Equations

A quick reference pulled directly from the notes. Filter by module or search for a variable, topic, or source note.

This page may have issues as this is a new one.

400 equations

S1 / Electrical Fundamentals

Per-Phase Equivalent Circuit 01
  ⟹  3-phase power=3∣Vl∣∣Il∣cos⁡θ\implies \text{3-phase power}= \sqrt{3}|V_l||I_l|\cos\theta

Symbols

VpV_p
phase voltage
VlV_l
line voltage
IlI_l
line current
cos⁡θ\cos\theta
power factor
Mean Value 02
vmean=1T∫T0T0+Tv(t)dtv_{\text{mean}}= \frac{1}{T} \int_{T_0}^{T_0+T}{v(t)\text{d}t}

Symbols

T0T_0
the starting time of a cycle
TT
the periodic time
AC Theory
Equation 03
C=ϵAdC = \frac{\epsilon A}{d}

Symbols

ϵ\epsilon
permittivity of the material in-between
dd
distance between the plates
AA
area of a plate
Capacitors
Impedance (Z) 04
Z=VI=R+jXZ=\frac{V}{I}=R+jX

Symbols

RR
Resistance
XX
Reactance
Admittance (Y) 05
Y=1Z=IV=G+jBY=\frac{1}{Z}=\frac{I}{V}=G+jB

Symbols

GG
Conductance
BB
Susceptance
Inductance (L) 06
v=−Ldidtv = -L \frac{\text{d}i}{\text{d}t}

Symbols

vv
voltage difference across the inductor
ii
current through the inductor
Inductors
Equation 07
R=ρlAR = \frac{\rho l}{A}

Symbols

ll
length
AA
cross-sectional area
ρ\rho
resistivity
Resistors

S1 / Fluid Mechanics

Continuity Equation 01
Q=AvQ= Av

Symbols

QQ
Flow rate
AA
Cross-sectional area
vv
velocity
Bernoulli's Equation 02
Total head H=z+Pρg+v22g\text{Total head }H= z + \frac{P}{\rho g} + \frac{v^2}{2g}

Symbols

zz
datum head, the height to the point from a reference level
Pρg\frac{P}{\rho g}
Pressure head
v22g\frac{v^2}{2g}
velocity head, the kinetic energy per unit weight
Steady Flow Momentum Equation 03
FS=Mo˙−Mi˙F_S = \dot{M_o}-\dot{M_i}

Symbols

FSF_S
Force exerted on the fluid within the control volume
Mo˙\dot{M_o}
Rate of change of momentum of the inflow fluid
Mi˙\dot{M_i}
Rate of change of momentum of the outflow fluid
Steady Flow Momentum Equation 04
M˙=ρQv\dot{M} = \rho Q v

Symbols

M˙\dot{M}
rate of change of momentum
ρ\rho
density
QQ
flow rate
vv
velocity
Power 05
P=γQHP=\gamma Q H

Symbols

γ\gamma
specific weight of the fluid
QQ
flow rate
HH
total head
Local Loss 06
KLv22gK_L \frac{v^2}{2g}

Symbols

KLK_L
local loss coefficient
vv
velocity
gg
gravitational acceleration
Losses
Friction Loss 07
λLDv22g\lambda \frac{L}{D} \frac{v^2}{2g}

Symbols

λ\lambda
friction factor
LL
length of the pipe
DD
diameter of the pipe
vv
velocity
gg
gravitational acceleration
Losses
Head Provided 08
H=f(Q)H=f(Q)

Symbols

HH
provided head
QQ
flow rate
Pumps
Impulse Turbines 09
μ=1v12(2u)(v1−u)(1+kcos⁡β)\mu = \frac{1}{v_1^2}(2u)(v_1 - u)(1+k\cos\beta)

Symbols

v1v_1
velocity of the jet of fluid
uu
velocity of the bucket
kk
loss coefficient (a little less than 1)
β\beta
angle of deflection of fluid inside the bucket
Turbines
Equation 10
u=weight of displaced fluid=vρgu=\text{weight of displaced fluid}=v\rho{g}

