Refinement and Monotonicity of Darboux Sums

Suppose you approximate the area under a curve using rectangles. Then you slice those rectangles into thinner ones. You do this by adding more division points. Does your estimate of area from above get better — that is, smaller? Does your estimate from below get better by growing larger? This is the intuitive question behind one of the load-bearing lemmas of Riemann integration theory. It sounds obvious from a picture. But a rigorous course cannot rest on pictures. We need an airtight argument. It must show that adding points to a partition can only push upper sums down. It can only push lower sums up, never the wrong way. This fact — refinement monotonicity — has an important corollary. Every lower sum is dominated by every upper sum. This holds even for sums built from different partitions. This is what makes the definitions of the upper and lower Riemann integral coherent in the first place. Without it, things could go wrong. The "infimum of upper sums" and "supremum of lower sums" used to define the Darboux integral could behave erratically. There would be no guarantee that the upper integral even exceeds the lower integral. This single inequality is the hinge on which the entire theory of Riemann integrability turns. The big picture: Darboux sums are built from partitions. Partitions form a directed structure under inclusion. You can always combine two partitions into a common refinement. The lemma proved here shows something important. The map "partition ↦ upper sum" is order-reversing. The map "partition ↦ lower sum" is order-preserving with respect to this structure. Combined with boundedness of the function, this guarantees something. The upper integral (an infimum) and lower integral (a supremum) exist as real numbers. It also guarantees that the former is at least the latter. This article gives the precise definitions. It states and proves the refinement theorem and its corollary in full. It works two asymmetric numerical examples. It exhibits what fails without boundedness. It previews how this machinery feeds directly into the definition of The Darboux Integral.

Key takeaways
  • A partition PP of [a,b][a,b] cuts it into finitely many subintervals; a refinement P∗P^* adds more points.
  • Upper sums U(f,P)U(f,P) use suprema of ff on each subinterval; lower sums L(f,P)L(f,P) use infima.
  • Refining decreases upper sums: P⊆P∗  ⟹  U(f,P∗)≤U(f,P)P \subseteq P^* \implies U(f,P^*) \le U(f,P).
  • Refining increases lower sums: P⊆P∗  ⟹  L(f,P∗)≥L(f,P)P \subseteq P^* \implies L(f,P^*) \ge L(f,P).
  • Any lower sum ≤\le any upper sum, even for different partitions P1,P2P_1, P_2, via the common refinement P1∪P2P_1 \cup P_2.
  • Key idea: adding one point to a partition only makes the local sup/inf boxes tighter, and induction extends this from one point to arbitrarily many.

Where this lives today

The refinement monotonicity lemma is not an isolated curiosity — it is the engine behind several downstream results students meet immediately after. It is what allows one to define the upper and lower integrals as a genuine infimum and supremum (rather than an ill-defined limit), which feeds directly into The Darboux Integral and the Riemann Integrability Criterion (a bounded function is integrable iff for every ε>0\varepsilon > 0 there is a partition with U(f,P)−L(f,P)<εU(f,P) - L(f,P) < \varepsilon — a statement that presupposes refining never makes the gap worse). In numerical analysis, the same monotonicity principle underlies adaptive quadrature methods, where a numerical integration routine refines a mesh locally and relies on the guarantee that finer meshes give provably tighter error bounds. In measure theory, the analogous monotonicity of upper and lower sums under refinement of a partition generalizes to monotonicity of approximating sums under refining a measurable partition, a recurring pattern in the construction of the Lebesgue integral and in probability theory's treatment of conditional expectation with respect to refining σ\sigma-algebras (filtrations). The order-theoretic idea — that a directed family of partitions under inclusion produces monotone approximating quantities — also appears in the theory of nets and Riemann–Stieltjes sums, and in computer science, in the analysis of divide-and-conquer numerical integration algorithms where correctness proofs invoke exactly this "refining never hurts" guarantee.

:::quiz[If P⊆P∗P \subseteq P^* are partitions of a bounded function ff on [a,b][a,b], which inequality is always true?]

  • U(f,P)≤U(f,P∗)U(f,P) \le U(f,P^*)
  • L(f,P∗)≤L(f,P)L(f,P^*) \le L(f,P)
  • U(f,P∗)≤U(f,P)U(f,P^*) \le U(f,P) ✓
  • L(f,P)=L(f,P∗)L(f,P) = L(f,P^*) always

:::quiz[Given two partitions P1,P2P_1, P_2 of [a,b][a,b] that are not refinements of each other, how do we prove L(f,P1)≤U(f,P2)L(f,P_1) \le U(f,P_2)?]

