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An arithmetic series adds the sequence terms of an arithmetic progression. Its $n$-th partial sum is $S_n={\sum }_{k=1}^n{a}_k$, with the empty sum convention $S_0=0$. For $n\ge 1$,Thus the total is the number of sequence terms multiplied by the arithmetic mean of the two endpoints. The formula also covers decreasing and constant arithmetic progressions.^[ref]

For the eight-entry example, the sum is $8(7+28)/2=140$, and the arithmetic mean of all eight entries is $17.5$. A decreasing example $11,9,7,5,3,1$ gives $6(11+1)/2=36$. Negative values cause no algebraic difficulty: $3,1,-1,-3,-5$ has sum $5(3-5)/2=-5$.

The triangular number $T_n=1+2+\cdots +n$ is the special case $a_1=d=1$, giving $n(n+1)/2$. More generally, an equal increase in each successive allocation makes the total grow as a quadratic function of the number of allocations. The eight-entry example hasIts first four entries add to $46$, its first eight to $140$, and its first twelve to $282$. The sequence terms lie on a straight line when plotted against their sequence indices, while the partial sums follow a quadratic function.

Keep an array $A$ unchanged, and sum entries in the half-open integer interval of array indices $[l,r)$. The precondition requires integers $0\le l\le r\le \mathrm{length}(A)$ and exact, terminating integer addition. An empty range returns the empty sum zero; a one-entry range returns that entry. Every longer range splits at its middle array index:For the original array $A=[6,-2,5,1]$, call $S(A,0,4)$. The left half returns $6+(-2)=4$, the right half returns $5+1=6$, and the original invocation returns $4+6=10$. The right half's left endpoint increases from zero to two: what decreases is range length $r-l$, not every argument. Negative entries affect the answer but do not affect algorithm termination.

For range length $n=r-l\ge 2$, the children have lengths $\lfloor n/2\rfloor$ and $\lceil n/2\rceil$, both positive and strictly less than $n$. The floor function chooses the split; the ceiling function gives the other length. Algorithm termination follows by this natural number measure. For algorithm correctness, apply mathematical induction on $n$: the two base cases return the specified sum; for a larger range, the two disjoint child ranges cover every original array index exactly once, so addition of their correct sums gives the required sum.

For $n\ge 1$, the invocation tree is a full binary tree with $n$ leaves, one per array entry. If $I$ is its number of nonleaf vertices, counting edges gives $2I=I+n-1$, so $I=n-1$. Thus the total invocation count is $2n-1$, and there are $n-1$ combining additions. Here $n=4$ gives seven calls and three combining additions. For $n=1$, there is one call and no combining addition; for $n=0$, there is also one call and no combining addition, but no entry leaf, so the $2n-1$ formula does not apply.

The call stack holds the active branch, rather than the entire invocation tree. Execute the left child completely before the right child, retaining the left result in the parent stack frame. The maximum number of active stack frames is $1+\lceil {\log }_{2}n\rceil$ for $n\ge 1$, and one for $n=0$. Balanced splitting therefore needs $O(\log n)$ frames for $n\ge 2$, compared with the $O(n)$ frames of the factorial recursive process. Assuming constant-cost array access, index operations and addition, its time complexity is $O(n)$ and its auxiliary space complexity is $O(\log n)$ machine words for $n\ge 2$, excluding the stored input. Pass the same array with different endpoints, rather than copying slices. These bounds require all stored indices and partial sums to fit in a constant number of machine words; exact unbounded integer addition and storage require a separate bit cost analysis.

