#238 - Infinite String Tour
Create a sequence of numbers using the "Blum Blum Shub" pseudo-random number generator:
\[\begin{align} s_0 &= 14025256\\ s_{n + 1} &= s_n^2 \bmod 20300713 \end{align}\]Concatenate these numbers \(s_0s_1s_2\cdots\) to create a string \(w\) of infinite length.
Then, \(w = {\color{blue}14025256741014958470038053646\cdots}\)
For a positive integer \(k\), if no substring of \(w\) exists with a sum of digits equal to \(k\), \(p(k)\) is defined to be zero. If at least one substring of \(w\) exists with a sum of digits equal to \(k\), we define \(p(k) = z\), where \(z\) is the starting position of the earliest such substring.
For instance:
The substrings \(\color{blue}1, 14, 1402, \dots\)
with respective sums of digits equal to \(1, 5, 7, \dots\)
start at position \(\mathbf 1\), hence \(p(1) = p(5) = p(7) = \cdots = \mathbf 1\).
The substrings \(\color{blue}4, 402, 4025, \dots\)
with respective sums of digits equal to \(4, 6, 11, \dots\)
start at position \(\mathbf 2\), hence \(p(4) = p(6) = p(11) = \cdots = \mathbf 2\).
The substrings \(\color{blue}02, 0252, \dots\)
with respective sums of digits equal to \(2, 9, \dots\)
start at position \(\mathbf 3\), hence \(p(2) = p(9) = \cdots = \mathbf 3\).
Note that substring \(\color{blue}025\) starting at position \(\mathbf 3\), has a sum of digits equal to \(7\), but there was an earlier substring (starting at position \(\mathbf 1\)) with a sum of digits equal to \(7\), so \(p(7) = 1\), not \(3\).
We can verify that, for \(0 \lt k \le 10^3\), \(\sum p(k) = 4742\).
Find \(\sum p(k)\), for \(0 \lt k \le 2 \times 10^{15}\).
Problem text © Project Euler, licensed under CC BY-NC-SA 4.0. Original: projecteuler.net/problem=238. Published Sunday, 29th March 2009, 03:00 pm. Solved by 1,156 members at time of mirroring.
Why this is useful
Probability Statistics. Expectation and state-based probability reasoning underpin pricing, risk, and statistical inference (Phases 7, 11, 13).
We classify relevance honestly - not every Euler problem is a trading application.
Prerequisites
Lessons that prepare you:
13.2 Monte Carlo Estimation and Error Analysis · 17.1 Python for Quants: NumPy, pandas, and Vectorization · 19.5 Combinatorics: Counting, Binomials, and Inclusion–Exclusion · 19.14 Computational Complexity, Feasibility Estimation, and Proving Algorithms Correct · 19.7 Dynamic Programming: Memoization and Tabulation · 19.6 Recurrence Relations and Generating Functions · 2.7 Sequences, Series, Convergence, and Power Series · 7.3 Independence, Conditional Probability, and Conditional Expectation · 7.2 Expectation, Moments, and Key Inequalities · 7.1 Probability Spaces, Random Variables, and Distributions
Recommended stepping-stone problems: #653 · #819 · #826
Concepts: probability sequences-series string-processing brute-force-reduction
Likely techniques: digit-dp inclusion-exclusion
Learning mode
Pick how much scaffolding you want. Your choice is remembered per problem.
Understand the problem
- What exactly is the input to problem 238? Is it a bound (10^15), 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 p(k), p(7) 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 10^15?
- Why is brute force hard HERE specifically? Estimate the number of candidates implied by 10^15 and the cost of testing one.
- Which probability 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 probability / digit-dp - do you agree, and why?)
- Predict the complexity of your intended method in terms of N = 10^15, 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 instance: The substrings blue1, 14, 1402,.") - then run it. A surprise here is worth more than an hour of debugging later.
Scratchpad
Mathematical notes, formulas, pseudocode, hypotheses, complexity notes. Saved automatically with your progress.
Python workbench
Real Python (Pyodide) in a sandboxed Web Worker - no network, no filesystem, no DOM access. Ctrl/Cmd+Enter runs. Escape leaves the editor. Stop terminates the worker.
Check your answer
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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 probability structure do the real work? Name the single observation that collapsed the search space.
Could you have reached the digit-dp 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 10^15 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 'digit-dp' 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: #653 · #819 · #826
- 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.