  • We assume it directly from the definition of infimum and supremum
  • We compare corresponding subintervals of P1P_1 and P2P_2 term by term
  • We use the common refinement P1∪P2P_1 \cup P_2 and chain inequalities through it ✓
  • We require P1P_1 and P2P_2 to have the same number of points

:::quiz[What role does boundedness of ff on [a,b][a,b] play in the theorem?]

  • It ensures the partition points are rational numbers
  • It ensures each Mi=sup⁡IifM_i = \sup_{I_i} f and mi=inf⁡Iifm_i = \inf_{I_i} f is a finite real number so sums are well-defined ✓
  • It ensures ff is monotonic on each subinterval
  • It has no logical role; it is stated only for convention

Ask the tutor: How does this monotonicity lemma get used to prove the Riemann integrability criterion involving U(f,P)−L(f,P)<εU(f,P) - L(f,P) < \varepsilon?

DefinitionPartitions, Refinements, and Darboux Sums

Let f:[a,b]→Rf:[a,b]\to\mathbb{R} be a bounded function on a closed bounded interval [a,b][a,b], a<ba<b. A partition of [a,b][a,b] is a finite set of points

P={a=x0<x1<x2<⋯<xn=b}.P = \{a = x_0 < x_1 < x_2 < \cdots < x_n = b\}.

It divides [a,b][a,b] into subintervals Ii=[xi−1,xi]I_i = [x_{i-1}, x_i] for i=1,…,ni = 1,\dots,n, with lengths Δxi=xi−xi−1\Delta x_i = x_i - x_{i-1}. For each subinterval, since ff is bounded, define

Mi=sup⁡x∈Iif(x),mi=inf⁡x∈Iif(x).M_i = \sup_{x \in I_i} f(x), \qquad m_i = \inf_{x \in I_i} f(x).

These are finite real numbers because ff is bounded on [a,b][a,b], hence on each IiI_i. The upper Darboux sum and lower Darboux sum of ff with respect to PP are

U(f,P)=∑i=1nMi Δxi,L(f,P)=∑i=1nmi Δxi.U(f,P) = \sum_{i=1}^n M_i \, \Delta x_i, \qquad L(f,P) = \sum_{i=1}^n m_i \, \Delta x_i.

A partition P∗P^* is a refinement of PP if P⊆P∗P \subseteq P^* (every point of PP is a point of P∗P^*; P∗P^* may contain additional points). Given two partitions P1,P2P_1, P_2 of [a,b][a,b], their common refinement is P1∪P2P_1 \cup P_2, which is itself a partition of [a,b][a,b] and refines both P1P_1 and P2P_2.

Intuition

Picture the graph of a bounded function over [a,b][a,b], with a partition drawn as vertical grid lines. Over each subinterval, the upper sum uses the tallest rectangle that still fits under (or touches) the graph's ceiling, and the lower sum uses the shortest rectangle that still reaches the graph's floor. Now insert one new grid line inside some subinterval Ii=[xi−1,xi]I_i = [x_{i-1}, x_i], splitting it into two pieces [xi−1,c][x_{i-1}, c] and [c,xi][c, x_i]. The supremum of ff over each of the two smaller pieces can only be less than or equal to the supremum over the whole of IiI_i, because each smaller piece is a subset of IiI_i and taking the sup over a smaller set never increases it. So the single wide rectangle of height MiM_i is replaced by two narrower rectangles whose heights are each at most MiM_i — the total upper area can only shrink or stay the same. Symmetrically, infima over subsets only increase, so lower sums only grow. Once this "one new point" step is justified, refining by finitely many points is just repeating the step, which is where induction enters. The final piece — that any lower sum is at most any upper sum, even across unrelated partitions — follows by comparing both to the sums for their common refinement, which sits above both partitions in the refinement order and lets us chain the inequalities together.

Figure
Refining a partition subdivides one rectangle into two, and both new heights are squeezed inside the old one
x y c x_{i-1} x_i M_i (before) new sups ≤ M_i
TheoremRefinement Monotonicity of Darboux Sums

Let f:[a,b]→Rf:[a,b]\to\mathbb{R} be bounded, and let PP and P∗P^* be partitions of [a,b][a,b] with P⊆P∗P \subseteq P^* (so P∗P^* is a refinement of PP). Then

L(f,P)≤L(f,P∗)≤U(f,P∗)≤U(f,P).L(f,P) \le L(f,P^*) \le U(f,P^*) \le U(f,P).

