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f( x )= y → f ′ ( x )= dy/dx

f( x )= xn → f ′ ( x )= nxn-1

f( x )= constant → f ′ ( x )= 0

f( x )= x → f ′ ( x )= 1

f( x )= ex → f ′ ( x )= ex

f( x )= eax → f ′ ( x )= a eax

f( x )= logx → f ′ ( x )= 1/x

f( x )= sinx → f ′ ( x )= cosx

f( x )= cosx → f ′ ( x )= - sinx

f( x )= tanx → f ′ ( x )= sec2 x

f( x )= secx → f ′ ( x )= secx tanx

f( x )= cosecx → f ′ ( x )= -cosecx cotx

f( x )= cotx → f ′ ( x )= - cosec2 x

f( x )= sin-1 x → f ′ ( x )= 1/√(1- x2)

f( x )= cos-1 x → f ′ ( x )= - 1/√(1+x2)

f( x )= tan-1 x → f ′ ( x )= 1/(1+x2)

f( x )= cot-1 x → f ′ ( x )= - 1/(1+x2)

f( x )= cosec-1 x → f ′ ( x )= - 1/|x| √(x2 -1)

f( x )= Sec-1 x → f ′ ( x )= 1/|x| √(x2 -1)

f( x )= UV → f ′ ( x )= UV′ + VU′(Product rule)

f( x )= U/V → f ′ ( x )= (VU′ - UV′) / V2 (Quotient rule)

f( x )= f(g(x)) → f ′ ( x )= f ′ (g) * g′ (x) (Chain rule)

### Partial Differentiation Identities

if f( x(r,s), y(r,s) )

df / dr = df / dx * dx / DR + df / dy * dy / DR

df / ds = df / dx * dx / Ds + df / dy * dy / Ds

if f( x(r,s) )

df / DR = df / dx * dx / DR

df / Ds = df / dx * dx / Ds

directional derivative

df(x,y) / d(Xi sub a) = f1(x,y) cos(a) + f2(x,y) sin(a)

(Xi sub a) = angle counterclockwise from pos. x axis.

Asymptotes

Definition of a horizontal asymptote: The line y = y0 is a "horizontal asymptote" of f(x) if and only if f(x) approaches y0 as x approaches + or - .

Definition of a vertical asymptote: The line x = x0 is a "vertical asymptote" of f(x) if and only if f(x) approaches + or - as x approaches x0 from the left or from the right.

Definition of a slant asymptote: the line y = ax + b is a "slant asymptote" of f(x) if and only if lim (x-->+/-) f(x) = ax + b.

Concavity

Definition of a concave up curve: f(x) is "concave up" at x0 if and only if f '(x) is increasing at x0

Definition of a concave down curve: f(x) is "concave down" at x0 if and only if f '(x) is decreasing at x0

The second derivative test: If f ''(x) exists at x0 and is positive, then f ''(x) is concave up at x0. If f ''(x0) exists and is negative, then f(x) is concave down at x0. If f ''(x) does not exist or is zero, then the test fails.

Critical Points

Definition of a critical point: a critical point on f(x) occurs at x0 if and only if either f '(x0) is zero or the derivative doesn't exist.

Extrema (Maxima and Minima)

Local (Relative) Extrema

Definition of a local maxima: A function f(x) has a local maximum at x0 if and only if there exists some interval I containing x0 such that f(x0) >= f(x) for all x in I.

Definition of a local minima: A function f(x) has a local minimum at x0 if and only if there exists some interval I containing x0 such that f(x0) <= f(x) for all x in I.

Occurrence of local extrema: All local extrema occur at critical points, but not all critical points occur at local extrema.

The first derivative test for local extrema: If f(x) is increasing (f '(x) > 0) for all x in some interval (a, x0] and f(x) is decreasing (f '(x) < 0) for all x in some interval [x0, b), then f(x) has a local maximum at x0. If f(x) is decreasing (f '(x) < 0) for all x in some interval (a, x0] and f(x) is increasing (f '(x) > 0) for all x in some interval [x0, b), then f(x) has a local minimum at x0.

The second derivative test for local extrema: If f '(x0) = 0 and f ''(x0) > 0, then f(x) has a local minimum at x0. If f '(x0) = 0 and f ''(x0) < 0, then f(x) has a local maximum at x0.

Absolute Extrema

Definition of absolute maxima: y0 is the "absolute maximum" of f(x) on I if and only if y0 >= f(x) for all x on I.

Definition of absolute minima: y0 is the "absolute minimum" of f(x) on I if and only if y0 <= f(x) for all x on I.

The extreme value theorem: If f(x) is continuous in a closed interval I, then f(x) has at least one absolute maximum and one absolute minimum in I.

Occurrence of absolute maxima: If f(x) is continuous in a closed interval I, then the absolute maximum of f(x) in I is the maximum value of f(x) on all local maxima and endpoints on I.

Occurrence of absolute minima: If f(x) is continuous in a closed interval I, then the absolute minimum of f(x) in I is the minimum value of f(x) on all local minima and endpoints on I.

Alternate method of finding extrema: If f(x) is continuous in a closed interval I, then the absolute extrema of f(x) in I occur at the critical points and/or at the endpoints of I.
(This is a less specific form of the above.)

Increasing/Decreasing Functions

Definition of an increasing function: A function f(x) is "increasing" at a point x0 if and only if there exists some interval I containing x0 such that f(x0) > f(x) for all x in I to the left of x0 and f(x0) < f(x) for all x in I to the right of x0.

Definition of a decreasing function: A function f(x) is "decreasing" at a point x0 if and only if there exists some interval I containing x0 such that f(x0) < f(x) for all x in I to the left of x0 and f(x0) > f(x) for all x in I to the right of x0.

The first derivative test: If f '(x0) exists and is positive, then f '(x) is increasing at x0. If f '(x) exists and is negative, then f(x) is decreasing at x0. If f '(x0) does not exist or is zero, then the test tells fails.

Inflection Points

Definition of an inflection point: An inflection point occurs on f(x) at x0 if and only if f(x) has a tangent line at x0 and there exists and interval I containing x0 such that f(x) is concave up on one side of x0 and concave down on the other side.

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