For $n\ge 0$ sorting records with integer sort keys in $\{0,\dots ,K-1\}$, where $K\ge 1$, let $c_k$ count the input occurrences of sort key $k$. Define two prefix sums:The empty sum gives $S_0=0$. Here $c_k,S_k,P_k$ are counts of sorting records. Exactly $S_k$ sorting records have smaller sort keys, so the output positions for sort key $k$ form the half-open integer intervalIts right endpoint $P_k$ is excluded: the last occupied array index is $P_k-1$ when $c_k>0$.^[ref]

For the seven-sorting record example, the frequency array, inclusive cumulative count array, and start array areThus sort keys zero, one, two and three occupy the integer intervals $[0,2)$, $[2,3)$, $[3,6)$ and $[6,7)$, respectively. In particular, the three sorting records with sort key two belong at array indices three, four and five. The inclusive cumulative count $P_2=6$ counts all six sorting records with sort keys at most two; six is the exclusive endpoint, not the final position of a sorting record with sort key two.

These integer intervals fit together because $P_k=S_{k+1}$ for $k<K-1$, and $P_{K-1}=n$. Their lengths $P_k-S_k=c_k$ add to $n$, so they partition all output positions without overlaps or gaps. An absent sort key contributes an empty integer interval: with frequencies $(0,2,0,1)$, the endpoints are $(0,2,2,3)$ and sort key two has range $[2,2)$. For empty input, every count and endpoint is zero; every reserved integer interval is empty. This makes the same formulas cover missing sort keys and the empty case.

An empty sum has no terms and is defined to be zero, the identity element for addition. This keeps the rule for combining sums valid when one index set is empty. In a Galton-Watson process, an empty generation contributes an empty sum of offspring counts, so the next generation is empty too. The corresponding convention for multiplication is the empty product, which equals one.

A Galton-Watson process is a branching process that counts individuals by generation. Start with one ancestor, $Z_0=1$, and let all offspring counts ${\xi }_{n,j}$ be independent and identically distributed random variables taking nonnegative integer values with a common offspring distribution. ThenThe empty sum is zero: zero is an absorbing state, so once a generation is empty every later generation is empty. The same offspring distribution applies to every individual in every generation. ^[ref]

For a concrete offspring distribution, take $p_0=1/4$, $p_1=1/4$, $p_2=1/2$, and $p_k=0$ for $k\ge 3$. An individual has no children with probability $1/4$, one with probability $1/4$, and two with probability $1/2$. The offspring expected value is $\mu =5/4$. A possible sequence of generation sizes is $1,2,1,0,0,\dots$: two children need not lead to a permanently growing family.

The probability generating function $F(s)=1/4+s/4+s^2/2$ gives the exact generation probability distributions through branching-process generating-function iteration. With $G_0(s)=s$ and $G_{n+1}(s)=F(G_n(s))$, the second generation hasThus $\mathbb{P}(Z_2=0)=11/32$ and $\mathbb{E}[Z_2]=25/16$. The figure shows full generation probability mass functions, computed by polynomial function composition, through generation six; no simulated frequencies are used.

The contrast is striking: the expected generation size in a Galton-Watson process grows as $(5/4{)}^n$, while the branching extinction probability is $1/2$. By generation six, $\mathbb{P}(Z_6=0)\approx 0.462186$ although $\mathbb{E}[Z_6]\approx 3.814697$. Larger surviving families carry the rising expected value; that mean is an average over possible families, not a forecast of one family's path. The Galton-Watson extinction fixed point turns an unbounded number of future generations into a single equation for this probability.

The historical question was the disappearance of surnames passed through male descendants. In Watson and Galton, On the Probability of the Extinction of Families, pages 139–140, Francis Galton formulates a constant-law model counting sons who reach adulthood, and Watson computes later generation probability distributions by composing generating functions. Those are assumptions of their model, rather than a claim that actual human reproduction follows this offspring distribution.

First take $1\le n\le F-1$. The forward object reaches $n$ at its $n$th move, while the reverse object reaches that label at its $(F-n)$th move. If the forward object wins after $k$ reverse moves, then $0\le k\le F-n-1$. The final choice is forward; among the preceding $n+k-1$ choices, exactly $k$ are reverse. By the negative binomial stopping argument, this case has probability $\left(\genfrac{}{}{0ex}{}{n-1+k}{k}\right)p^n{q}^k$.