Consequently, for any two partitions P1,P2P_1, P_2 of [a,b][a,b] (not necessarily related by refinement),

L(f,P1)≤U(f,P2).L(f,P_1) \le U(f,P_2).
Proof

We prove the theorem in stages. Throughout, ff is bounded on [a,b][a,b], so all suprema and infima appearing below are finite real numbers (a bounded set of reals has a finite sup and finite inf, by the completeness of R\mathbb{R}).

Step 0: for any single partition PP, L(f,P)≤U(f,P)L(f,P) \le U(f,P). Write P={x0<⋯<xn}P = \{x_0 < \cdots < x_n\}, and for each ii let mi=inf⁡Iifm_i = \inf_{I_i} f, Mi=sup⁡IifM_i = \sup_{I_i} f on Ii=[xi−1,xi]I_i = [x_{i-1},x_i]. Since IiI_i is nonempty, mi≤Mim_i \le M_i (the infimum of a set of real numbers is always ≤\le its supremum, when both exist and the set is nonempty — pick any point t∈Iit \in I_i; then mi≤f(t)≤Mim_i \le f(t) \le M_i). Since Δxi=xi−xi−1>0\Delta x_i = x_i - x_{i-1} > 0,

miΔxi≤MiΔxifor each i,m_i \Delta x_i \le M_i \Delta x_i \quad \text{for each } i,

and summing over i=1,…,ni = 1,\dots,n gives L(f,P)≤U(f,P)L(f,P) \le U(f,P).

Step 1: the one-point refinement lemma. Suppose P∗=P∪{c}P^* = P \cup \{c\} where c∉Pc \notin P is a single new point. We claim

L(f,P)≤L(f,P∗),U(f,P∗)≤U(f,P).L(f,P) \le L(f,P^*), \qquad U(f,P^*) \le U(f,P).

Since c∉Pc \notin P, it lies strictly inside exactly one subinterval of PP, say c∈(xk−1,xk)c \in (x_{k-1}, x_k) for some kk. All other subintervals of PP are unchanged in P∗P^*; the only difference between the sums is in the terms coming from Ik=[xk−1,xk]I_k = [x_{k-1},x_k], which P∗P^* splits into

Ik′=[xk−1,c],Ik′′=[c,xk].I_k' = [x_{k-1}, c], \qquad I_k'' = [c, x_k].

Let

Mk=sup⁡Ikf,Mk′=sup⁡Ik′f,Mk′′=sup⁡Ik′′f,M_k = \sup_{I_k} f,\quad M_k' = \sup_{I_k'} f,\quad M_k'' = \sup_{I_k''} f,

and similarly mk,mk′,mk′′m_k, m_k', m_k'' for infima. Since Ik′⊆IkI_k' \subseteq I_k and Ik′′⊆IkI_k'' \subseteq I_k, and the supremum of ff over a subset of IkI_k cannot exceed the supremum of ff over IkI_k itself (any upper bound for ff on IkI_k is also an upper bound for ff on the smaller set), we get

Mk′≤Mk,Mk′′≤Mk.M_k' \le M_k, \qquad M_k'' \le M_k.

Similarly, the infimum over a subset cannot be smaller than the infimum over the whole set (any lower bound for ff on IkI_k is also a lower bound on the subset), so

mk′≥mk,mk′′≥mk.m_k' \ge m_k, \qquad m_k'' \ge m_k.

Now compare the sums directly. The upper sum for PP contributes MkΔxk=Mk(c−xk−1)+Mk(xk−c)M_k \Delta x_k = M_k(c - x_{k-1}) + M_k(x_k - c) to the total from IkI_k (just splitting the length additively), while the upper sum for P∗P^* contributes Mk′(c−xk−1)+Mk′′(xk−c)M_k'(c-x_{k-1}) + M_k''(x_k - c) from the two pieces. Since Mk′≤MkM_k' \le M_k and Mk′′≤MkM_k'' \le M_k, and both length factors (c−xk−1)(c - x_{k-1}) and (xk−c)(x_k - c) are positive,

Mk′(c−xk−1)+Mk′′(xk−c)  ≤  Mk(c−xk−1)+Mk(xk−c)=MkΔxk.M_k'(c-x_{k-1}) + M_k''(x_k-c) \;\le\; M_k(c-x_{k-1}) + M_k(x_k-c) = M_k \Delta x_k.

