Reading this result: Each step commits a local truncation error — O(h²) for Euler, O(h³) for Heun, O(h⁵) for RK4 — and those accumulate over ~8 steps into the global error shown (one order lower: O(h), O(h²), O(h⁴)). That extra power of h is why halving the step cuts Euler's error ~2×, Heun's ~4×, and RK4's ~16×, so RK4 hugs the true y(x) far more tightly for the same work.
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How it works
Each method advances dy/dx = f(x, y) forward in fixed steps of size h, but they differ in how they estimate the slope over each step. Euler uses one slope evaluation (O(h) global error), Heun averages two (O(h²)), and RK4 blends four (O(h⁴)). Overlaid against the known analytic solution, the gap is obvious: at large h Euler drifts or — on a stiff ODE — blows up entirely, while RK4 stays glued to the true curve. Halving the step cuts Euler's error roughly 2×, Heun's 4×, and RK4's 16×, the payoff of higher-order accuracy that makes RK4 the workhorse of numerical ODE solving.
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