Sum these mutually exclusive cases and equate the result to part (e), using parts (b) and (d). This proves the success-failure stopping identity here:The right side splits the same race into a special meeting at $n$ and all meetings beyond $n$; the left side splits it according to the number of reverse moves at the winning time.

At $n=F$, both sides are zero with an empty sum and $\left(\genfrac{}{}{0ex}{}{F-1}{F}\right)=0$. At $n=0$, the event has probability one; the right side equals ${\sum }_{k=0}^F\left(\genfrac{}{}{0ex}{}{F}{k}\right)p^k{q}^{F-k}=1$. The printed left side includes $\left(\genfrac{}{}{0ex}{}{-1}{0}\right)$ and is outside the usual nonnegative-upper-index combinatorial definition. With the explicit generalized convention $\left(\genfrac{}{}{0ex}{}{-1}{0}\right)=1$, its $k=0$ term is one and all $k\ge 1$ terms vanish, so the identity also holds at that endpoint. Without this convention the displayed sum is undefined there; the zero-target race must instead be stated separately. All identities extend to $p=0,1$ by their polynomial forms.

Additivity of the definite integral combines the adjacent intervals into $[0,n]$. For an integer $n\ge 1$, the power rule givesFor $n=0$, interpret the indexed sum as an empty sum, equal to zero; the displayed expression also gives zero. This convention resolves the otherwise degenerate endpoint of the stated nonnegative-integer range.

Use the measurable partition $B_n=\{N=n\}$ for $n\ge 0$, omitting any null cells. For $0\le s\le 1$, the random variable $s^X$ is bounded, so the law of total expectation applies without moment assumptions. Conditional on $N=n$, the sum contains exactly $n$ terms. Independence of $N$ from the whole sequence preserves their joint law under this conditioning; independence of the $Y_k$ then givesFor $n=0$ the empty sum is zero and the empty product is one, in agreement with the formula. Therefore the probability generating function of an independent random sum is

Take an independent and identically distributed sequence with $X_i\sim \mathrm{Bernoulli}(r)$, independent of $N$. Conditional on $N=n$, the random sum ${\sum }_{i=1}^n{X}_i$ has binomial distribution with parameters $n,r$, including the empty sum at $n=0$. This construction therefore has exactly the prescribed joint law of $(N,Z)$ and represents $Z$ as the required random sum.

For a literal representation that retains an already given pair $(N,Z)$, enlarge the probability space by auxiliary randomness. Conditional on $N=n,Z=z$, choose uniformly a size-$z$ subset of $\{1,\dots ,n\}$, put ones at those indices and zeros at the others, and append independent Bernoulli variables of parameter $r$. Given $N=n$, a specified length-$n$ pattern with $z$ ones has probability $\left(\genfrac{}{}{0ex}{}{n}{z}\right)r^z(1-r{)}^{n-z}/(\genfrac{}{}{0ex}{}{n}{z})=r^z(1-r{)}^{n-z}$. Thus the entire sequence is independent and identically distributed and independent of $N$, while its first $N$ terms sum to the original $Z$.

A prefix sum stores the accumulated sum through a specified position in a finite sequence or array. For inputs $a_1,\dots ,a_n$, include the empty sum $P_0=0$ and setFor the original input $(4,-2,5,-1,3,-4)$, the prefix array including $P_0$ is $(0,4,2,7,6,9,5)$. A negative entry makes the accumulated sum decrease; a prefix sum does not have to be monotone. Conversely, the original entry is recovered by subtraction: $a_m=P_m-P_{m-1}$.