All other terms of U(f,P)U(f,P) and U(f,P∗)U(f,P^*) (from subintervals other than IkI_k) are identical. Adding these identical terms to both sides of the inequality above gives

U(f,P∗)≤U(f,P).U(f,P^*) \le U(f,P).

The argument for lower sums is symmetric: since mk′≥mkm_k' \ge m_k and mk′′≥mkm_k'' \ge m_k,

mk′(c−xk−1)+mk′′(xk−c)  ≥  mk(c−xk−1)+mk(xk−c)=mkΔxk,m_k'(c-x_{k-1}) + m_k''(x_k-c) \;\ge\; m_k(c-x_{k-1}) + m_k(x_k-c) = m_k \Delta x_k,

and adding the unchanged remaining terms gives

L(f,P∗)≥L(f,P).L(f,P^*) \ge L(f,P).

This proves Step 1.

Step 2: induction to a refinement by finitely many points. Suppose P⊆P∗P \subseteq P^* where P∗P^* has r≥0r \ge 0 points not in PP. We prove U(f,P∗)≤U(f,P)U(f,P^*) \le U(f,P) and L(f,P∗)≥L(f,P)L(f,P^*) \ge L(f,P) by induction on rr. Base case r=0r = 0: then P∗=PP^* = P and both inequalities are equalities, trivially true. Inductive step: suppose the claim holds whenever the refinement adds at most r−1r-1 new points (r≥1r \ge 1), and let P∗P^* add exactly rr new points to PP. Enumerate the new points as c1<c2<⋯<crc_1 < c_2 < \cdots < c_r (all lying in [a,b]∖P[a,b]\setminus P), and define an intermediate partition

Q=P∪{c1,…,cr−1},Q = P \cup \{c_1,\dots,c_{r-1}\},

which adds only r−1r-1 new points to PP, and note P∗=Q∪{cr}P^* = Q \cup \{c_r\} adds exactly one new point to QQ (since cr∉Qc_r \notin Q, as cr∉Pc_r \notin P and cr≠c1,…,cr−1c_r \neq c_1,\dots,c_{r-1} by the strict ordering). By the inductive hypothesis applied to P⊆QP \subseteq Q:

U(f,Q)≤U(f,P),L(f,Q)≥L(f,P).U(f,Q) \le U(f,P), \qquad L(f,Q) \ge L(f,P).

By Step 1 applied to the one-point refinement Q⊆P∗=Q∪{cr}Q \subseteq P^* = Q \cup \{c_r\}:

U(f,P∗)≤U(f,Q),L(f,P∗)≥L(f,Q).U(f,P^*) \le U(f,Q), \qquad L(f,P^*) \ge L(f,Q).

Chaining these inequalities,

U(f,P∗)≤U(f,Q)≤U(f,P),L(f,P∗)≥L(f,Q)≥L(f,P).U(f,P^*) \le U(f,Q) \le U(f,P), \qquad L(f,P^*) \ge L(f,Q) \ge L(f,P).

This completes the induction, proving that for any refinement P⊆P∗P \subseteq P^* (finitely many added points, since both are finite sets),

L(f,P)≤L(f,P∗)andU(f,P∗)≤U(f,P).L(f,P) \le L(f,P^*) \quad \text{and} \quad U(f,P^*) \le U(f,P).

Step 3: assembling the chain in the theorem. Given P⊆P∗P \subseteq P^*, combine Step 2 with Step 0 applied to the single partition P∗P^*:

L(f,P)    L(f,P∗)    U(f,P∗)    U(f,P).L(f,P) \;\overset{\text{Step 2}}{\le}\; L(f,P^*) \;\overset{\text{Step 0}}{\le}\; U(f,P^*) \;\overset{\text{Step 2}}{\le}\; U(f,P).

This is exactly the claimed chain of inequalities.

Step 4: the corollary for arbitrary P1,P2P_1, P_2. Let P1,P2P_1, P_2 be any two partitions of [a,b][a,b], and let P∗=P1∪P2P^* = P_1 \cup P_2 be their common refinement. Since P1⊆P∗P_1 \subseteq P^* and P∗P^* is a finite set (a union of two finite sets), the chain from Step 3 applied to the pair (P1,P∗)(P_1, P^*) gives

L(f,P1)≤U(f,P1)is not what we need directly; instead we use:L(f,P1)≤L(f,P∗).L(f,P_1) \le U(f,P_1) \quad\text{is not what we need directly; instead we use:}\qquad L(f,P_1) \le L(f,P^*).

