#149 - Maximum-sum Subsequence
Looking at the table below, it is easy to verify that the maximum possible sum of adjacent numbers in any direction (horizontal, vertical, diagonal or anti-diagonal) is \(16\) (\(= 8 + 7 + 1\)).
| \(-2\) | \(5\) | \(3\) | \(2\) |
| \(9\) | \(-6\) | \(5\) | \(1\) |
| \(3\) | \(2\) | \(7\) | \(3\) |
| \(-1\) | \(8\) | \(-4\) | \(8\) |
Now, let us repeat the search, but on a much larger scale:
First, generate four million pseudo-random numbers using a specific form of what is known as a "Lagged Fibonacci Generator":
For \(1 \le k \le 55\), \(s_k = [100003 - 200003 k + 300007 k^3] \pmod{1000000} - 500000\).
For \(56 \le k \le 4000000\), \(s_k = [s_{k-24} + s_{k - 55} + 1000000] \pmod{1000000} - 500000\).
Thus, \(s_{10} = -393027\) and \(s_{100} = 86613\).
The terms of \(s\) are then arranged in a \(2000 \times 2000\) table, using the first \(2000\) numbers to fill the first row (sequentially), the next \(2000\) numbers to fill the second row, and so on.
Finally, find the greatest sum of (any number of) adjacent entries in any direction (horizontal, vertical, diagonal or anti-diagonal).
Problem text © Project Euler, licensed under CC BY-NC-SA 4.0. Original: projecteuler.net/problem=149. Published Friday, 13th April 2007, 10:00 pm. Solved by 5,649 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:
1.3 Proof Techniques: Direct, Contrapositive, and Contradiction · 13.2 Monte Carlo Estimation and Error Analysis · 19.1 Divisibility, GCD, and the Euclidean Algorithm · 19.9 Graph Algorithms: BFS, DFS, Dijkstra, and Minimum Spanning Trees · 19.13 Matrix Exponentiation and Linear Recurrence Acceleration · 19.3 Modular Arithmetic, Inverses, and Fast Exponentiation · 19.10 Search: Backtracking, Branch-and-Bound, Binary Search, Meet-in-the-Middle · 19.2 Primes, Sieves, and Integer Factorization · 3.1 Vectors, Multivariable Functions, and Level Sets · 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: #587 · #504 · #577
Concepts: geometry graph-theory number-theory probability
Likely techniques: bfs-dfs matrix-exponentiation
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Understand the problem
- What exactly is the input to problem 149? Is it a bound (4000000), 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 constraint does the bound 4000000 impose, and is it inclusive or exclusive?
- What are the edge cases: the smallest legal object, zero/one, ties, and the boundary at exactly 4000000?
- Why is brute force hard HERE specifically? Estimate the number of candidates implied by 4000000 and the cost of testing one.
- Which geometry 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 geometry / matrix-exponentiation - do you agree, and why?)
- Predict the complexity of your intended method in terms of N = 4000000, 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 - then run it. A surprise here is worth more than an hour of debugging later.
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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 geometry structure do the real work? Name the single observation that collapsed the search space.
Could you have reached the matrix-exponentiation 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 4000000 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 'matrix-exponentiation' underneath?
State the transferable technique in one sentence, without mentioning this problem's story at all.
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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: #587 · #504 · #577
- 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.