The useful cancellation is that any contiguous block has sumBoth prefixes include the entries before $\ell$; subtracting cancels those entries. In the example, the sum from positions two through four is $-2+5-1=2$, recovered as $P_4-P_1=6-4$. The arithmetic mean of that block is $2/3$, obtained by dividing its sum by its length. Prefix sums therefore make many repeated questions about an unchanged array cheap: reuse accumulated totals instead of resumming every block.

The sequential recurrence builds all $n+1$ prefixes in $O(n)$ time complexity and storage; each block query needs two reads and one subtraction, hence $O(1)$ time complexity. These counts assume constant-cost arithmetic and access to array entries. If one input changes, all later stored prefixes change, so this simple representation is most useful when queries outnumber updates. For integer inputs, sufficient numeric capacity must be retained; with floating-point arithmetic, subtracting large nearby prefixes can lose relative accuracy for a small block total. The exact mathematical identity does not eliminate representation error.

The apparently sequential recurrence also motivates a prefix scan: associativity permits a tree of partial calculations that yields all accumulated results in parallel. Guy E. Blelloch's 1990 technical report systematically develops this operation and its applications; its authored chapter version explains the two tree sweeps used to obtain all prefixes. ^[ref]. ^[ref].

The prime omega function $\omega (n)$ counts the distinct prime factors of a positive integer $n$, so it is the cardinality of the prime support of an integer $P(n)$. For a prime factorization with distinct prime numbers $p_1,\dots ,p_r$ and positive exponents,At $n=1$ there are no prime factors, so the empty sum gives $\omega (1)=0$. The total number of prime factors instead gives $\Omega (n)={\sum }_i{a}_i$. ^[ref]

For $72=2^3\cdot 3^2$, the prime support of an integer is $\{2,3\}$ and $\omega (72)=2$. The comparison examples give $\omega (30)=3$, $\omega (32)=1$ and $\omega (36)=2$. Raising an existing prime factor to a higher power does not affect the prime omega function: $\omega (p^k)=1$ for a prime number $p$ and $k\ge 1$. Thus the prime omega function is a strongly additive arithmetic function, withThe reason is that coprime integers have disjoint prime supports of an integer. For arbitrary positive integers, their prime support of an integer combines by set union, and the shared prime factors are exactly those of the greatest common divisor. Counting this set union gives the more general identityFor $a=12$, $b=18$, both prime supports of an integer are $\{2,3\}$ and $\gcd (a,b)=6$, so $\omega (216)=2+2-2=2$. In particular $\omega (4)=1\ne 2=\omega (2)+\omega (2)$: the prime omega function is not a completely additive arithmetic function.

The prime omega function is useful for counting choices involving distinct prime factors. Every squarefree divisor of $n$ chooses a subset of its prime support of an integer, so there are $2^{\omega (n)}$ squarefree divisors. For $72$, this gives $2^2=4$, namely $1,2,3,6$. Also $\omega (\mathrm{rad}(n))=\omega (n)$, because the radical of an integer keeps exactly that prime support of an integer. The total number of prime factors records what this count discards, and the two counts agree exactly on squarefree integers.

The proper-divisor sum, or aliquot sum, adds the positive proper divisors:In particular $s(1)=0$, an empty sum. Removing $n$ from the sum-of-divisors function is essential: $\sigma (6)=12$, but its proper-divisor sum is $6$.

In independent Bernoulli trials with success probability $p$ and failure probability $q=1-p$, let $T_n$ be the trial of success $n$ and $U_m$ the trial of failure $m$, for positive integers $n,m$. If success $n$ occurs after $k<m$ failures, its final trial is a success and the preceding trials have $n-1$ successes and $k$ failures. The negative binomial stopping argument therefore gives the title sum.