Similarly, applying the chain to the pair (P2,P∗)(P_2, P^*) (noting P2⊆P∗P_2 \subseteq P^*) gives

U(f,P∗)≤U(f,P2).U(f,P^*) \le U(f,P_2).

Also by Step 0 applied to P∗P^* itself, L(f,P∗)≤U(f,P∗)L(f,P^*) \le U(f,P^*). Chaining all three:

L(f,P1)≤L(f,P∗)≤U(f,P∗)≤U(f,P2).L(f,P_1) \le L(f,P^*) \le U(f,P^*) \le U(f,P_2).

Hence L(f,P1)≤U(f,P2)L(f,P_1) \le U(f,P_2), as claimed. ■\blacksquare

∎
CorollaryBounding the Upper and Lower Integrals

Let ff be bounded on [a,b][a,b]. Then the set {L(f,P):P a partition of [a,b]}\{L(f,P) : P \text{ a partition of } [a,b]\} is bounded above (by U(f,P0)U(f,P_0) for any fixed partition P0P_0), and the set {U(f,P):P}\{U(f,P): P\} is bounded below (by L(f,P0)L(f,P_0)). Consequently both

∫ab‾f:=sup⁡PL(f,P),∫ab‾f:=inf⁡PU(f,P)\underline{\int_a^b} f := \sup_P L(f,P), \qquad \overline{\int_a^b} f := \inf_P U(f,P)

exist as finite real numbers, and ∫ab‾f≤∫ab‾f\underline{\int_a^b} f \le \overline{\int_a^b} f.

Proof

Fix any partition P0P_0. By the theorem's corollary (Step 4), for every partition PP we have L(f,P)≤U(f,P0)L(f,P) \le U(f,P_0), so U(f,P0)U(f,P_0) is an upper bound for {L(f,P):P}\{L(f,P): P\}; this set is also nonempty (it contains L(f,P0)L(f,P_0)), so by completeness of R\mathbb{R} its supremum ∫ab‾f\underline{\int_a^b} f exists and is finite, and moreover

∫ab‾f≤U(f,P0)\underline{\int_a^b} f \le U(f,P_0)

for this arbitrary fixed P0P_0 (since the supremum is the least upper bound, it is ≤\le any given upper bound). As P0P_0 was arbitrary, ∫ab‾f\underline{\int_a^b} f is a lower bound for {U(f,P):P}\{U(f,P): P\}. This set is nonempty and bounded below, so by completeness its infimum ∫ab‾f\overline{\int_a^b} f exists, is finite, and satisfies

∫ab‾f≤∫ab‾f,\underline{\int_a^b} f \le \overline{\int_a^b} f,

since ∫ab‾f\overline{\int_a^b} f is the greatest lower bound and we exhibited ∫ab‾f\underline{\int_a^b} f as a lower bound. ■\blacksquare

∎

Worked examples

Example 1: Refining an asymmetric partition of a linear function. Let f(x)=3x+1f(x) = 3x + 1 on [0,4][0, 4], and let P={0,1,4}P = \{0, 1, 4\} (an intentionally uneven partition — one short subinterval, one long one). Since ff is increasing, on any subinterval [u,v][u,v] we have sup⁡f=f(v)=3v+1\sup f = f(v) = 3v+1 and inf⁡f=f(u)=3u+1\inf f = f(u) = 3u+1. Subintervals of PP: [0,1][0,1] with Δx1=1\Delta x_1 = 1, and [1,4][1,4] with Δx2=3\Delta x_2 = 3.

M1=f(1)=4, m1=f(0)=1,M2=f(4)=13, m2=f(1)=4.M_1 = f(1) = 4,\ m_1 = f(0) = 1, \qquad M_2 = f(4) = 13,\ m_2 = f(1) = 4.
U(f,P)=4(1)+13(3)=4+39=43,L(f,P)=1(1)+4(3)=1+12=13.U(f,P) = 4(1) + 13(3) = 4 + 39 = 43, \qquad L(f,P) = 1(1) + 4(3) = 1 + 12 = 13.