Symbols

uu
the upthrust
vv
the submerged volume
ρ\rho
density of the fluid
Buoyancy
Equation 11
P=12ρω2r2−ρgz+cP = \frac{1}{2}\rho \omega^2 r^2 - \rho gz +c

Symbols

ω\omega
angular velocity
Centre of Pressure 12
yp=yc+IccA⋅ycy_p = y_c + \frac{I_{\text{cc}}}{A\cdot y_c}

Symbols

AA
total submerged area
ypy_p
distance to centre of pressure measured along the submerged surface from the free surface
ycy_c
distance to CCmeasured along the submerged surface from the free surface
IccI_{\text{cc}}
second moment of submerged area about the centroidal axis parallel to the free surface
Vertical Acceleration 13
P2=hρ(az+g)P_2=h\rho(a_z+g)

Symbols

hρgh\rho g
hydrostatic pressure
hρazh\rho a_z
due to aza_z
Rotation Is About Centroidal Axis of Waterline Plane 14
∫OCxtan⁡θ dA=∫OAxtan⁡θ dA  ⟹  ∫OCx dA=0=Ax‾\int_{O}^{C}{x\tan{\theta}\,\text{d}A} = \int_{O}^{A}{x\tan{\theta}\,\text{d}A} \implies \int_{O}^{C}{x\,\text{d}A}=0=A\overline{x}

Symbols

AA
area of waterline plane
x‾\overline{x}
distance to the centroid from axis OOOO
Equation for Metacentric Radius 15
V(BB′)=θ(∫ACx2 dA)=IθV(BB')= \theta \bigg( \int_{A}^{C}{x^2\,\text{d}A} \bigg) =I\theta

Symbols

VV
submerged volume
II
second moment of area of the waterline plane about the centroidal axis OOOO
Time Period of Oscillation 16
θ¨=−W(GM)IGθ=−Mg(GM)Mk2θ\ddot{\theta}=-\frac{W(\text{GM})}{I_G}\theta=-\frac{Mg(\text{GM})}{Mk^2}\theta

Symbols

τ\tau
restoring torque
WW
Total weight
IGI_G
Moment of inertia of the body about axis of rotation
MM
Total mass
kk
Radius of gyration about axis of rotation

S1 / Mathematics

Equation 01
z,w∈Cz,w \in \mathbb{C}

Symbols

DD
domain of ff
D′D'
codomain of ff
Representation Methods 02
z=peiθz=pe^{i\theta}

Symbols

x=pcos⁡θx = p\cos{\theta}
real part
y=psin⁡θy=p\sin{\theta}
imaginary part
p=x2+y2p=\sqrt{x^{2} + y^{2}}
modulus
θ=tan⁡−1(yx)\theta=\tan^{-1}{(\frac{y}{x})}
arg angle
Properties 03
∃ D,P   s.t. D=P−1AP\exists\, D,P\;\text{ s.t. } D = P^{-1}AP

Symbols

DD
a diagonal matrix
PP
an invertible matrix
Solution 04
y=yp+ycy=y_p+y_c

Symbols

ypy_p
**particular solution**
ycy_c
**complementary solution**
Complementary Solution 05
yc=∑i=1nci yiy_c = \sum_{i=1}^{n}{c_i\,y_i}

Symbols

cic_i
constant coefficients
yiy_i
a linearly-independent solution
Linear 06
P0(x)y+P1(x)y′+ ... +Pn(x)y(n)+Q(x)=0P_0 (x) y + P_1 (x) y' +\,...\,+ P_n (x) y^{(n)} + Q(x) = 0

Symbols

P0,P1,…,Pn,QP_0, P_1,\dots,P_n,Q
differentiable functions of xx that do not depend on yy
y(x)y(x)
the unknown function
y(n)y^{(n)}
the nnth derivative of yy
Equation 07
∑n=0∞an(x−c)n\sum_{n=0}^\infty a_n (x-c)^n

Symbols

xx
a variable
cc
a constant
Distance Between 2 Skew Lines 08
distance=∣AB→⋅n‾∣\text{distance}=\lvert{\overrightarrow{AB}\cdot\underline{n}}\rvert

