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Calculus 3 Cheat Sheet: Quick Review, Cheat Sheet of Calculus

Quick and useful review on Calculus 3: all main notions, formulas and theorems in one sheet.

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2019/2020

Uploaded on 10/09/2020

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Derivatives
Dxex=ex
Dxsin(x)=cos(x)
Dxcos(x)=sin(x)
Dxtan(x)=sec
2(x)
Dxcot(x)=csc2(x)
Dxsec(x)=sec(x)tan(x)
Dxcsc(x)=csc(x)cot(x)
Dxsin1=1
p1x2,x2[1,1]
Dxcos1=1
p1x2,x2[1,1]
Dxtan1=1
1+x2,
2x
2
Dxsec1=1
|x|px21,|x|>1
Dxsinh(x)=cosh(x)
Dxcosh(x)=sinh(x)
Dxtanh(x)=sech2(x)
Dxcoth(x)=csch2(x)
Dxsech(x)=sech(x)tanh(x)
Dxcsch(x)=csch(x)coth(x)
Dxsinh1=1
px2+1
Dxcosh1=1
px21,x> 1
Dxtanh1=1
1x21<x<1
Dxsech1=1
xp1x2,0<x<1
Dxln(x)=1
x
Integrals
R1
xdx = ln |x|+c
Rexdx =ex+c
Raxdx =1
lnaax+c
Reaxdx =1
aeax +c
R1
p1x2dx = sin1(x)+c
R1
1+x2dx = tan1(x)+c
R1
xpx21dx =sec
1(x)+c
Rsinh(x)dx = cosh(x)+c
Rcosh(x)dx = sinh(x)+c
Rtanh(x)dx = ln |cosh(x)|+c
Rtanh(x)sech(x)dx =sech(x)+c
Rsech2(x)dx = tanh(x)+c
Rcsch(x)coth(x)dx =csch(x)+c
Rtan(x)dx =ln|cos(x)|+c
Rcot(x)dx = ln |sin(x)|+c
Rcos(x)dx = sin(x)+c
Rsin(x)dx =cos(x)+c
R1
pa2u2dx = sin1(u
a)+c
R1
a2+u2dx =1
atan1u
a+c
Rln(x)dx =(xln(x)) x+c
U-Substitution
Let u=f(x)(canbemorethanone
variable).
Determine: du =f(x)
dx dx and solve for
dx.
Then, if a definite integral, substitute
the bounds for u=f(x)ateachbounds
Solve the integral using u.
Integration by Parts
Rudv =uv Rvdu
Fns and Identities
sin(cos1(x)) = p1x2
cos(sin1(x)) = p1x2
sec(tan1(x)) = p1+x2
tan(sec1(x))
=(
px21ifx1)
=(px21if x 1)
sinh1(x)=lnx+px2+1
sinh1(x)=lnx+px21,x1
tanh1(x)=1
2lnx+1+x
1x,1<x<1
sech1(x)=ln[
1+p1x2
x],0<x1
sinh(x)=exex
2
cosh(x)=ex+ex
2
Trig Identities
sin2(x)+cos
2(x)=1
1+tan
2(x)=sec
2(x)
1+cot
2(x)=csc
2(x)
sin(x±y)=sin(x)cos(y)±cos(x)sin(y)
cos(x±y)=cos(x)cos(y)±sin(x)sin(y)
tan(x±y)= tan(x)±tan(y)
1tan(x)tan(y)
sin(2x)=2sin(x) cos(x)
cos(2x)=cos
2(x)sin2(x)
cosh(n2x)sinh2x=1
1+tan
2(x)=sec
2(x)
1+cot
2(x)=csc
2(x)
sin2(x)=1cos(2x)
2
cos2(x)=1+cos(2x)
2
tan2(x)=1cos(2x)
1+cos(2x)
sin(x)=sin(x)
cos(x)=cos(x)
tan(x)=tan(x)
Calculus 3 Concepts
Cartesian coords in 3D
given two points:
(x1,y
1,z
1)and(x2,y
2,z
2),
Distance between them:
p(x1x2)2+(y1y2)2+(z1z2)2
Midpoint:
(x1+x2
2,y1+y2
2,z1+z2
2)
Sphere with center (h,k,l) and radius r:
(xh)2+(yk)2+(zl)2=r2
Vectors
Vect or: ~u
Unit Vector: ˆu
Magnitude: ||~u|| =qu2
1+u2
2+u2
3
Unit Vector: ˆu=~u
||~u||
Dot Product
~u·~v
Produces a Scalar
(Geometrically, the dot product is a
vector pro jection)
~u=<u
1,u
2,u
3>
~v=<v
1,v
2,v
3>
~u·~v=~
0 means the two vectors are
Perpendicular is the angle between
them.
~u·~v=||~u|| ||~v|| cos()
~u·~v=u1v1+u2v2+u3v3
NOTE:
ˆu·ˆv= cos()
||~u||2=~u·~u
~u·~v= 0 when ?
Angle Between ~uand ~v:
= cos1(~u·~v
||~u|| ||~v|| )
Projection of ~uonto ~v:
pr~v~u=( ~u·~v
||~v||2)~v
Cross Product
~u~v
Produces a Vector
(Geometrically, the cross product is the
area of a paralellogram with sides ||~u||
and ||~v||)
~u=<u
1,u
2,u
3>
~v=<v
1,v
2,v
3>
~u~v=
ˆ
iˆ
jˆ
k
u1u2u3
v1v2v3
~u~v=~
0 means the vectors are paralell
Lines and Planes
Equation of a Plane
(x0,y
0,z
0) is a point on the plane and
