Local Maximum Condition
Imagine hiking in a mountain range. You reach a point where, for a few steps in any direction, the ground drops away. You may not be the highest peak in the whole range, but right here every step goes downhill. That's a local maximum: a point higher than all nearby points.
The Intuition First
On a smooth, wavy curve, a local maximum is a "hilltop" — a point where the function peaks, then falls on both sides. Zoom in and the values just to the left and right are both lower.
"Local" means "in a small neighbourhood." The function might have higher values elsewhere (a global maximum), but that doesn't matter — a local maximum is king of its own tiny kingdom.
The Precise Mathematical Statement
Let f be a real-valued function on a domain D⊆R. A point c∈D is a local maximum if there exists some δ>0 such that for every x in the domain within distance δ of c:
f(x)≤f(c)
That is, on some open interval (c−δ,c+δ), f(c) is the largest value.
The inequality is f(x)≤f(c), not f(x)<f(c). If equality holds for some x=c (a flat plateau), it's still a local maximum — just not a strict one.
The First Derivative Test
If f is differentiable at c and c is a local maximum, then:
This is the critical point condition — the tangent is horizontal. But f′(c)=0 is necessary, not sufficient: a horizontal tangent could also be a local minimum or a saddle point (like f(x)=x3 at x=0).
A common mistake: assuming f′(c)=0 guarantees a local extremum. It does not. Check the sign change of the derivative around c, or use the second derivative test.
The Second Derivative Test
If f′(c)=0 and f′′(c)<0, then c is a local maximum: a negative second derivative means f is concave down at c — curving down like an upside-down bowl. If f′′(c)>0, it's a local minimum. If f′′(c)=0, the test is inconclusive.
A Concrete Example
Consider f(x)=−x2+4x−3. …