Symbols

n‾\underline{n}
the unit normal to both l1,l2l_1,l_2
A,BA, B
points lying on each line
Skew Lines
Shortest Distance from a Point 09
d2=∣r‾−p‾∣2−[n‾⋅(r‾−p‾)∣n‾∣]2d^2 = {\big\lvert\underline{r} - \underline{p}\big\rvert}^2 - \bigg[ \frac{\underline{n}\cdot (\underline{r} - \underline{p})}{\lvert \underline{n} \rvert} \bigg]^2

Symbols

PP
the arbitrary point
p‾\underline{p}
the position vector of PP
r‾\underline{r}
the position vector of a point on the line
n‾\underline{n}
parallel to the line

S1 / Mechanics

General 2D Motion 01
aB=aA+aB/A=aA+θ¨‾×rB/A+θ˙‾×(θ˙‾×rB/A)a_\text{B}= a_\text{A}+a_{\text{B}/\text{A}}= a_\text{A}+\underline{\ddot{\theta}}\times r_{\text{B}/\text{A}}+\underline{\dot{\theta}}\times (\underline{\dot{\theta}}\times r_{\text{B}/\text{A}})

Symbols

θ˙‾\underline{\dot{\theta}}
angular velocity of B\text{B} relative to A\text{A}
vB/Av_{\text{B}/\text{A}}
velocity of B\text{B} relative to A\text{A}
aB/Aa_{\text{B}/\text{A}}
acceleration of B\text{B} relative to A\text{A}
rB/Ar_{\text{B}/\text{A}}
constant position vector of B\text{B} relative to A\text{A}
Pitch 02
d=mz=2rd = mz = 2r

Symbols

dd
pitch circle diameter
mm
pitch of the gear
zz
no. of teeth in the gear
rr
radius of the gear
Gears
For a Particle 03
F−ma=0F - ma = 0

Symbols

FF
resultant applied force
mm
mass of the particle
aa
acceleration of the particle
For a System of Particles 04
F=∑miai=F=maGF = \sum m_ia_i = F = ma_G

Symbols

FF
resultant applied force
mim_i
mass of a particle
aia_i
acceleration of a particle
mm
total mass
aGa_G
acceleration of the center of mass
Kinetics of a Rigid Body 05
F=ma      and      M=IαF=ma \;\;\; \text{and} \;\;\; M=I\alpha

Symbols

FF
resultant applied force
mm
mass of the body
aa
acceleration of the body
MM
moment of the resultant force
II
mass moment of inertia
α\alpha
angular acceleration of the body
General Plane Motion 06
∑MG=IGα\sum M_G = I_G \alpha

Symbols

MGM_G
moment about center of mass
IGI_G
mass moment of inertia about center of mass
α\alpha
angular acceleration of the body
Center of Percussion 07
lP/G=kG2OGl_{\text{P/G}} = \frac{k_\text{G}^2}{\text{OG}}

Symbols

P\text{P}
Center of percussion
G\text{G}
Center of gravity
O\text{O}
Pivot
Grubler's Equation 08
F=3(N−1)−2L−H=1  ⟹  3N−2L+H=4F=3(N-1)-2L-H=1 \implies 3N-2L+H=4

Symbols

FF
degree of freedoms
NN
number of kinematic elements
LL
number of lower pairs
HH
number of higher pairs
Mechanisms
Acceleration Diagram 09
a1b1=a1x1+x1b1=(AB) ωAB2+(AB) αABa_1b_1 = a_1x_1 + x_1b_1 =(\text{AB})\,\omega_{AB}^2 + (\text{AB})\,\alpha_{AB}

Symbols

a1x1a_1x_1
the radial component of the relative acceleration between A and B
x1b1x_1b_1
the transverse component of the relative acceleration between A and B
Deflection of a Beam 10
Smax=WL4I    ∧    Dmax=WL348EIS_{\text{max}} = \frac{WL}{4I} \;\; \land \;\; D_{\text{max}} = \frac{WL^3}{48EI}

Symbols

SmaxS_\text{max}
Maximum stress
DmaxD_\text{max}
Deflection
WW
Load
LL
Span length
EE
Young's modulus
II
Second moment of cross-sectional area
Beams
First Moment of Area 11
About y-axis=∫Ax  dA=Ayˉ\text{About y-axis} = \int_A {x\;\text{d}A} = A\bar{y}

