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Diffstat (limited to 'tutorials/module_3/2_roots_optimization.md')
| -rw-r--r-- | tutorials/module_3/2_roots_optimization.md | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/tutorials/module_3/2_roots_optimization.md b/tutorials/module_3/2_roots_optimization.md index 83684d5..cc9dbb5 100644 --- a/tutorials/module_3/2_roots_optimization.md +++ b/tutorials/module_3/2_roots_optimization.md @@ -59,8 +59,8 @@ Let's consider a continuous function $f(x)$ with an unknown root $x_r$ . Using t Once we bisect the interval and found we set the new predicted root to be in the middle. We can then compare the two sections and see if there is a sign change between the bounds. Once the section with the sign change has been identified, we can repeat this process until we near the root. -![[Pasted image 20250905120647.png|500]] -As you the figure shows, the predicted root $x_r$ get's closer to the actual root each iteration. In theory this is an infinite process that can keep on going. In practice, computer precision may cause error in the result. A work-around to these problems is setting a tolerance for the accuracy. As engineers it is our duty to determine what the allowable deviation is. +![[bisection.png|500]] +As you the figure shows, the predicted root $x_r$ get's closer to the actual root each iteration. In theory this is an infinite process that ca3n keep on going. In practice, computer precision may cause error in the result. A work-around to these problems is setting a tolerance for the accuracy. As engineers it is our duty to determine what the allowable deviation is. So let's take a look at how we can write this in python. ```python |