<A,B,C>is a normal vector
A(xx0)+B(yy0)+C(zz0)=0
<A,B,C>·<xx0,yy0,zz0>=0
Ax +By +Cz =Dwhere
D=Ax0+By0+Cz0
Equation of a line
AlinerequiresaDirectionVector
~u=<u
1,u
2,u
3>and a point
(x1,y
1,z
1)
then,
a parameterization of a line could be:
x=u1t+x1
y=u2t+y1
z=u3t+z1
Distance from a Point to a Plane
The distance from a point (x0,y
0,z
0)to
a plane Ax+By+Cz=D can be expressed
by the formula:
d=|Ax0+By0+Cz0D|
pA2+B2+C2
Coord Sys Conv
Cylindrical to Rectangular
x=rcos()
y=rsin()
z=z
Rectangular to Cylindrical
r=px2+y2
tan()=y
x
z=z
Spherical to Rectangular
x=sin()cos()
y=sin()sin()
z=cos()
Rectangular to Spherical
=px2+y2+z2
tan()=y
x
cos()= z
px2+y2+z2
Spherical to Cylindrical
r=sin()
=
z=cos()
Cylindrical to Spherical
=pr2+z2
=
cos()= z
pr2+z2
Surfaces
Ellipsoid
x2
a2+y2
b2+z2
c2=1
Hyperboloid of One Sheet
x2
a2+y2
b2z2
c2=1
(Major Axis: z because it follows - )
Hyperboloid of Two Sheets
z2
c2x2
a2y2
b2=1
(Major Axis: Z because it is the one not
subtracted)
Elliptic Paraboloid
z=x2
a2+y2
b2
(Major Axis: z because it is the variable
NOT squared)
Hyperbolic Paraboloid
(Major Axis: Z axis because it is not
squared)
z=y2
b2x2
a2
Elliptic Cone
(Major Axis: Z axis because it’s the only
one being subtracted)
x2
a2+y2
b2z2
c2=0
Cylinder
1 of the variables is missing
OR
(xa)2+(yb2)=c
(Major Axis is missing variable)
Partial Derivatives
Partial Derivatives are simply holding all
other variables constant (and act like
constants for the derivative) and only
taking the derivative with respect to a
given variable.
Given z=f(x,y), the partial derivative of
zwithrespecttoxis:
fx(x,y)=zx=@z
@x=@f(x,y)
@x
likewise for partial with respect to y:
fy(x,y)=zy=@z
@y=@f(x,y)
@y
Notation
For fxyy, work ”inside to outside” fx
then fxy,thenfxyy
fxyy =@3f
@x@2y,
For @3f
@x@2y, work right to left in the
denominator
Gradients
The Gradient of a function in 2 variables
is rf=<f
x,f
y>
The Gradient of a function in 3 variables
is rf=<f
x,f
y,f
z>
Chain Rule(s)
Take the Partialderivative with respect
to the first-order variables of the
function times the partial (or normal)
derivative of the first-order variable to
the ultimate variable you are looking for
summed with the same process for other
first-order variables this makes sense for.
Example:
let x = x(s,t), y = y(t) and z = z(x,y).
zthenhasfirstpartialderivative:
@z
@xand @z
@y
xhasthepartialderivatives:
@x
@sand @x
@t
and y has the derivative:
dy
dt
In this case (with z containing x and y
as well as x and y both containing s and
t), the chain rule for @z
@sis @z
@s=@z
@x
@x
@s
The chain rule for @z
@tis
@z
@t=@z
@x
@x
@t+@z
@y
dy
dt
Note: the use of ”d” instead of @ with
the function of only one independent
varia ble
Limits and Continuity
Limits in 2 or more variables
Limits taken over a vectorized limit just
evaluate separately for each component
of the limit.
Strategies to show limit exists
1. Plug in Numbers, Everything is Fine
2. Algebraic Manipulation
-factoring/dividing out
-use trig identites
3. Change to polar coords
if(x, y)!(0,0) ,r!0
Strategies to show limit DNE
1. Show limit is dierent if approached
from dierent paths
(x=y, x=y2,etc.)
2. Switch to Polar coords and show the
limit DNE.
