#183 - Maximum Product of Parts
Let \(N\) be a positive integer and let \(N\) be split into \(k\) equal parts, \(r = N/k\), so that \(N = r + r + \cdots + r\).
Let \(P\) be the product of these parts, \(P = r \times r \times \cdots \times r = r^k\).
For example, if \(11\) is split into five equal parts, \(11 = 2.2 + 2.2 + 2.2 + 2.2 + 2.2\), then \(P = 2.2^5 = 51.53632\).
Let \(M(N) = P_{\mathrm{max}}\) for a given value of \(N\).
It turns out that the maximum for \(N = 11\) is found by splitting eleven into four equal parts which leads to \(P_{\mathrm{max}} = (11/4)^4\); that is, \(M(11) = 14641/256 = 57.19140625\), which is a terminating decimal.
However, for \(N = 8\) the maximum is achieved by splitting it into three equal parts, so \(M(8) = 512/27\), which is a non-terminating decimal.
Let \(D(N) = N\) if \(M(N)\) is a non-terminating decimal and \(D(N) = -N\) if \(M(N)\) is a terminating decimal.
For example, \(\sum\limits_{N = 5}^{100} D(N)\) is \(2438\).
Find \(\sum\limits_{N = 5}^{10000} D(N)\).
Problem text © Project Euler, licensed under CC BY-NC-SA 4.0. Original: projecteuler.net/problem=183. Published Friday, 22nd February 2008, 05:00 pm. Solved by 5,358 members at time of mirroring.
Why this is useful
Optimization. A genuine objective is optimised over a choice set - the same shape as calibration and optimal-execution problems (Phases 10, 16).
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Prerequisites
Lessons that prepare you:
10.4 Algorithms: Gradient Descent and Newton's Method · 10.1 Convex Sets and Convex Functions · 19.14 Computational Complexity, Feasibility Estimation, and Proving Algorithms Correct
Recommended stepping-stone problems: #8 · #11 · #103
Concepts: optimization
Learning mode
Pick how much scaffolding you want. Your choice is remembered per problem.
Understand the problem
- What exactly is the input to problem 183? Is it a bound (10000), a supplied dataset, or a definition you must generate from?
- What is the required output - restate it precisely: a single sum.
- Which objects exactly are in scope, and which are excluded by the wording (strict vs non-strict inequality, 'distinct', 'proper', 'below' vs 'up to')?
- What do the arguments of D(N), M(N) mean, and what is the value's type (count, sum, probability)?
- What are the edge cases: the smallest legal object, zero/one, ties, and the boundary at exactly 10000?
- Why is brute force hard HERE specifically? Estimate the number of candidates implied by 10000 and the cost of testing one.
- Which optimization fact would, if true, collapse the search - and can you state it as a testable claim before you look for a proof?
Predict & plan (before you code)
- Predict the strategy: in one sentence, what will your solution do? (The classification says optimization - do you agree, and why?)
- Predict the complexity of your intended method in terms of N = 10000, and the wall-clock time you expect. Write both down now.
- Predict the key data structure: what is stored, keyed by what, and how large will it get at full scale?
- Predict the failure mode: what is most likely to break - an off-by-one on the bound, a definition misread, precision, or memory?
- Predict the output of the small case from rung 3 BEFORE running it (the statement says: "For example, if 11 is split into five equal parts, 11 = 2.") - then run it. A surprise here is worth more than an hour of debugging later.
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Progressive hints
Optimization
You have a correct answer. That is the start of the learning, not the end.
- Reduce the time complexity. What is the bottleneck, and what mathematical fact removes it?
- Reduce memory. Can you stream, or keep only the last k states?
- Replace brute force with a closed form, a sieve, a recurrence, or a symmetry argument.
- Prove the optimized version computes the same thing.
- Compare two implementations and time them.
Explain it
Which step of your solution were you least confident about, and what evidence would settle it?
What did you try first, and what specifically made you abandon it - a proof, a timing, or a wrong small-case answer?
Where did the optimization structure do the real work? Name the single observation that collapsed the search space.
Could you have reached the optimization idea faster? Which words in the statement were pointing at it, and did you notice them?
What was the bug that cost you the most time, and what CLASS of bug was it (off-by-one, definition misread, precision, state under-specified)?
How would your solution change if the bound 10000 were multiplied by 1000? Does it survive, or does it need a different idea?
What is the honest complexity of what you wrote (not what you intended), and where is the remaining slack?
Which problem you have already solved is this most similar to, and what is the shared skeleton - is it really 'optimization' underneath?
State the transferable technique in one sentence, without mentioning this problem's story at all.
Self-assess (mastery is not a correct number)
You reach Mastered only when you have solved it, rated yourself at least Solid across the dimensions, and written a real explanation.
Confidence
Low confidence schedules this problem for spaced review, even if you solved it.
Mastery check
- Variation: change the bound (or a rule) in the statement. Does your method still work? What breaks first?
- Constraints: if the limit were 10× larger, which step fails, and what would you replace it with?
- Related problem: #8 · #11 · #103
- Transfer: where else does this technique appear? Name a lesson and a real computational setting.
- Spaced re-attempt: come back after the review interval and re-solve it with no hints.