#648 - Skipping Squares
For some fixed \(\rho \in [0, 1]\), we begin a sum \(s\) at \(0\) and repeatedly apply a process: With probability \(\rho\), we add \(1\) to \(s\), otherwise we add \(2\) to \(s\).
The process ends when either \(s\) is a perfect square or \(s\) exceeds \(10^{18}\), whichever occurs first. For example, if \(s\) goes through \(0, 2, 3, 5, 7, 9\), the process ends at \(s=9\), and two squares \(1\) and \(4\) were skipped over.
Let \(f(\rho)\) be the expected number of perfect squares skipped over when the process finishes.
It can be shown that the power series for \(f(\rho)\) is \(\sum_{k=0}^\infty a_k \rho^k\) for a suitable (unique) choice of coefficients \(a_k\). Some of the first few coefficients are \(a_0=1\), \(a_1=0\), \(a_5=-18\), \(a_{10}=45176\).
Let \(F(n) = \sum_{k=0}^n a_k\). You are given that \(F(10) = 53964\) and \(F(50) \equiv 842418857 \pmod{10^9}\).
Find \(F(1000)\), and give your answer modulo \(10^9\).
Problem text © Project Euler, licensed under CC BY-NC-SA 4.0. Original: projecteuler.net/problem=648. Published Sunday, 23rd December 2018, 10:00 am. Solved by 345 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).
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Prerequisites
Lessons that prepare you:
1.3 Proof Techniques: Direct, Contrapositive, and Contradiction · 1.1 Sets, Functions, and Relations · 13.2 Monte Carlo Estimation and Error Analysis · 17.1 Python for Quants: NumPy, pandas, and Vectorization · 19.14 Computational Complexity, Feasibility Estimation, and Proving Algorithms Correct · 19.1 Divisibility, GCD, and the Euclidean Algorithm · 19.3 Modular Arithmetic, Inverses, and Fast Exponentiation · 19.6 Recurrence Relations and Generating Functions · 19.10 Search: Backtracking, Branch-and-Bound, Binary Search, Meet-in-the-Middle · 19.2 Primes, Sieves, and Integer Factorization · 2.1 Functions, Limits, and Continuity · 2.7 Sequences, Series, Convergence, and Power Series · 3.1 Vectors, Multivariable Functions, and Level Sets · 4.2 Linear Maps, Matrices, Rank, and the Null Space · 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: #819 · #826 · #316
Concepts: algebra geometry number-theory probability sequences-series brute-force-reduction
Likely techniques: generating-functions hashing modular-exponentiation
Learning mode
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Understand the problem
- What exactly is the input to problem 648? Is it a bound (10^9), a supplied dataset, or a definition you must generate from?
- What is the required output - restate it precisely: a single residue (the answer is reduced modulo a given number, so keep everything in modular arithmetic from the start). Required format: give your answer modulo 10^9.
- 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 F(n), F(10) 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 10^9?
- Why is brute force hard HERE specifically? Estimate the number of candidates implied by 10^9 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 / generating-functions - do you agree, and why?)
- Predict the complexity of your intended method in terms of N = 10^9, 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 s goes through 0, 2, 3, 5, 7, 9, the process ends at s=9, and two squares 1 and 4 were skipped over.") - then run it. A surprise here is worth more than an hour of debugging later.
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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 generating-functions 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 10^9 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 'generating-functions' underneath?
State the transferable technique in one sentence, without mentioning this problem's story at all.
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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: #819 · #826 · #316
- 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.