Continunity
Afn,z=f(x,y), is continuous at (a,b)
if
f(a,b)=lim
(x,y)!(a,b)f(x,y )
Which means:
1. The limit exists
2. The fn value is defined
3. They are the same value
Directional Derivatives
Let z=f(x,y) be a fuction, (a,b) ap point
in the domain (a valid input point) and
ˆua unit vector (2D).
The Directional Derivative is then the
derivative at the point (a,b) in the
direction of ˆuor:
D~uf(a, b)=ˆu·rf(a,b)
This will return a scalar. 4-D version:
D~uf(a, b, c)=ˆu·rf(a,b, c)
Tangent Planes
let F(x,y,z) = k be a surface and P =
(x0,y
0,z
0)beapointonthatsurface.
Equation of a Tangent Plane:
rF(x0,y
0,z
0)·<xx0,yy0,zz0>
Approximations
let z=f(x,y)beadierentiable
function total dierential of f = dz
dz =rf·<dx,dy>
This is the approximate change in z
The actual change in z is the dierence
in z values:
z=zz1
Maxima and Minima
Internal Points
1. Take the Partial Derivatives with
respect to X and Y (fxand fy)(Canuse
gradient)
2. Set derivatives equal to 0 and use to
solve system of equations for x and y
3. Plug back into original equation for z.
Use Second Derivative Testfor whether
points are local max, min, or saddle
Second Partial Derivative Test
1. Find all (x,y) points such that
rf(x,y)=~
0
2. Let D=fxx(x, y )fyy(x, y)f2
xy(x, y)
IF (a) D >0ANDfxx<0,f(x,y) is
local max value
(b) D >0ANDfxx(x,y)>0 f(x,y) is
local min value
(c) D <0, (x,y,f(x,y)) is a saddle point
(d) D = 0, test is inconclusive
3. Determine if any boundary point
gives min or max. Typically, we have to
parametrize boundary and then reduce
to a Calc 1 type of min/max problem to
solve.
The following only apply only if a
boundary is given
1. check the corner points
2. Check each line (0 x5would
give x=0 and x=5 )
On Bounded Equations, this is the
global min and max...second derivative
test is not needed.
Lagrange Multipliers
Given a function f(x,y) with a constraint
g(x,y), solve the following system of
equations to find the max and min
points on the constraint (NOTE: may
need to also find internal points.):
rf=rg
g(x,y)=0(orkif given)
Double Integrals
With Respect to the xy-axis, if taking an
integral,
RRdydx is cutting in vertical rectangles,
RRdxdy is cutting in horizontal
rectangles
Polar Coordinates
When using polar coordinates,
dA =rdrd
Surface Area of a Curve
let z = f(x,y) be continuous over S (a
closed Region in 2D domain)
Then the surface area of z = f(x,y) over
S is:
SA =RR
Sqf2
x+f2
y+1dA
Triple Integrals
RRR
sf(x,y, z )dv =
Ra2
a1R2(x)
1(x)R 2(x,y)
1(x,y)f(x,y , z)dzdydx
Note: dv can be exchanged for dxdydz in
any order, but you must then choose
your limits of integration according to
that order
Jacobian Method
RR
Gf(g(u,v),h(u,v ))|J(u, v)|dudv =
RR
Rf(x,y)dxdy
J(u,v)=
@x
@u
@x
@v
@y
@u
@y
@v
Common Jacobians:
Rect. to Cylindrical: r
Rect. to Spherical: 2sin()
Vector Fields
let f(x,y, z )beascalarfieldand
~
F(x,y, z )=
M(x,y, z )ˆ
i+N(x,y, z )ˆ
j+P(x,y, z )ˆ
kbe
a vector field,
Grandient of f = rf=<@f
@x,@f
@y,@f
@z>
Divergence of ~
F:
r·~
F=@M
@x+@N
@y+@P
@z
Curl of ~
F:
r⇥~
F=
ˆ
iˆ
jˆ
k
@
@x
@
@y
@
@z
MNP
Line Integrals
Cgivenbyx=x(t),y=y(t),t2[a, b]
Rcf(x,y)ds =Rb
af(x(t),y(t))ds
where ds =q(dx
dt )2+(dy
dt )2dt
or q1+(dy
dx)2dx
or q1+(dx
dy)2dy