Put $F=n+m$. By trial $F-1$, at least one target must have been reached, so the same event is that a binomial distribution with $F-1$ trials has at least $n$ successes. Conditioning instead on the final trial of a block of $F$ trials givesIndeed, more than $n$ successes in the full block guarantee the success target won; with exactly $n$ successes it won only when the final trial was a failure. These disjoint cases explain the extra boundary term. A zero success target wins immediately; a zero failure target has already been reached. To include $n=0$ in the displayed finite sum requires the explicit generalized convention $\left(\genfrac{}{}{0ex}{}{-1}{0}\right)=1$; an upper summation limit of $-1$ denotes an empty sum.

Compute $1+2+\cdots +n$, the triangular number $T_n$, for a nonnegative integer $n$. The empty sum gives zero when $n=0$. Use exact mathematical integers and keep the input $n$ unchanged; every comparison and variable assignment is a terminating operation. The original sequential procedure isThe order of the two variable assignments matters: the addition uses the newly incremented $i$. For $n=4$, the pairs $(i,s)$ at successive condition checks are $(0,0),(1,1),(2,3),(3,6),(4,10)$. At the last check, $i<n$ is false, and the returned sum is $10$.

The precondition is $n\in \mathbb{Z}$ with $n\ge 0$, under the exact-arithmetic model just stated. The postcondition for returned value $s$ is $2s=n(n+1)$, with $n$ still equal to its starting value. At each condition check use the loop invariantand the loop variant $V=n-i$. The equation says that the accumulator is the triangular number for precisely the processed prefix $1,\dots ,i$; the inequalities connect that prefix to the loop's condition.

Initialization gives $i=s=0$, so $I$ holds, including when $n=0$. For preservation, let the old values satisfy $I$ and $i<n$. Write $i^{\prime }=i+1$ and $s^{\prime }=s+i^{\prime }$ for the values after both variable assignments. Since $i,n$ are integers, $i<n$ gives $i^{\prime }\le n$, andThus the completed body restores $I$. Between the two variable assignments, the equation can be false; the loop invariant is required at the loop head. This preservation step is mathematical induction on the number of completed iterations.

At exit, the false condition gives $i\ge n$, while $I$ gives $i\le n$. Hence $i=n$, so $2s=n(n+1)$: this proves partial correctness. For algorithm termination, $I$ gives the nonnegative integer $V=n-i$. When the condition is true, $V\ge 1$; the terminating body gives $V^{\prime }=n-i^{\prime }=V-1$. Therefore the loop cannot repeat forever. Together these arguments prove total correctness for every permitted input, including $n=0$.

Two changes expose what the proof checks. Swapping the two body instructions computes $0+1+\cdots +(n-1)$, returning $6$ rather than $10$ for $n=4$. Leaving $i$ unchanged can preserve an initial equation forever without producing any answer. The first change breaks the intended postcondition; the second breaks algorithm termination. A trace can reveal these failures, while the two proof obligations explain them.

The exact-arithmetic assumption has a concrete boundary. In a stipulated unsigned eight-bit model where each addition wraps modulo $256$, $n=23$ fits in the input representation, but its intended triangular number is $276$. The stored accumulator instead finishes at $20$, so the mathematical postcondition fails. This is integer overflow, and the proof above does not apply to that altered model. Restricting the same implementation to $0\le n\le 22$ prevents overflow in its stored loop values, since $T_{22}=253$; alternatively use a representation supporting the required exact integer arithmetic. The equations in the proof are mathematical statements, not instructions to evaluate products in eight bits.

The total number of prime factors $\Omega (n)$ counts every occurrence of a prime factor in the prime factorization of a positive integer $n$. Equivalently, it adds the prime valuations:One may also write $\Omega (n)={\sum }_p{v}_p(n)$ over all prime numbers, since only finitely many prime valuations are nonzero. The empty sum gives $\Omega (1)=0$. By contrast, the prime omega function has $\omega (n)=r$ and counts distinct prime factors. ^[ref]

Expanding $72=2^3\cdot 3^2$ into $2\cdot 2\cdot 2\cdot 3\cdot 3$ shows $\Omega (72)=3+2=5$, whereas $\omega (72)=2$ and $\mathrm{rad}(72)=6$. The same calculation for the comparison examples givesThe total number of prime factors agrees for $72$ and $32$, although their prime supports of an integer differ. Meanwhile $72$ and $36$ have the same radical of an integer and prime omega function, but different values of the total number of prime factors. These examples show exactly which information each arithmetic function forgets.

