First perform the inner integral, which gives the vertical width of the region:
Using gives
At the intersection, , so
The region lies above the parabola and below the upper semicircle . Their intersections satisfy , hence with . In particular, . The region is
It is symmetric about the axis, with endpoints ; at it extends from to .
Figure 1.
Integration region between a parabola and the upper unit semicircle
. Details.
Height above the surface is . Integrate the density over all exterior spherical shells:
Repeated integration by parts, or the substitution , gives
Expand and use these moments to obtain the mass of an exponential spherical atmosphere:
For , the leading term is surface area times column mass ; the other terms retain the finite-curvature corrections.
The function is likewise positive and decreasing on , with . Now
The integral test for convergence shows that converges. This comparison integral determines convergence, not the numerical value of the series itself.
The function is positive and decreasing for , since . The integral test for convergence therefore applies. Its comparison improper integral is
Consequently diverges.
Separate one factor of the hyperbolic sine and use the hyperbolic Pythagorean identity . With and , integration by substitution gives
For real , the hyperbolic cosine never vanishes. Differentiating the result gives , checking the sign of the reciprocal term.
Use integration by substitution with , so . For , also , and
The domain condition explains why no absolute value is needed around the inner logarithm.
The same integration by substitution gives the expected value
The remaining integrand is an odd function. Its integral is absolutely convergent, since and is integrable. Thus cancellation under symmetry in integration is legitimate, rather than merely a principal-value calculation. Consequently
Equivalently, the logistic distribution is symmetric about , and its exponentially decaying tails ensure that its expected value exists.
For , integrate the probability density function to obtain the cumulative distribution function
If , the requested probability is . Since the hyperbolic tangent is odd, this gives
The second case matters because the question places no sign restriction on : the event is empty when its upper endpoint is nonpositive. Using an oriented integral from to a negative would give a negative number, not a probability.
A probability density function must be nonnegative. The printed condition is therefore too weak: for the given prefactor makes the function negative everywhere. The location-scale form of the logistic distribution requires , which is assumed in the remaining parts.
Set , so . The normalization of a probability distribution follows from the antiderivative of the squared hyperbolic secant:
For completeness, the same expression with integrates to , since the transformed limits reverse. If a signed scale were desired, replacing its prefactor by would give a valid density, but that is a correction to the printed formula.
The Maclaurin series give and . Inverting the latter using the geometric series gives
Multiplying the two expansions yields
The remainder is because the product already generates an term from .
Use the Maclaurin series and . The terms through fourth degree give
In particular the cubic coefficient cancels; it has not been omitted accidentally.
For , the local minimum at the origin has . Its full level set is
The connected component containing the minimum is just the isolated origin. The same level also has the outer branches for ; these are different connected components, not curves approaching the origin.
Both saddle points have . Their common level set factors as
Its explicit equations are therefore
The two parabolas cross at each saddle, explaining the change of contour connectivity there. Each displayed equation can be restricted to the square of part (ii) if only that sketch window is wanted.
Because the exponential function is strictly increasing, the level sets are exactly those of . For they satisfy
There are no contours for , the level is the origin, and gives nested closed ovals symmetric about both axes. Their stronger fourth-power growth in makes them relatively narrower horizontally as the level rises.
For , the contour equation becomes
The central local minimum is surrounded by closed ovals for , accompanied by outer open branches. At , the level passes through the two saddle points and changes its connectivity. Levels have only outer branches, while levels above the saddle value have an upper and a lower branch. The sketch is restricted to the requested square; labels give , so they identify the corresponding contours of without exponentially large labels.
Figure 1.
Contours for the two parameter signs; labels are log f and dots mark stationary points
. Details.
Put , so . Since and , its stationary points satisfy
The origin occurs for every real . There are two additional points only for , at .
At a stationary point, the Hessian matrix simplifies because :
At the origin this is , so is a strict local minimum for every , with . If it is also the unique global minimum, since with equality only there. For , the two extra points have , so their Hessian has opposite-sign eigenvalues. Thus
There are no local maxima. When , the origin is not a global minimum: as , but never attains zero.
Write , , at the stationary point, and let . Under the usual second-order Taylor expansion hypothesis, the second-derivative test gives
Indeed the quadratic term is . For , completing its square gives . The two coefficients have the same positive sign, the same negative sign, or opposite signs in the respective cases. A negative also forces the two Hessian eigenvalues to have opposite signs, including the case .
There is a qualification to the printed assumptions: existence of second partial derivatives only at the point does not ensure the needed quadratic approximation. Continuous second partial derivatives in a neighborhood are sufficient. The existence of second partial derivatives does not justify the Hessian test: for example, the function away from the origin, extended by , has Hessian at the origin but and . Thus the classification above uses the standard additional regularity assumption, which the smooth function in part (b) satisfies.
An interior stationary point has vanishing gradient:
For a differentiable function, this is the necessary first-order condition for an interior local minimum or local maximum.
The nonzero factor can be cancelled in the local differential equation, leaving . On an interval with ,
Hence the explicit general solution is
on any interval that avoids the excluded origin. It also follows from fixing the local angle potential, with slope . As parameterized curves there are also vertical rays , which cannot be represented as single-valued functions . Geometrically the connected integral curves are rays, not lines continued through the undefined origin.
On a chart with , set
The chain rule verifies both components:
A coordinate-independent description of the local potential is a continuous branch of , the complex argument of the scaled coordinates. Such branches can also cover points where , using a different angular chart. Continuing once around the missing origin changes that angle by a nonzero multiple of , so there is no global single-valued on the full stated domain.

Pinned article: Introduction to the OurBigBook Project

Welcome to the OurBigBook Project! Our goal is to create the perfect publishing platform for STEM subjects, and get university-level students to write the best free STEM tutorials ever.
Everyone is welcome to create an account and play with the site: ourbigbook.com/go/register. We belive that students themselves can write amazing tutorials, but teachers are welcome too. You can write about anything you want, it doesn't have to be STEM or even educational. Silly test content is very welcome and you won't be penalized in any way. Just keep it legal!
We have two killer features:
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    Articles of different users are sorted by upvote within each article page. This feature is a bit like:
    • a Wikipedia where each user can have their own version of each article
    • a Q&A website like Stack Overflow, where multiple people can give their views on a given topic, and the best ones are sorted by upvote. Except you don't need to wait for someone to ask first, and any topic goes, no matter how narrow or broad
    This feature makes it possible for readers to find better explanations of any topic created by other writers. And it allows writers to create an explanation in a place that readers might actually find it.
    Figure 1.
    Screenshot of the "Derivative" topic page
    . View it live at: ourbigbook.com/go/topic/derivative
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    This way you can be sure that even if OurBigBook.com were to go down one day (which we have no plans to do as it is quite cheap to host!), your content will still be perfectly readable as a static site.
    Figure 5. . You can also edit articles on the Web editor without installing anything locally.
    Video 3.
    Edit locally and publish demo
    . Source. This shows editing OurBigBook Markup and publishing it using the Visual Studio Code extension.
  3. https://raw.githubusercontent.com/ourbigbook/ourbigbook-media/master/feature/x/hilbert-space-arrow.png
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    Figure 6.
    Dynamic article tree with infinitely deep table of contents
    .
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