To evaluatea Line Integral,
·get a paramaterized version of the line
(usually in terms of t, though in
exclusive terms of x or y is ok)
·evaluate for the derivatives needed
(usually dy, dx, and/or dt)
·plug in to original equation to get in
terms of the independant variable
·solve integral
Work
Let ~
F=Mˆ
i+ˆ
j+ˆ
k(force)
M=M(x,y, z ),N =N(x, y, z),P =
P(x,y, z )
(Literally)d~r=dxˆ
i+dyˆ
j+dzˆ
k
Work w=Rc~
F·d~r
(Work done by movinga p article over
curve C with force ~
F)
Independence of Path
Fund T hm of Lin e Integ ral s
Ciscurvegivenby~r(t),t2[a, b];
~r0(t) exists. If f(~r) is continuously
dierentiable on an open set containing
C, then Rcrf(~r)·d~r=f(~
b)f(~a)
Equivalent Conditions
~
F(~r) continuous on open connected set
D. Then,
(a)~
F=rffor some fn f. (if ~
Fis
conservative)
,(b)Rc~
F(~r)·d~risindep.ofpathinD
,(c)Rc~
F(~r)·d~r= 0 for all closed paths
in D.
Conservation Theorem
~
F=Mˆ
i+Nˆ
j+Pˆ
kcontinuously
dierentiable on open, simply connected
set D.
~
Fconservative ,r⇥~
F=~
0
(in 2D r⇥~
F=~
0iMy=Nx)
Green’s Theorem
(method of changing line integral for
double integral - Use for Flux and
Circulation across 2D curve and line
integrals over a closed boundary)
HMdyNdx=RR
R(Mx+Ny)dxdy
HMdx+Ndy=RR
R(NxMy)dxdy
Let:
·Rbearegioninxy-plane
·C is simple, closed curve enclosing R
(w/ paramerization ~r(t))
·~
F(x,y)=M(x, y )ˆ
i+N(x,y)ˆ
jbe
continuously dierentiable over R[C.
Form 1 : Flu x Acr oss B ounda ry
~n= unit normal vector to C
Hc~
F·~n=RR
Rr·~
FdA
,HMdyNdx=RR
R(Mx+Ny)dxdy
Form 2 : Cir cul ati on Alon g
Boundary
Hc~
F·d~r=RR
Rr⇥~
F·ˆudA
,HMdx+Ndy=RR
R(NxMy)dxdy
Area of R
A=H(1
2ydx+1
2xdy)
Gauss’ Divergence Thm
(3D Analog of Green’s Theorem - Use
for Flux over a 3D surface) Let:
·~
F(x,y, z ) be vector field continuously
dierentiable in solid S
·S is a 3D solid ·@Sboundary of S (A
Surface)
·ˆnunit outer normal to @S
Then,
RR
@S~
F(x,y, z )·ˆndS =RRR
Sr·~
FdV
(dV = dxdydz)
Surface Integrals
Let
·R be closed, bounded region in xy-plane
·fbeafnwithfirstorderpartial
derivatives on R
·GbeasurfaceoverRgivenby
z=f(x,y)
·g(x,y, z )=g(x, y, f(x, y )) is cont. on R
Then,
RR
Gg(x,y, z )dS =
RR
Rg(x,y, f (x, y))dS
where dS =qf2
x+f2
y+1dydx
Flux of ~
Facross G
RR
G~
F·ndS =
RR
R[MfxNfy+P]dxdy
where:
·~
F(x,y, z )=
M(x,y, z )ˆ
i+N(x,y, z )ˆ
j+P(x,y, z )ˆ
k
·G is surface f(x,y)=z
·~nis upward unit normal on G.
·f(x,y) has continuous 1st order partial
derivatives
Unit Circle
(cos, sin)
Other Information
pa
pb=pa
b
Where a Cone is defined as
z=pa(x2+y2),
In Spherical Coordinates,
= cos1(qa
1+a)
Right Circular Cylinder:
V=r2h,S A =r2+2rh
limn!inf(1 + m
n)pn =emp
Law of Cosines:
a2=b2+c22bc(cos())
Stokes Theorem
Let:
·S be a 3D surface
·~
F(x,y, z )=
M(x,y, z )ˆ
i+N(x,y, z )ˆ
j+P(x,y, z )ˆ
l
·M,N,P have continuous 1st order partial
derivatives
·C is piece-wise smooth, simple, closed,
curve, positively oriented
·ˆ
Tis unit tangent vector to C.
Then,
H~
Fc·ˆ
TdS=RR
s(r⇥~
F)·ˆndS =
RR
R(r⇥~
F)·~ndxdy
Remember:
H~
F·~
Tds=Rc(Mdx+Ndy+Pdz)
Originally Written By Daniel Kenner for
MATH 2210 at the Universityof Utah.
Source code available at
https://github.com/keytotime/Calc3 CheatSheet
Thanks to Kelly Macarthur for Teachingand
Providing Notes.