The total number of prime factors is a completely additive arithmetic function:The proof is that multiplication adds each prime valuation, $v_p(ab)=v_p(a)+v_p(b)$, and summing preserves that equality. No coprimality condition is needed. For example, $12=2^2\cdot 3$ and $18=2\cdot 3^2$ both have total number of prime factors three; their product $216=2^3\cdot 3^3$ has six. By comparison, the prime omega function is only an additive arithmetic function: here $\omega (216)=2\ne 4=\omega (12)+\omega (18)$.

There is a useful way to see the total number of prime factors as a count of elementary building blocks: any expression of $n$ as a product of prime numbers has exactly $\Omega (n)$ factors, by the fundamental theorem of arithmetic. Equivalently, every factorization tree for $n>1$ has $\Omega (n)$ leaves. Repeated prime factors increase this count without introducing new kinds of prime number. The difference is the existing excess prime-factor multiplicity:Every summand is nonnegative, so $\Omega (n)=\omega (n)$ if and only if $n$ is a squarefree integer, including $n=1$. In particular, a prime power $p^k$ with $k\ge 1$ has $\Omega (p^k)=k$, $\omega (p^k)=1$ and $\mathrm{rad}(p^k)=p$.

Historically, Hardy and Ramanujan's 1917 paper explicitly separated the prime omega function from the total number of prime factors, writing them as $f(n)$ and $F(n)$, respectively. Their example $2^3\cdot 3^2\cdot 5$ has counts three and six. That distinction let them compare two measures of how an integer is built from prime factors. ^[ref]

A triangular number counts rows of $1,2,\dots ,n$ objects, with $n$ a nonnegative integer:The first values are $0,1,3,6,10,15,21,28,36,45,\dots$. The empty arrangement contributes zero, by the empty sum convention. Adding one more row gives the triangular number recurrence $T_n-T_{n-1}=n$ for $n\ge 1$. The row lengths form an arithmetic progression, while their running totals are triangular numbers.

Pair the rows with a reversed copy: $1+n=2+(n-1)=\cdots =n+1$. There are $n$ such pairs, so two copies contain $n(n+1)$ dots. Dividing by two proves the formula. This rectangle picture explains the factor one-half without requiring a formula for a long summation. For example, one hundred rows contain $T_{100}=5050$ dots.

The same count appears whenever every pair of different objects must be considered once. Five participants make ten possible handshakes; five sensors have ten different pairwise comparisons; a complete graph on five vertices has ten edges. These are the triangular number pair-counting identity, with one fewer row than objects. The distinction prevents a common error: $T_5=15$ counts all pairs from six objects, whereas five objects give $T_4=10$.

The triangular number square-complement identity says that two neighboring triangular numbers fill a square. Combining that identity with the one-row difference makes the difference of squares $T_n^2-T_{n-1}^2$ equal to $n^3$. It leads to Nicomachus's theorem: the sum of the first $n$ cubes of numbers is the square of a number $T_n^2$. A change in the way one groups objects exposes a relation between summation, multiplication and geometry.

This geometric view is already visible in Nicomachus's Introduction to Arithmetic by Nicomachus (Hahn teacher edition): Book II, Chapter 8 describes growing triangular rows, Chapter 12 combines successive triangular counts into squares, and Chapter 20 groups successive odd integers into cubes of numbers.^[ref] The linked edition uses Martin Luther D'Ooge's translation, revised with modern teaching material by Nathanael Hahn. Our empty-row index $T_0=0$ is a modern convention; the historical passage starts from unity and calls three the first actual triangle.