2025 AMC 12A Problems/Problem 2: Difference between revisions
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Note that we can set the information given in the problem into a table shown below: | Note that we can set the information given in the problem into a table shown below: | ||
<cmath>\renewcommand{\arraystretch}{1.5} | |||
\begin{centering} | |||
\begin{array}{| c | c | c |} | \begin{array}{| c | c | c |} | ||
\hline | \hline | ||
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\hline | \hline | ||
\end{array} | \end{array} | ||
\end{centering} | |||
</cmath> | |||
We are given that the new nut mix will contain <imath>40\%</imath> peanuts. Hence, <imath>5 + \frac{2}{10}x</imath> is <imath>40\%</imath> of the total mix which is <imath>10 + x</imath>. | We are given that the new nut mix will contain <imath>40\%</imath> peanuts. Hence, <imath>5 + \frac{2}{10}x</imath> is <imath>40\%</imath> of the total mix which is <imath>10 + x</imath>. | ||
| Line 67: | Line 71: | ||
==Solution 5(extremely long, overcomplicated, never use on the test)== | ==Solution 5(extremely long, overcomplicated, never use on the test)== | ||
Note: This got messed up when putting into the wiki and it has been re-interpreted by AI. Please review this solution carefully and correct any AI errors. | |||
1️⃣ Measure-Theoretic Setup | |||
Let (Ω, F, μ) be a finite measure space, where Ω = {peanuts, cashews, almonds}. | |||
Define a density function f_i : Ω → [0,1] representing the probability distribution (composition) of each mix i: | |||
(Ω,F,μ) | |||
be a finite measure space, where | - f₁(peanuts) = 0.5, f₁(cashews) = 0.2, f₁(almonds) = 0.3 | ||
Ω | - f₂(peanuts) = 0.2, f₂(cashews) = 0.4, f₂(almonds) = 0.4 | ||
= | |||
{ | Each mix corresponds to a measure ν_i = m_i f_i μ, where m_i is the total mass (10 lb for i=1, unknown x lb for i=2). | ||
peanuts | |||
, | The combined measure is: | ||
cashews | ν = ν₁ + ν₂ = (m₁f₁ + m₂f₂)μ | ||
, | |||
almonds | |||
} | |||
Define a density function | |||
: | |||
Ω | |||
→ | |||
i: | |||
( | |||
peanuts | |||
) | |||
= | |||
0.2 | |||
, | |||
( | |||
cashews | |||
) | |||
= | |||
0.4 | |||
, | |||
( | |||
almonds | |||
Each mix corresponds to a measure | |||
= | |||
where | |||
i=1, unknown | |||
x lb for | |||
i=2). | |||
The combined measure is | |||
ν | |||
= | |||
+ | |||
= | |||
( | |||
+ | |||
) | |||
μ | |||
The normalized mixture (probability measure for composition) is: | The normalized mixture (probability measure for composition) is: | ||
f | f = (m₁f₁ + m₂f₂) / (m₁ + m₂) | ||
= | |||
We are told that f(peanuts) = 0.4. | |||
+ | |||
+ | |||
We are told that | |||
f | |||
( | |||
peanuts | |||
) | |||
= | |||
0.4. | |||
2️⃣ Functional Equation in Measure Form | 2️⃣ Functional Equation in Measure Form | ||
This is equivalent to: | This is equivalent to: | ||
[m₁f₁(peanuts) + m₂f₂(peanuts)] / (m₁ + m₂) = 0.4 | |||
Substitute m₁ = 10: | |||
[10(0.5) + x(0.2)] / (10 + x) = 0.4 | |||
( | |||
peanuts | Same as before — but this time we view x as a scalar measure parameter in the space of signed measures. | ||
) | |||
+ | Solving yields: '''x = 5''' | ||
( | |||
peanuts | |||
) | |||
Substitute | |||
10 | |||
( | |||
0.5 | |||
) | |||
+ | |||
x | |||
( | |||
0.2 | |||
) | |||
10 | |||
+ | |||
x | |||
= | |||
0.4 | |||
Same as before — but this time we view | |||
x as a scalar measure parameter in the space of signed measures. | |||
Solving yields: | |||
x | |||
= | |||
5 | |||
3️⃣ Abstract Affine Geometry View | 3️⃣ Abstract Affine Geometry View | ||
Let | |||
Let Δ₂ = {(p,c,a) ∈ ℝ³ : p+c+a=1, p,c,a≥0}, the 2-simplex representing all possible nut compositions. | |||