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Derivatives

D

x

e

x

= e

x

Dx sin(x) = cos(x)

Dx cos(x) = sin(x)

Dx tan(x) = sec

2

(x)

D

x

cot(x) = csc

2 (x)

D

x

sec(x) = sec(x) tan(x)

D

x

csc(x) = csc(x) cot(x)

D

x

sin

1

1 p

1 x 2

, x 2 [ 1 , 1]

Dx cos

1

=

1 p

1 x

2

, x 2 [ 1 , 1]

Dx tan

1

=

1

1+x

2

2

 x 

2

Dx sec

1

=

1

|x|

p

x

2 1

, |x| > 1

D

x

sinh(x) = cosh(x)

D

x

cosh(x) = sinh(x)

D

x

tanh(x) = sech

2

(x)

Dx coth(x) = csch

2

(x)

Dxsech(x) = sech(x) tanh(x)

D

x

csch(x) = csch(x) coth(x)

Dx sinh

1

=

1

p

x

2

D

x

cosh

1

=

1

p

x

2 1

, x > 1

D

x

tanh

1

=

1

1 x

2

1 < x < 1

D

x

sech

1

=

1

x

p

1 x

2

, 0 < x < 1

D

x

ln(x) =

1

x

Integrals

R

1

x

dx = ln |x| + c R

e

x dx = e

x

  • c R

a

x

dx =

1

ln a

a

x

  • c R

e

ax dx =

1

a

e

ax

  • c

R

1 p

1 x

2

dx = sin

1 (x) + c

R

1

1+x

2

dx = tan

1 (x) + c

R

1

x

p

x

2 1

dx = sec

1 (x) + c

R

sinh(x)dx = cosh(x) + c R

cosh(x)dx = sinh(x) + c R

tanh(x)dx = ln | cosh(x)| + c R

tanh(x)sech(x)dx = sech(x) + c R

sech

2

(x)dx = tanh(x) + c R

csch(x) coth(x)dx = csch(x) + c R

tan(x)dx = ln | cos(x)| + c R

cot(x)dx = ln | sin(x)| + c R

cos(x)dx = sin(x) + c R

sin(x)dx = cos(x) + c

R

1

p

a

2 u

2

dx = sin

1

(

u

a

) + c

R

1

a

2 +u

2

dx =

1

a

tan

1 u

a

  • c

R

ln(x)dx = (xln(x)) x + c

U-Substitution

Let u = f (x) (can be more than one

variable).

Determine: du =

f (x)

dx

dx and solve for

dx.

Then, if a definite integral, substitute

the bounds for u = f (x) at each bounds

Solve the integral using u.

Integration by Parts R

udv = uv

R

vdu

Fns and Identities

sin(cos

1

(x)) =

p

1 x

2

cos(sin

1 (x)) =

p

1 x

2

sec(tan

1

(x)) =

p

1 + x

2

tan(sec

1

(x))

p

x

2

1 if x 1)

p

x

2 1 if x  1)

sinh

1

(x) = ln x +

p

x

2

  • 1

sinh

1

(x) = ln x +

p

x

2 1 , x 1

tanh

1

(x) =

1

2

ln x +

1+x

1 x

, 1 < x < 1

sech

1

(x) = ln[

1+

p

1 x

2

x

], 0 < x  1

sinh(x) =

e

x e

x

2

cosh(x) =

e

x

+e

x

2

Trig Identities

sin

2

(x) + cos

2

(x) = 1

1 + tan

2

(x) = sec

2

(x)

1 + cot

2

(x) = csc

2

(x)

sin(x ± y) = sin(x) cos(y) ± cos(x) sin(y)

cos(x ± y) = cos(x) cos(y) ± sin(x) sin(y)

tan(x ± y) =

tan(x)±tan(y)

1 ⌥tan(x) tan(y)

sin(2x) = 2 sin(x) cos(x)

cos(2x) = cos

2

(x) sin

2

(x)

cosh(n

2

x) sinh

2

x = 1

1 + tan

2

(x) = sec

2

(x)

1 + cot

2

(x) = csc

2

(x)

sin

2

(x) =

1 cos(2x)

2

cos

2

(x) =

1+cos(2x)

2

tan

2 (x) =

1 cos(2x)

1+cos(2x)

sin(x) = sin(x)

cos(x) = cos(x)

tan(x) = tan(x)

Calculus 3 Concepts

Cartesian coords in 3D

given two points:

(x 1 , y 1 , z 1 ) and (x 2 , y 2 , z 2 ),

Distance between them: p

(x 1

x 2

2

  • (y 1

y 2

2

  • (z 1

z 2

2

Midpoint:

x 1

+x 2

2

y 1

+y 2

2

z 1

+z 2

2

Sphere with center (h,k,l) and radius r:

(x h)

2

  • (y k)

2

  • (z l)

2

= r

2

Vectors

Vector: ~u

Unit Vector: ˆu

Magnitude: ||~u|| =

q

u

2

1

  • u

2

2

  • u

2

3

Unit Vector: ˆu =

~u

||~u||

Dot Product

~u · ~v

Produces a Scalar

(Geometrically, the dot product is a

vector projection)

~u =< u 1

, u 2

, u 3

~v =< v 1

, v 2

, v 3

~u · ~v =

0 means the two vectors are

Perpendicular ✓ is the angle between

them.

