition $Q$ describes the required result. Partial correctness says that starting with $P$ and finishing implies $Q$. Total correctness also guarantees finishing. A loop invariant supplies the connection between these conditions during repeated steps.^[ref]

Correctness is relative to a formal specification. Returning the first entry of an array correctly implements "return the first entry", but fails "return the maximum" on $[3,1,4,4,2]$. A fast wrong answer does not acquire algorithm correctness from its time complexity. Numerical closeness matters only when the formal specification allows an approximation. Software testing and test coverage provide evidence about selected executions; they do not by themselves establish algorithm correctness for every permitted input.

A statement true whenever execution reaches a chosen point in a loop, usually immediately before its condition is tested. Establish it initially, preserve it through each iteration, and combine it with the exit condition to establish algorithm correctness. Termination needs a separate argument.^[ref]

Write $I$ for the loop invariant, $B$ for the condition, $P$ for the precondition, and $Q$ for the postcondition. Prove that initialization under $P$ establishes $I$, that a completed body starting with $I$ and $B$ restores $I$, and that $I$ with false $B$ implies $Q$.^[ref]

In the prefix maximum correctness example, the loop invariant remembers exactly the maximum of the processed prefix and the valid index bounds. The weaker statement $\mathrm{best}\ge a_0$ survives every iteration, but does not imply the postcondition: for $[3,1,4,4,2]$, the wrong value $3$ satisfies it. A useful loop invariant retains enough information to recover the answer when the condition becomes false.

A decreasing measure used to prove algorithm termination. A nonnegative integer loop variant must strictly decrease after every completed body whose condition was true; the body itself must terminate.^[ref]

The prefix maximum correctness example uses $V=n-k$. It counts unread entries and is independent of whether the new entry changes the maximum. Ties therefore cannot stall progress: $k$ increases even when $\mathrm{best}$ stays the same. An integer bounded below cannot strictly decrease indefinitely, which supplies the missing algorithm termination argument.

Strict decrease and a lower bound are insufficient for arbitrary real numbers. The positive sequence $1,1/2,1/4,\dots$ strictly decreases and is bounded below by zero, but never reaches zero. Choosing an integer measure avoids this failure.