#527 - Randomized Binary Search
A secret integer \(t\) is selected at random within the range \(1 \le t \le n\).
The goal is to guess the value of \(t\) by making repeated guesses, via integer \(g\). After a guess is made, there are three possible outcomes, in which it will be revealed that either \(g \lt t\), \(g = t\), or \(g \gt t\). Then the process can repeat as necessary.
Normally, the number of guesses required on average can be minimized with a binary search: Given a lower bound \(L\) and upper bound \(H\) (initialized to \(L = 1\) and \(H = n\)), let \(g = \lfloor(L+H)/2\rfloor\) where \(\lfloor \cdot \rfloor\) is the integer floor function. If \(g = t\), the process ends. Otherwise, if \(g \lt t\), set \(L = g+1\), but if \(g \gt t\) instead, set \(H = g - 1\). After setting the new bounds, the search process repeats, and ultimately ends once \(t\) is found. Even if \(t\) can be deduced without searching, assume that a search will be required anyway to confirm the value.
Your friend Bob believes that the standard binary search is not that much better than his randomized variant: Instead of setting \(g = \lfloor(L+H)/2\rfloor\), simply let \(g\) be a random integer between \(L\) and \(H\), inclusive. The rest of the algorithm is the same as the standard binary search. This new search routine will be referred to as a random binary search.
Given that \(1 \le t \le n\) for random \(t\), let \(B(n)\) be the expected number of guesses needed to find \(t\) using the standard binary search, and let \(R(n)\) be the expected number of guesses needed to find \(t\) using the random binary search. For example, \(B(6) = 2.33333333\) and \(R(6) = 2.71666667\) when rounded to \(8\) decimal places.
Find \(R(10^{10}) - B(10^{10})\) rounded to \(8\) decimal places.
Problem text © Project Euler, licensed under CC BY-NC-SA 4.0. Original: projecteuler.net/problem=527. Published Saturday, 26th September 2015, 04:00 pm. Solved by 894 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:
10.4 Algorithms: Gradient Descent and Newton's Method · 10.1 Convex Sets and Convex Functions · 13.2 Monte Carlo Estimation and Error Analysis · 19.14 Computational Complexity, Feasibility Estimation, and Proving Algorithms Correct · 19.4 Exact Arithmetic: Big Integers, Rationals, and Floating-Point Traps · 19.9 Graph Algorithms: BFS, DFS, Dijkstra, and Minimum Spanning Trees · 2.4 Taylor Series and Local Approximation · 5.1 Floating-Point Arithmetic, Conditioning, and Stability · 7.3 Independence, Conditional Probability, and Conditional Expectation · 7.2 Expectation, Moments, and Key Inequalities · 7.1 Probability Spaces, Random Variables, and Distributions
Concepts: numerical-methods optimization probability brute-force-reduction
Likely techniques: bfs-dfs precision-control
Learning mode
Pick how much scaffolding you want. Your choice is remembered per problem.
Understand the problem
- What exactly is the input to problem 527? Is it a bound (10^10), a supplied dataset, or a definition you must generate from?
- What is the required output - restate it precisely: a real number quoted to a stated precision, so the whole computation must control rounding error.
- 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 B(n), R(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 10^10?
- Why is brute force hard HERE specifically? Estimate the number of candidates implied by 10^10 and the cost of testing one.
- Which numerical-methods 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 numerical-methods / bfs-dfs - do you agree, and why?)
- Predict the complexity of your intended method in terms of N = 10^10, 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, B(6) = 2.") - 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
Answers are checked against a salted hash held in a separate file - not printed in this page. This prevents accidental spoilers; it is not cryptographic protection (see the build notes).
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 numerical-methods structure do the real work? Name the single observation that collapsed the search space.
Could you have reached the bfs-dfs 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 10^10 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 'bfs-dfs' 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: explore the same concept filter in the Lab
- 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.