~u · ~v = ||~u|| ||~v|| cos(✓)

~u · ~v = u 1

v 1

  • u 2

v 2

  • u 3

v 3

NOTE:

ˆu · vˆ = cos(✓)

||~u||

2

= ~u · ~u

~u · ~v = 0 when?

Angle Between ~u and ~v:

✓ = cos

1 (

~u·~v

||~u|| ||~v||

Projection of ~u onto ~v:

pr~v ~u = (

~u·~v

||~v||

2

)~v

Cross Product

~u ⇥ ~v

Produces a Vector

(Geometrically, the cross product is the

area of a paralellogram with sides ||~u||

and ||~v||)

~u =< u 1 , u 2 , u 3 >

~v =< v 1

, v 2

, v 3

~u ⇥ ~v =

i

j

k

u 1

u 2

u 3

v 1 v 2 v 3

~u ⇥ ~v =

0 means the vectors are paralell

Lines and Planes

Equation of a Plane

(x 0 , y 0 , z 0 ) is a point on the plane and

< A, B, C > is a normal vector

A(x x 0 ) + B(y y 0 ) + C(z z 0 ) = 0

< A, B, C > · < xx 0 , yy 0 , zz 0 >= 0

Ax + By + Cz = D where

D = Ax 0

  • By 0

  • Cz 0

Equation of a line

A line requires a Direction Vector

~u =< u 1

, u 2

, u 3

and a point

(x 1 , y 1 , z 1 )

then,

a parameterization of a line could be:

x = u 1

t + x 1

y = u 2

t + y 1

z = u 3

t + z 1

Distance from a Point to a Plane

The distance from a point (x 0 , y 0 , z 0 ) to

a plane Ax+By+Cz=D can be expressed

by the formula:

d =

|Ax 0

+By 0

+Cz 0

D|

p

A 2 +B 2 +C 2

Coord Sys Conv

Cylindrical to Rectangular

x = r cos(✓)

y = r sin(✓)

z = z

Rectangular to Cylindrical

r =

p

x

2

  • y

2

tan(✓) =

y

x

z = z

Spherical to Rectangular

x = ⇢ sin() cos(✓)

y = ⇢ sin() sin(✓)

z = ⇢ cos()

Rectangular to Spherical

p

x

2

  • y

2

  • z

2

tan(✓) =

y

x

cos() =

z p

x

2 +y

2 +z

2

Spherical to Cylindrical

r = ⇢ sin()

z = ⇢ cos()

Cylindrical to Spherical

p

r

2

  • z

2

cos() =

z p

r

2 +z

2

Surfaces

Ellipsoid

x

2

a

2

y

2

b

2

z

2

c

2

Hyperboloid of One Sheet

x

2

a

2

y

2

b

2

z

2

c

2

(Major Axis: z because it follows - )

Hyperboloid of Two Sheets

z

2

c

2

x

2

a

2

y

2

b

2

(Major Axis: Z because it is the one not

subtracted)

Elliptic Paraboloid

z =

x

2

a

2

y

2

b

2

(Major Axis: z because it is the variable

NOT squared)

Hyperbolic Paraboloid

(Major Axis: Z axis because it is not

squared)

z =

y

2

b

2

x

2

a

2

Elliptic Cone

(Major Axis: Z axis because it’s the only

one being subtracted)

x

2

a

2

y

2

b

2

z

2

c

2

Cylinder

1 of the variables is missing

OR

(x a)

2

  • (y b

2

) = c

(Major Axis is missing variable)

Partial Derivatives

Partial Derivatives are simply holding all

other variables constant (and act like

constants for the derivative) and only

taking the derivative with respect to a

given variable.

Given z=f(x,y), the partial derivative of

z with respect to x is:

f x

(x, y) = z x

@z

@x

@f (x,y)

@x

likewise for partial with respect to y:

f y

(x, y) = z y

@z

@y

@f (x,y)

@y

Notation

For f xyy

, work ”inside to outside” f x

then f xy

, then f xyy

f xyy

@

3 f

@x@

2 y

For

@

3 f

@x@

2 y

, work right to left in the

denominator

Gradients

The Gradient of a function in 2 variables

is rf =< f x

, f y

The Gradient of a function in 3 variables

is rf =< fx, fy , fz >

Chain Rule(s)

Take the Partial derivative with respect

to the first-order variables of the

function times the partial (or normal)

derivative of the first-order variable to

the ultimate variable you are looking for

summed with the same process for other

first-order variables this makes sense for.

Example:

let x = x(s,t), y = y(t) and z = z(x,y).

z then has first partial derivative:

@z

@x

and

@z

@y

x has the partial derivatives:

@x

@s

and

@x

@t

and y has the derivative:

dy

dt

In this case (with z containing x and y

as well as x and y both containing s and

t), the chain rule for

@z

@s

is

@z

@s

@z

@x

@x

@s

The chain rule for

@z

@t

is

@z

@t

@z

@x

@x

@t

@z

@y

dy

dt

Note: the use of ”d” instead of ”@” with

the function of only one independent

variable

Limits and Continuity

Limits in 2 or more variables

Limits taken over a vectorized limit just

evaluate separately for each component

of the limit.