= | |||
{ | |||
( | |||
p | |||
, | |||
c | |||
, | |||
a | |||
) | |||
∈ | |||
Each mix is a point in this simplex: | Each mix is a point in this simplex: | ||
- v₁ = (0.5, 0.2, 0.3) | |||
- v₂ = (0.2, 0.4, 0.4) | |||
= | |||
( | |||
0.5 | |||
, | |||
0.2 | |||
, | |||
0.3 | |||
) | |||
= | |||
( | |||
0.2 | |||
, | |||
0.4 | |||
, | |||
0.4 | |||
The combined mix lies on the affine line joining them: | The combined mix lies on the affine line joining them: | ||
v | v = (10v₁ + 5v₂) / 15 | ||
= | |||
The map Φ: (ℝ₊)² → Δ₂, (m₁,m₂) ↦ (m₁v₁ + m₂v₂)/(m₁ + m₂) is an affine morphism of positive cones that collapses scalar measures to compositions. | |||
The constraint π_p(v) = 0.4 defines a hyperplane section of the simplex, and the intersection with the line segment joining v₁, v₂ defines a unique barycentric coordinate λ = 1/3. | |||
+ | |||
15 | |||
The map | |||
Φ | |||
: | |||
( | |||
) | |||
→ | |||
, | |||
( | |||
, | |||
) | |||
↦ | |||
+ | |||
is an affine morphism of positive cones that collapses scalar measures to compositions. | |||
The constraint | |||
, | |||
λ | |||
= | |||
1 | |||
3 | |||
This corresponds to an affine convex combination: | This corresponds to an affine convex combination: | ||
v | v = (1-λ)v₁ + λv₂, λ = 1/3 | ||
= | |||
( | |||
1 | |||
λ | |||
) | |||
+ | |||
, | |||
λ | |||
1 | |||
3 | |||
4️⃣ Categorical Abstract Algebra Interpretation | 4️⃣ Categorical Abstract Algebra Interpretation | ||
We can view the mixing process as a functor: | We can view the mixing process as a functor: | ||
Mix: (FinMeas, +) → (Δ₂, convex combinations) | |||
Mix:(FinMeas,+)→( | |||
where each object is a measure with labeled components (mass and composition), and morphisms are scalar additions of measures. | where each object is a measure with labeled components (mass and composition), and morphisms are scalar additions of measures. | ||
The condition | |||
Φ | The condition "final mix has 40% peanuts" is a natural transformation constraint between two functors: | ||
, | Φ, Ψ: FinMeas → ℝ | ||
Ψ | |||
: | where: | ||
- Φ(ν) = total mass of peanuts | |||
- Ψ(ν) = total mass | |||
We require Φ(ν)/Ψ(ν) = 0.4. | |||
This induces a categorical equation that forces the unique morphism ratio ν₂:ν₁ = 1:2. | |||
→ | Hence '''x = 5'''. | ||
Φ | |||
( | |||
ν | |||
) | |||
= | |||
total mass of peanuts | |||
Ψ | |||
( | |||
ν | |||
) | |||
= | |||
total mass | |||
We require | |||
Φ | |||
( | |||
ν | |||
) | |||
/ | |||
Ψ | |||
( | |||
ν | |||
) | |||
= | |||
0.4. | |||
This induces a categorical equation that forces the unique morphism ratio | |||
: | |||
= | |||
1 | |||
: | |||
2 | |||
Hence | |||
x | |||
= | |||
5. | |||
5️⃣ Differential-Geometric / Tangent-Space Insight | 5️⃣ Differential-Geometric / Tangent-Space Insight | ||
On the manifold | |||
M | On the manifold M = Δ₂, the line of mixtures parameterized by x is a 1D affine submanifold: | ||
= | γ(x) = (10v₁ + xv₂)/(10 + x) | ||
The constraint surface S = {v ∈ Δ₂ : p = 0.4} is a codimension-1 affine submanifold (a plane slice). | |||
The intersection S ∩ Im(γ) is transversal because the derivative dπ_p(γ'(x)) ≠ 0. | |||
Hence there exists a unique transverse intersection point '''x = 5'''. | |||
x is a 1D affine submanifold: | |||
γ | |||
( | |||
x | |||
) | |||
= | |||
+ | |||
10 | |||
+ | |||
The constraint surface | |||
S | |||
= | |||
{ | |||
v | |||
∈ | |||
The intersection | |||
S | |||
∩ | |||
Im | |||
( | |||
γ | |||
( | |||
x | |||
) | |||
) | |||
≠ | |||
0 | |||
Hence there exists a unique transverse intersection point | |||
