Porisma

Worked solution

Recover a function from its gradient and one value

The diagram shows y=f′(x)y=f^{\prime}(x), passing through the origin with turning points (−3,0)(-3,0) and (−1.5,−1)(-1.5,-1). The x-axis and x=1x=1 are its two asymptotes.

(i) Find the range of xx for which y=f(x)y=f(x) is strictly increasing and concave downwards.

(ii) State the x-coordinates and natures of all stationary points of y=f(x)y=f(x).

(iii) Given f(0)=1f(0)=1, sketch y=f(x)y=f(x) for x<1x<1, clearly indicating all stationary points, asymptotes and intersections with the axes.

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Given f′ passes through (−3,0), where it touches the x-axis from below, has minimum (−1.5,−1), then crosses the origin and rises towards positive infinity at x=1 from the left. For x>1 it falls from positive infinity towards horizontal asymptote y=0.
Given f′ passes through (−3,0), where it touches the x-axis from below, has minimum (−1.5,−1), then crosses the origin and rises towards positive infinity at x=1 from the left. For x>1 it falls from positive infinity towards horizontal asymptote y=0.
Given f′ passes through (−3,0), where it touches the x-axis from below, has minimum (−1.5,−1), then crosses the origin and rises towards positive infinity at x=1 from the left. For x>1 it falls from positive infinity towards horizontal asymptote y=0.

[2009/HCI/I/3]

CJC · Discussion · Question 6

Answer

(i) x>1x>1. (ii) Stationary inflection at x=−3x=-3 and minimum (0,1)(0,1). (iii) No x-intercepts for x<1x<1; the printed sketch additionally shows vertical asymptote x=1x=1, subject to the stated qualification.

(i) Combine two gradient conditions

Strict increase requires f′>0f^{\prime}>0; concavity downwards requires f′′<0f^{\prime\prime}<0, so f′f^{\prime} must be decreasing. Both conditions hold on the right-hand branch, x>1x>1.

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(ii) Classify both stationary points

The zeros of f′f^{\prime} are x=−3x=-3 and x=0x=0.

xSign of f′ beforeSign at xSign afterNature of f
−3−0−Stationary point of inflection
0−0+Minimum

At −3-3, the derivative touches zero and changes from increasing to decreasing, so ff changes concavity without turning. At 00, its gradient changes from negative to positive.

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(iii) Position the function and retain the printed sketch

The minimum is (0,1)(0,1), which is the y-intercept. The graph decreases up to this minimum, with a stationary inflection at x=−3x=-3 and another change of concavity at x=−1.5x=-1.5; it then increases for 0<x<10<x<1. It stays at or above 11 for x<1x<1, so has no x-intercepts. The source labels the two negative x-values but does not assign their function values.

The printed answer draws x=1x=1 as a vertical asymptote of ff, with the right-hand end tending to +∞+\infty. The SVG retains that printed qualitative sketch.

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Distinguish the drawn asymptote from what is forced

The gradient graph alone does not guarantee that ff diverges at x=1x=1: a derivative can tend to +∞+\infty while having a finite improper integral. Thus the vertical asymptote in the printed answer is an additional smooth-shape interpretation; it requires a non-integrable gradient near 11. The stationary points, sign intervals, y-intercept and absence of x-intercepts above follow directly from the supplied gradient and f(0)=1f(0)=1.

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Compare your sketch

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Printed function sketch for x<1: decreasing through a stationary inflection at x=−3, changing concavity again at x=−1.5, then reaching minimum and y-intercept (0,1). It rises thereafter towards the drawn vertical asymptote x=1 and has no x-intercepts. The gradient data alone do not force that function asymptote.
Printed function sketch for x<1: decreasing through a stationary inflection at x=−3, changing concavity again at x=−1.5, then reaching minimum and y-intercept (0,1). It rises thereafter towards the drawn vertical asymptote x=1 and has no x-intercepts. The gradient data alone do not force that function asymptote.
Printed function sketch for x<1: decreasing through a stationary inflection at x=−3, changing concavity again at x=−1.5, then reaching minimum and y-intercept (0,1). It rises thereafter towards the drawn vertical asymptote x=1 and has no x-intercepts. The gradient data alone do not force that function asymptote.

Common mistakes

An unbounded derivative need not make the function itself unbounded.

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