Strategies to show limit exists

  1. Plug in Numbers, Everything is Fine
  2. Algebraic Manipulation

-factoring/dividing out

-use trig identites

  1. Change to polar coords

if (x, y)! (0, 0) , r! 0

Strategies to show limit DNE

  1. Show limit is di↵erent if approached

from di↵erent paths

(x=y, x = y

2

, etc.)

  1. Switch to Polar coords and show the

limit DNE.

Continunity

A fn, z = f (x, y), is continuous at (a,b)

if

f (a, b) = lim (x,y)!(a,b)

f (x, y)

Which means:

  1. The limit exists
  2. The fn value is defined
  3. They are the same value

Directional Derivatives

Let z=f(x,y) be a fuction, (a,b) ap point

in the domain (a valid input point) and

u ˆ a unit vector (2D).

The Directional Derivative is then the

derivative at the point (a,b) in the

direction of ˆu or:

D

~u

f (a, b) = ˆu · rf (a, b)

This will return a scalar. 4-D version:

D

~u

f (a, b, c) = ˆu · rf (a, b, c)

Tangent Planes

let F(x,y,z) = k be a surface and P =

(x 0 , y 0 , z 0 ) be a point on that surface.

Equation of a Tangent Plane:

rF (x 0

, y 0

, z 0

)· < x x 0

, y y 0

, z z 0

Approximations

let z = f (x, y) be a di↵erentiable

function total di↵erential of f = dz

dz = rf · < dx, dy >

This is the approximate change in z

The actual change in z is the di↵erence

in z values:

z = z z 1

Maxima and Minima

Internal Points

  1. Take the Partial Derivatives with

respect to X and Y (fx and fy ) (Can use

gradient)

  1. Set derivatives equal to 0 and use to

solve system of equations for x and y

  1. Plug back into original equation for z.

Use Second Derivative Test for whether

points are local max, min, or saddle

Second Partial Derivative Test

  1. Find all (x,y) points such that

rf (x, y) =

  1. Let D = f xx

(x, y)f yy

(x, y) f

2

xy

(x, y)

IF (a) D > 0 AND f xx

< 0 , f(x,y) is

local max value

(b) D > 0 AND fxx(x, y) > 0 f(x,y) is

local min value

(c) D < 0, (x,y,f(x,y)) is a saddle point

(d) D = 0, test is inconclusive

  1. Determine if any boundary point

gives min or max. Typically, we have to

parametrize boundary and then reduce

to a Calc 1 type of min/max problem to

solve.

The following only apply only if a

boundary is given

  1. check the corner points
  2. Check each line (0  x  5 would

give x=0 and x=5 )

On Bounded Equations, this is the

global min and max...second derivative

test is not needed.

Lagrange Multipliers

Given a function f(x,y) with a constraint

g(x,y), solve the following system of

equations to find the max and min

points on the constraint (NOTE: may

need to also find internal points.):

rf = rg

g(x, y) = 0(orkif given)

Double Integrals

With Respect to the xy-axis, if taking an

integral, R R

dydx is cutting in vertical rectangles,

R R

dxdy is cutting in horizontal

rectangles

Polar Coordinates

When using polar coordinates,

dA = rdrd✓

Surface Area of a Curve

let z = f(x,y) be continuous over S (a

closed Region in 2D domain)

Then the surface area of z = f(x,y) over

S is:

SA =

R R

S

q

f

2

x

  • f

2

y

  • 1dA

Triple Integrals

R R R

s

f (x, y, z)dv =

R

a 2

a 1

R

2

(x)

1

(x)

R

2

(x,y)

1

(x,y)

f (x, y, z)dzdydx

Note: dv can be exchanged for dxdydz in

any order, but you must then choose

your limits of integration according to

that order

Jacobian Method

R R

G

f (g(u, v), h(u, v))|J(u, v)|dudv =

R R

R

f (x, y)dxdy

J(u, v) =

@x

@u

@x

@v

@y

@u

@y

@v

Common Jacobians:

Rect. to Cylindrical: r

Rect. to Spherical: ⇢

2

sin()

Vector Fields

let f (x, y, z) be a scalar field and

F (x, y, z) =

M (x, y, z)

i + N (x, y, z)

j + P (x, y, z)

k be

a vector field,

Grandient of f = rf =<

@f

@x

@f

@y

@f

@z

Divergence of

F :

r ·

F =

@M

@x

@N

@y

@P

@z

Curl of

F :

r ⇥

F =

i

j

k

@

@x

@

@y

@

@z

M N P

Line Integrals

C given by x = x(t), y = y(t), t 2 [a, b]