x | |||
= | |||
5 | |||
That transversality guarantees that the equilibrium composition is structurally stable under small perturbations of the parameters — i.e., you could wiggle the percentages slightly and the solution still exists and varies smoothly. | That transversality guarantees that the equilibrium composition is structurally stable under small perturbations of the parameters — i.e., you could wiggle the percentages slightly and the solution still exists and varies smoothly. | ||
6️⃣ Return to measurable quantity | 6️⃣ Return to measurable quantity | ||
Total cashew mass: | Total cashew mass: | ||
M_cashew = 10(0.20) + 5(0.40) = 2 + 2 = '''4 pounds''' | |||
= | |||
10 | |||
( | |||
0.20 | |||
) | |||
+ | |||
5 | |||
( | |||
0.40 | |||
) | |||
= | |||
2 | |||
+ | |||
2 | |||
= | |||
4 | |||
==Video Solution by Power Solve== | ==Video Solution by Power Solve== | ||
Latest revision as of 17:58, 10 November 2025
- The following problem is from both the 2025 AMC 10A #2 and 2025 AMC 12A #2, so both problems redirect to this page.
Problem
A box contains
pounds of a nut mix that is
percent peanuts,
percent cashews, and
percent almonds. A second nut mix containing
percent peanuts,
percent cashews, and
percent almonds is added to the box resulting in a new nut mix that is
percent peanuts. How many pounds of cashews are now in the box?
Solution 1
We are given
pounds of cashews in the first box.
Denote the pounds of nuts in the second nut mix as
Thus, we have
pounds of the second mix.
~pigwash
~yuvaG (Formatting)
~LucasW (Minor LaTeX)
Solution 2
Let the number of pounds of nuts in the second nut mix be
. Therefore, we get the equation
. Solving it, we get
. Therefore the amount of cashews in the two bags is
, so our answer choice is
.
~iiiiiizh
~yuvaG -
Formatting ;)
~Amon26(really minor edits)
Solution 3
The percent of peanuts in the first mix is
away from the total percentage of peanuts, and the percent of peanuts in the second mix is
away from the total percentage. This means the first mix has twice as many nuts as the second mix, so the second mix has
pounds.
pounds of cashews. So our answer is,
~LUCKYOKXIAO
~LEONG2023-Latex
Solution 4
Note that we can set the information given in the problem into a table shown below:
We are given that the new nut mix will contain
peanuts. Hence,
is
of the total mix which is
.
Solving the equation
yields
Therefore, the number of cashews in the new mix is equal to
.
~Moonlight11
~TehSovietOnion (LaTeX)
Solution 5(extremely long, overcomplicated, never use on the test)
Note: This got messed up when putting into the wiki and it has been re-interpreted by AI. Please review this solution carefully and correct any AI errors.
1️⃣ Measure-Theoretic Setup
Let (Ω, F, μ) be a finite measure space, where Ω = {peanuts, cashews, almonds}.
Define a density function f_i : Ω → [0,1] representing the probability distribution (composition) of each mix i:
- f₁(peanuts) = 0.5, f₁(cashews) = 0.2, f₁(almonds) = 0.3 - f₂(peanuts) = 0.2, f₂(cashews) = 0.4, f₂(almonds) = 0.4
Each mix corresponds to a measure ν_i = m_i f_i μ, where m_i is the total mass (10 lb for i=1, unknown x lb for i=2).
The combined measure is: ν = ν₁ + ν₂ = (m₁f₁ + m₂f₂)μ
The normalized mixture (probability measure for composition) is: f = (m₁f₁ + m₂f₂) / (m₁ + m₂)
We are told that f(peanuts) = 0.4.
2️⃣ Functional Equation in Measure Form
This is equivalent to: [m₁f₁(peanuts) + m₂f₂(peanuts)] / (m₁ + m₂) = 0.4
Substitute m₁ = 10: [10(0.5) + x(0.2)] / (10 + x) = 0.4
Same as before — but this time we view x as a scalar measure parameter in the space of signed measures.