Symbols

xˉ\bar{x}
Centroid's xxcoordinate
yˉ\bar{y}
Centroid's yycoordinate
AA
Total area
Necessary Condition for Being Simply Stiff 12
m=2j−3m = 2j - 3

Symbols

mm
Number of members (bars)
jj
Number of joints
Trusses

S1 / Programming Fundamentals

Two's Complement 01
00

Symbols

11
negative
00
positive
Integers

S1 / Properties of Materials

Rule of Mixtures 01
Ec=VmEm+VfEfE_c = V_m E_m + V_f E_f

Symbols

Vm,VfV_m, V_f
volume fractions of matrix and fiber
Em,EfE_m, E_f
elastic moduli of matrix and fiber
Composites
Density 02
ρ=Mass/unit cellVolume/unit cell=MnvL\rho= \frac{\text{Mass/unit cell}}{\text{Volume/unit cell}} = \frac{M n}{v L}

Symbols

MM
molar mass
nn
atoms per unit cell
vv
volume of the unit cell
LL
avagadro's number
Relative Permittivity 03
ϵr=ϵϵ0\epsilon_r = \frac{\epsilon}{\epsilon_0}

Symbols

ϵ\epsilon
permittivity of the material
ϵ0\epsilon_0
permittivity of free space (8.85×10−128.85 \times 10^{-12}F/m)
Fermi-Dirac Distribution 04
f(Ei)=1exp⁡(Ei−EfKBT)+1f(E_i)=\frac{1}{\exp{\Big(\frac{E_i-E_f}{K_BT}}\Big)+1}

Symbols

EfE_f
Fermi level
EiE_i
Energy of the iith energy state
KBK_B
Boltzmann constant
TT
Absolute temperature
exp⁡()\exp()
exponent function
Temperature Coefficient of Resistivity 05
ρ=ρ0[1+α(T−T0)]\rho=\rho_0\Big[1 + \alpha(T-T_0)\Big]

Symbols

ρ\rho
resistivity at temperature TT
ρ0\rho_0
resistivity at temperature T0T_0
α\alpha
temperature coefficient of resistivity
Current Density 06
J=nqν‾=σEJ=nq\overline{\nu}=\sigma E

Symbols

nn
carrier concentration
qq
charge on a single charge carrier
ν‾\overline{\nu}
drift velocity
σ\sigma
conductivity
EE
applied electric field
Piezoelectric Materials 07
Q=d×FQ=d\times F

Symbols

QQ
charge stored
dd
charge sensitivity (a material property)
FF
applied force
Piezoelectric Materials 08
C=ϵAt  ⟹  V=dϵσtC=\epsilon\frac{A}{t} \implies V=\frac{d}{\epsilon}\sigma t

Symbols

VV
applied voltage
dd
charge sensitivity
ϵ\epsilon
permittivity
σ\sigma
applied stress
tt
thickness
Conductivity of Intrinsic Semiconductors 09
σ=n∣e∣(μe+μh)\sigma=n\lvert{e}\rvert(\mu_e+\mu_h)

Symbols

σ\sigma
conductivity
nn
carrier concentration
ee
electron's charge
μe\mu_e
carrier mobility of electrons
μh\mu_h
carrier mobility of holes
Equation 10
MOR=3PL2bd2\text{MOR} = \frac{3PL}{2bd^2}

Symbols

PP
fracture load
LL
support span length
bb
width of specimen
dd
depth of specimen
Vickers 11
HV=1.854 Fd2HV = \frac{1.854\,F}{d^2}

Symbols

FF
applied load (kgf)
dd
average diagonal length of indentation (mm)
Hardness
Brinell (HB) 12
HB=2FπDh\text{HB} = \frac{2F}{\pi D h}

Symbols

FF
applied load (in kN\text{kN})
DD
diameter of the indenter (in mm\text{mm})
dd
diameter of the indentation (in mm\text{mm})
h=(D−D2−d2)h = (D-\sqrt{D^2 - d^2})
depth of the indentation (in mm\text{mm})
Relation to Elastic Moduli 13
G=E2(1+ν)K=E3(1−2ν)G = \frac{E}{2(1+\nu)} \qquad K = \frac{E}{3(1-2\nu)}