R

c

f (x, y)ds =

R

b

a

f (x(t), y(t))ds

where ds =

q

dx

dt

2

  • (

dy

dt

2 dt

or

q

dy

dx

2 dx

or

q

dx

dy

2

dy

To evaluate a Line Integral,

· get a paramaterized version of the line

(usually in terms of t, though in

exclusive terms of x or y is ok)

· evaluate for the derivatives needed

(usually dy, dx, and/or dt)

· plug in to original equation to get in

terms of the independant variable

· solve integral

Work

Let

F = M

i +

j +

k (force)

M = M (x, y, z), N = N (x, y, z), P =

P (x, y, z)

(Literally)d~r = dx

i + dy

j + dz

k

Work w =

R

c

F · d~r

(Work done by moving a particle over

curve C with force

F )

Independence of Path

Fund Thm of Line Integrals

C is curve given by ~r(t), t 2 [a, b];

~r 0 (t) exists. If f (~r) is continuously

di↵erentiable on an open set containing

C, then

R

c

rf (~r) · d~r = f (

b) f (~a)

Equivalent Conditions

F (~r) continuous on open connected set

D. Then,

(a)

F = rf for some fn f. (if

F is

conservative)

, (b)

R

c

F (~r) · d~risindep.of pathinD

, (c)

R

c

F (~r) · d~r = 0 for all closed paths

in D.

Conservation Theorem

F = M

i + N

j + P

k continuously

di↵erentiable on open, simply connected

set D.

F conservative , r ⇥

F =

(in 2D r ⇥

F =

0 i↵ M y

= N

x

Green’s Theorem

(method of changing line integral for

double integral - Use for Flux and

Circulation across 2D curve and line

integrals over a closed boundary) H

M dy N dx =

R R

R

(Mx + Ny )dxdy

H

M dx + N dy =

R R

R

(N

x

M

y

)dxdy

Let:

·R be a region in xy-plane

·C is simple, closed curve enclosing R

(w/ paramerization ~r(t))

F (x, y) = M (x, y)

i + N (x, y)

j be

continuously di↵erentiable over R[C.

Form 1: Flux Across Boundary

~n = unit normal vector to C H

c

F · ~n =

R R

R

r ·

F dA

H

M dy N dx =

R R

R

(M

x

+ N

y

)dxdy

Form 2: Circulation Along

Boundary

H

c

F · d~r =

R R

R

r ⇥

F · ˆudA

H

M dx + N dy =

R R

R

(N

x

M

y

)dxdy

Area of R

A =

H

1

2

ydx +

1

2

xdy)

Gauss’ Divergence Thm

(3D Analog of Green’s Theorem - Use

for Flux over a 3D surface) Let:

F (x, y, z) be vector field continuously

di↵erentiable in solid S

·S is a 3D solid ·@S boundary of S (A

Surface)

·ˆnunit outer normal to @S

Then,

R R

@S

F (x, y, z) · ˆndS =

R R R

S

r ·

F dV

(dV = dxdydz)

Surface Integrals

Let

·R be closed, bounded region in xy-plane

·f be a fn with first order partial

derivatives on R

·G be a surface over R given by

z = f (x, y)

·g(x, y, z) = g(x, y, f (x, y)) is cont. on R

Then,

R R

G

g(x, y, z)dS =

R R

R

g(x, y, f (x, y))dS

where dS =

q

f

2

x

  • f

2

y

  • 1dydx

Flux of

F across G

R R

G

F · ndS =

R R

R

[M fx N fy + P ]dxdy

where:

F (x, y, z) =

M (x, y, z)

i + N (x, y, z)

j + P (x, y, z)

k

·G is surface f(x,y)=z

·~n is upward unit normal on G.

·f(x,y) has continuous 1

st

order partial

derivatives

Unit Circle

(cos, sin)

Other Information

p

a

p

b

p

a

b

Where a Cone is defined as

z =

p

a(x

2

  • y

2 ),

In Spherical Coordinates,

= cos

1

(

q

a

1+a

Right Circular Cylinder:

V = ⇡r

2

h, SA = ⇡r

2

  • 2⇡rh

lim n!inf

m

n

pn = e

mp

Law of Cosines:

a

2

= b

2

  • c

2

2 bc(cos(✓))

Stokes Theorem

Let:

·S be a 3D surface

F (x, y, z) =

M (x, y, z)

i + N (x, y, z)

j + P (x, y, z)

l

·M,N,P have continuous 1

st

order partial

derivatives

·C is piece-wise smooth, simple, closed,

curve, positively oriented

T is unit tangent vector to C.

Then,

H

Fc ·

T dS =

R R

s

(r ⇥

F ) · ˆndS =

R R

R

(r ⇥

F ) · ~ndxdy

Remember: H

F ·

T ds =

R

c

(M dx + N dy + P dz)

Originally Written By Daniel Kenner for

MATH 2210 at the University of Utah.

Source code available at

https://github.com/keytotime/Calc3 CheatSheet

Thanks to Kelly Macarthur for Teaching and

Providing Notes.