Now refine by inserting the single point c=2.5c = 2.5 into the long subinterval [1,4][1,4], giving P∗={0,1,2.5,4}P^* = \{0, 1, 2.5, 4\}. The subinterval [0,1][0,1] is unchanged. The subinterval [1,4][1,4] splits into [1,2.5][1, 2.5] (length 1.51.5) and [2.5,4][2.5, 4] (length 1.51.5):

sup⁡[1,2.5]f=f(2.5)=8.5,inf⁡[1,2.5]f=f(1)=4,\sup_{[1,2.5]} f = f(2.5) = 8.5,\quad \inf_{[1,2.5]} f = f(1) = 4,
sup⁡[2.5,4]f=f(4)=13,inf⁡[2.5,4]f=f(2.5)=8.5.\sup_{[2.5,4]} f = f(4) = 13,\quad \inf_{[2.5,4]} f = f(2.5) = 8.5.
U(f,P∗)=4(1)+8.5(1.5)+13(1.5)=4+12.75+19.5=36.25.U(f,P^*) = 4(1) + 8.5(1.5) + 13(1.5) = 4 + 12.75 + 19.5 = 36.25.
L(f,P∗)=1(1)+4(1.5)+8.5(1.5)=1+6+12.75=19.75.L(f,P^*) = 1(1) + 4(1.5) + 8.5(1.5) = 1 + 6 + 12.75 = 19.75.

Check the theorem's chain:

L(f,P)=13≤L(f,P∗)=19.75≤U(f,P∗)=36.25≤U(f,P)=43.L(f,P) = 13 \le L(f,P^*) = 19.75 \le U(f,P^*) = 36.25 \le U(f,P) = 43.

Indeed 13≤19.75≤36.25≤4313 \le 19.75 \le 36.25 \le 43, confirming refinement decreased the upper sum (43→36.2543 \to 36.25) and increased the lower sum (13→19.7513 \to 19.75), exactly as the theorem predicts — and note the decrease and increase are of different magnitudes (6.756.75 vs.\ 6.756.75 here coincidentally equal since ff is linear with slope 33 and the split was symmetric in length, but observe this need not happen in general, as the next example shows with a nonlinear function).

Example 2: Two unrelated partitions and the common-refinement argument, for a nonlinear function. Let f(x)=x2f(x) = x^2 on [0,3][0,3]. Take two partitions that are not refinements of one another:

P1={0,2,3},P2={0,1,3}.P_1 = \{0, 2, 3\}, \qquad P_2 = \{0, 1, 3\}.

Compute L(f,P1)L(f,P_1). On [0,2][0,2]: inf⁡f=f(0)=0\inf f = f(0) = 0. On [2,3][2,3]: inf⁡f=f(2)=4\inf f = f(2) = 4.

L(f,P1)=0(2)+4(1)=4.L(f,P_1) = 0(2) + 4(1) = 4.

Compute U(f,P2)U(f,P_2). On [0,1][0,1]: sup⁡f=f(1)=1\sup f = f(1) = 1. On [1,3][1,3]: sup⁡f=f(3)=9\sup f = f(3) = 9.

U(f,P2)=1(1)+9(2)=1+18=19.U(f,P_2) = 1(1) + 9(2) = 1 + 18 = 19.

The theorem's corollary claims L(f,P1)≤U(f,P2)L(f,P_1) \le U(f,P_2), i.e. 4≤194 \le 19 — true, but let's verify it is not a coincidence by explicitly building the common refinement P∗=P1∪P2={0,1,2,3}P^* = P_1 \cup P_2 = \{0,1,2,3\} and checking the full chain. Subintervals of P∗P^*: [0,1],[1,2],[2,3][0,1], [1,2], [2,3], each of length 11.

inf⁡[0,1]f=0, inf⁡[1,2]f=1, inf⁡[2,3]f=4  ⟹  L(f,P∗)=0+1+4=5.\inf_{[0,1]} f = 0,\ \inf_{[1,2]} f = 1,\ \inf_{[2,3]} f = 4 \implies L(f,P^*) = 0+1+4 = 5.
sup⁡[0,1]f=1, sup⁡[1,2]f=4, sup⁡[2,3]f=9  ⟹  U(f,P∗)=1+4+9=14.\sup_{[0,1]} f = 1,\ \sup_{[1,2]} f = 4,\ \sup_{[2,3]} f = 9 \implies U(f,P^*) = 1+4+9 = 14.

Since P1⊆P∗P_1 \subseteq P^*, the theorem gives L(f,P1)≤L(f,P∗)L(f,P_1) \le L(f,P^*), i.e. 4≤54 \le 5 — true. Since P2⊆P∗P_2 \subseteq P^*, the theorem gives U(f,P∗)≤U(f,P2)U(f,P^*) \le U(f,P_2), i.e. 14≤1914 \le 19 — true. And L(f,P∗)≤U(f,P∗)L(f,P^*) \le U(f,P^*) gives 5≤145 \le 14 — true. Chaining exactly as in Step 4 of the proof:

L(f,P1)=4  ≤  L(f,P∗)=5  ≤  U(f,P∗)=14  ≤  U(f,P2)=19,L(f,P_1) = 4 \;\le\; L(f,P^*) = 5 \;\le\; U(f,P^*) = 14 \;\le\; U(f,P_2) = 19,

which reproduces 4≤194 \le 19 but through the intermediate common refinement, exactly as the proof constructs it — not by any special relationship between P1P_1 and P2P_2 themselves.