Solving yields: x = 5
3️⃣ Abstract Affine Geometry View
Let Δ₂ = {(p,c,a) ∈ ℝ³ : p+c+a=1, p,c,a≥0}, the 2-simplex representing all possible nut compositions.
Each mix is a point in this simplex: - v₁ = (0.5, 0.2, 0.3) - v₂ = (0.2, 0.4, 0.4)
The combined mix lies on the affine line joining them: v = (10v₁ + 5v₂) / 15
The map Φ: (ℝ₊)² → Δ₂, (m₁,m₂) ↦ (m₁v₁ + m₂v₂)/(m₁ + m₂) is an affine morphism of positive cones that collapses scalar measures to compositions.
The constraint π_p(v) = 0.4 defines a hyperplane section of the simplex, and the intersection with the line segment joining v₁, v₂ defines a unique barycentric coordinate λ = 1/3.
This corresponds to an affine convex combination: v = (1-λ)v₁ + λv₂, λ = 1/3
4️⃣ Categorical Abstract Algebra Interpretation
We can view the mixing process as a functor: Mix: (FinMeas, +) → (Δ₂, convex combinations)
where each object is a measure with labeled components (mass and composition), and morphisms are scalar additions of measures.
The condition "final mix has 40% peanuts" is a natural transformation constraint between two functors: Φ, Ψ: FinMeas → ℝ
where: - Φ(ν) = total mass of peanuts - Ψ(ν) = total mass
We require Φ(ν)/Ψ(ν) = 0.4.
This induces a categorical equation that forces the unique morphism ratio ν₂:ν₁ = 1:2.
Hence x = 5.
5️⃣ Differential-Geometric / Tangent-Space Insight
On the manifold M = Δ₂, the line of mixtures parameterized by x is a 1D affine submanifold: γ(x) = (10v₁ + xv₂)/(10 + x)
The constraint surface S = {v ∈ Δ₂ : p = 0.4} is a codimension-1 affine submanifold (a plane slice).
The intersection S ∩ Im(γ) is transversal because the derivative dπ_p(γ'(x)) ≠ 0.
Hence there exists a unique transverse intersection point x = 5.
That transversality guarantees that the equilibrium composition is structurally stable under small perturbations of the parameters — i.e., you could wiggle the percentages slightly and the solution still exists and varies smoothly.
6️⃣ Return to measurable quantity
Total cashew mass: M_cashew = 10(0.20) + 5(0.40) = 2 + 2 = 4 pounds
Video Solution by Power Solve
https://youtu.be/QBn439idcPo?si=jrzzKE72p29BIDQZ&t=102
Chinese Video Solution
https://www.bilibili.com/video/BV1S52uBoE8d/
~metrixgo
Video Solution (Intuitive, Quick Explanation!)
~ Education, the Study of Everything
Video Solution (Fast and Easy)
https://youtu.be/YpJ3QZTmDuw?si=ucvH15JKX2tw4SKZ ~ Pi Academy
Video Solution by SpreadTheMathLove
https://www.youtube.com/watch?v=dAeyV60Hu5c
Video Solution by Daily Dose of Math
~Thesmartgreekmathdude
Video Solution
~MK
Video Solution
See Also
| 2025 AMC 10A (Problems • Answer Key • Resources) | ||
| Preceded by Problem 1 |
Followed by Problem 3 | |
| 1 • 2 • 3 • 4 • 5 • 6 • 7 • 8 • 9 • 10 • 11 • 12 • 13 • 14 • 15 • 16 • 17 • 18 • 19 • 20 • 21 • 22 • 23 • 24 • 25 | ||
| All AMC 10 Problems and Solutions | ||
| 2025 AMC 12A (Problems • Answer Key • Resources) | |
| Preceded by Problem 1 |
Followed by Problem 3 |
| 1 • 2 • 3 • 4 • 5 • 6 • 7 • 8 • 9 • 10 • 11 • 12 • 13 • 14 • 15 • 16 • 17 • 18 • 19 • 20 • 21 • 22 • 23 • 24 • 25 | |
| All AMC 12 Problems and Solutions | |
These problems are copyrighted © by the Mathematical Association of America.