CounterexampleWhy boundedness of ff cannot be dropped

The theorem requires ff to be bounded on [a,b][a,b], so that every Mi=sup⁡IifM_i = \sup_{I_i} f and mi=inf⁡Iifm_i = \inf_{I_i} f is a finite real number. Drop this hypothesis and the sums may fail to be well-defined real numbers at all, making "decreases" or "increases" meaningless. Let f:[0,1]→Rf:[0,1]\to\mathbb{R} be defined by

f(x)={1/x0<x≤10x=0.f(x) = \begin{cases} 1/x & 0 < x \le 1 \\ 0 & x = 0. \end{cases}

This ff is unbounded on [0,1][0,1] (it is unbounded on every interval (0,ε)(0,\varepsilon)). Take P={0,1}P = \{0, 1\}. On I1=[0,1]I_1 = [0,1], sup⁡I1f=+∞\sup_{I_1} f = +\infty since f(x)=1/x→∞f(x) = 1/x \to \infty as x→0+x \to 0^+. So U(f,P)U(f,P) is not a finite number — it does not exist as a real number, and the "upper Darboux sum" is undefined (or must be declared +∞+\infty by convention, which is not a real number and breaks the ordered-field arithmetic the theorem's proof relies on). Now refine to P∗={0,0.5,1}P^* = \{0, 0.5, 1\}. On [0,0.5][0, 0.5], sup⁡f=+∞\sup f = +\infty still (since ff is unbounded on every interval touching 00), so U(f,P∗)U(f,P^*) is also not finite — refining did not fix the problem, and there is no meaningful sense in which U(f,P∗)≤U(f,P)U(f,P^*) \le U(f,P) since neither is a real number. The entire chain of inequalities in the theorem, which relies at each step on comparing finite reals via the completeness of R\mathbb{R} and on subtracting/adding finite quantities, simply cannot be formed. This is precisely why the definitions of Darboux sums and the theorem both begin with "let ff be bounded": without it, MiM_i and mim_i need not exist as real numbers, Step 0 of the proof ("mi≤Mim_i \le M_i") is not even a well-posed comparison of real numbers, and the entire refinement argument collapses before it starts.

Scope, edge cases & common traps

SituationWhat happens
P=P∗P = P^* (no new points)Chain becomes equalities: L(f,P)≤L(f,P)≤U(f,P)≤U(f,P)L(f,P) \le L(f,P) \le U(f,P) \le U(f,P) — theorem holds trivially.
P1,P2P_1, P_2 unrelated (neither refines the other)Theorem still gives L(f,P1)≤U(f,P2)L(f,P_1) \le U(f,P_2) via the common refinement P1∪P2P_1 \cup P_2; this is the corollary, not the main chain.
ff constant, f≡kf \equiv kEvery Mi=mi=kM_i = m_i = k, so U(f,P)=L(f,P)=k(b−a)U(f,P) = L(f,P) = k(b-a) for every partition — refinement changes nothing, consistent with the theorem (all inequalities become equalities).
ff unbounded on [a,b][a,b]MiM_i or mim_i may be +∞+\infty or −∞-\infty; Darboux sums are undefined; theorem's hypotheses are not met. See the counterexample above.
Refining by infinitely many points at onceNot covered — the proof's induction is on the finite number of added points; partitions are finite sets by definition, so this case does not arise within the theorem's scope.

The mathematician behind it

Origins · 1 minJean-Gaston Darboux and how this was discovered
JD
Jean-Gaston Darboux
1842–1917 · French mathematician, geometer, and analyst working at the Sorbonne and École Normale Supérieure

Darboux developed a self-contained, purely order-theoretic account of the integral using upper and lower sums, publishing it in 1875. His approach reformulated Riemann's earlier, more computational definition (built from arbitrary tagged sums and limits over partition meshes) into a cleaner criterion based on suprema and infima, sup and inf of sums over all partitions. Darboux's version is the one almost universally taught today as "the Riemann integral," even though it is more precisely the **Darboux integral** — proved equivalent to Riemann's original definition, but logically prior in most modern textbooks because its monotonicity properties, exactly the ones proved in this article, are so much easier to establish rigorously.

History
- 1854: Bernhard Riemann introduces the integral bearing his name, via limits of Riemann sums over tagged partitions, in his habilitation thesis.
- 1875: Gaston Darboux reformulates the theory using upper and lower sums, proving the refinement monotonicity theorem as the technical backbone of his approach.
- 1881: Darboux's full treatment, including the equivalence with Riemann's original definition, is published and widely adopted in French analysis texts.
- 1902: Henri Lebesgue's measure-theoretic integral appears, subsuming Riemann/Darboux integrability for a much broader class of functions, but the Darboux framework remains the standard first course in rigorous integration.
- 20th century: The Darboux formulation becomes the near-universal presentation of "the Riemann integral" in undergraduate real analysis courses worldwide, precisely because of the clean monotonicity lemma proved here.
Does refining a partition always strictly decrease the upper sum?

No. If ff is constant on the subinterval being split, or more generally if the supremum over each of the two new pieces still equals the supremum over the original piece (which can happen even for non-constant ff, e.g. if the sup is attained at an endpoint shared by both pieces), the upper sum stays exactly the same after refining. The theorem only guarantees a non-strict inequality U(f,P∗)≤U(f,P)U(f,P^*) \le U(f,P). Strict decrease is common but not guaranteed; proving strict decrease requires extra hypotheses (such as ff attaining strictly different suprema on the two sub-pieces), which is beyond the scope of this monotonicity result.

Why do we need the common refinement to compare sums from different partitions?

Two arbitrary partitions P1P_1 and P2P_2 may have completely different sets of division points, with no subinterval of P1P_1 contained in or equal to a subinterval of P2P_2. There is no way to directly compare L(f,P1)L(f,P_1) and U(f,P2)U(f,P_2) term by term because the subintervals do not align. The trick is to pass through a third partition, P∗=P1∪P2P^* = P_1 \cup P_2, which refines both P1P_1 and P2P_2 simultaneously. The refinement theorem (proved for the inclusion order) then applies to each pair (P1,P∗)(P_1, P^*) and (P2,P∗)(P_2, P^*) separately, and the inequalities chain together through L(f,P∗)≤U(f,P∗)L(f,P^*) \le U(f,P^*).

Is this the same as proving that ff is Riemann integrable?

No, and this is a common confusion. This theorem only shows that upper sums decrease and lower sums increase under refinement, and that every lower sum is at most every upper sum. It guarantees the upper integral ∫‾f\overline{\int} f and lower integral ∫‾f\underline{\int} f exist as finite numbers with ∫‾f≤∫‾f\underline{\int} f \le \overline{\int} f. Integrability requires the stronger statement that these two numbers are equal, which is a separate theorem (the Riemann/Darboux integrability criterion) built on top of this one.

Can the theorem fail if the partition points are not required to be increasing, i.e. not properly ordered?

The definition of a partition already builds in x0<x1<⋯<xnx_0 < x_1 < \cdots < x_n, so "not properly ordered" point sets are not partitions at all — the theorem's hypotheses simply do not apply to them. If you tried to define sums using an unordered listing of points, the subintervals Δxi=xi−xi−1\Delta x_i = x_i - x_{i-1} could be negative, breaking the very first inequality miΔxi≤MiΔxim_i \Delta x_i \le M_i \Delta x_i in Step 0 of the proof (which relies on Δxi>0\Delta x_i > 0 to preserve the direction of the inequality when multiplying).

Does this theorem require ff to be continuous?

No. The only hypothesis on ff is boundedness on [a,b][a,b]; continuity is never used anywhere in the proof. The suprema and infima Mi,miM_i, m_i exist for any bounded function by the completeness of R\mathbb{R} (bounded nonempty sets of reals have finite sup and inf), regardless of continuity. Continuity becomes important later, in proving that specific classes of functions (like continuous functions) are actually Riemann integrable, but it plays no role in this monotonicity lemma itself.

What happens if a=ba = b, i.e. the interval is degenerate?

The setup assumes a<ba < b so that partitions have at least one subinterval of positive length. If a=ba = b, there are no subintervals to speak of, both sums are vacuously 00 for the trivial "partition" {a}\{a\}, and the theorem's content is empty (there is nothing to refine). This trivial case is typically excluded from the outset, as done here, to avoid degenerate bookkeeping.

Before this

Where this leads