Symbols

GG
shear modulus
KK
bulk modulus
EE
Young's modulus

S2 / Computer Organization and Digital Design

Power Consumption 01
Dynamic power consumption=CV2f\text{Dynamic power consumption} = CV^2 f

Symbols

CC
Capacitance of the circuit
VV
Supply voltage
ff
Charging frequency of the capacitor
Power Consumption 02
Static power consumption=VIleakage\text{Static power consumption} = V I_{\text{leakage}}

Symbols

VV
Supply voltage
IleakageI_{\text{leakage}}
Leakage current

S2 / Methods of Mathematics

Equation 01
Ln,k(x)=∏i=0i≠knx−xixk−xiL_{n,k}(x) = \prod_{\substack{i=0 \\ i \neq k}}^{n} \frac{x - x_i}{x_k - x_i}

Symbols

x0,x1,…,xnx_0, x_1, \ldots, x_n
the n+1n+1distinct data points (nodes)
Jacobi Method 02
T=D−1(L+U)∧C=D−1B⃗T = D^{-1}(L + U) \land C = D^{-1} \vec{B}

Symbols

DD
a diagonal matrix whose entries are of AA
−L-L
the strictly lower-triangular part of AA
−U-U
the strictly upper-triangular part of AA
Gauss-Seidel Method 03
∀i∈{1,2,…,n}    aii≠0\forall i \in \{1,2,\dots,n\}\;\; a_{ii} \neq 0

Symbols

DD
a diagonal matrix whose entries are of AA
−L-L
the strictly lower-triangular part of AA
−U-U
the strictly upper-triangular part of AA
Chi-Square Distribution 04
f(x;k)=12k/2Γ(k/2)xk/2−1e−x/2,x>0f(x; k) = \frac{1}{2^{k/2} \Gamma(k/2)} x^{k/2 - 1} e^{-x/2}, \quad x > 0

Symbols

kk
the degrees of freedom
Γ\Gamma
the gamma function
Mean 05
E(X)=μ=∑ixi⋅P(X=xi)E(X) = \mu = \sum_{i} x_i \cdot P(X = x_i)

Symbols

xix_i
each possible value of XX
P(X=xi)P(X = x_i)
the probability of observing that value
Bernoulli Distribution 06
P(x)=px(1−p)1−x    ;  x=0  or  1P(x) = p^x (1-p)^{1-x} \;\;;\; x = 0\;\text{or}\;1

Symbols

pp
the probability of success
Binomial Distribution 07
P(X)=(nx)px(1−p)n−x    ;  x=0,1,…,nP(X) = \binom{n}{x} p^x (1-p)^{n-x}\;\;;\; x = 0, 1, \ldots, n

Symbols

nn
the number of trials
θ\theta
the probability of success
Poisson Distribution 08
P(x)=e−λλxx!    ;  x=0,1,2,…P(x) = \frac{e^{-\lambda} \lambda^x}{x!}\;\;;\; x=0,1,2,\ldots

Symbols

λ\lambda
average number of successes occurred within the fixed time interval
Conditional Probability 09
P(A∣B)=P(A∩B)P(B)P(A|B) = \frac{P(A \cap B)}{P(B)}

Symbols

P(A∣B)P(A|B)
the conditional probability of AA given BB
P(A∩B)P(A \cap B)
the joint probability of both AA and BBoccurring
P(B)P(B)
the probability of event BBoccurring

S2 / Theory of Electricity

Magnetomotive Force 01
f=Ni\mathfrak{f} = Ni

Symbols

NN
number of turns
ii
current in the coil
Reluctance 02
R=lμA=fϕ\mathcal{R} = \frac{l}{\mu A} = \frac{\mathfrak{f}}{\phi}

Symbols

ll
Length of the path
μ\mu
Permeability
AA
Cross-sectional area
DC Time Constant 03
τRL=LR\tau_{RL} = \frac{L}{R}

Symbols

RR
resistance (ohms)
CC
capacitance (farads)
LL
inductance (henries)
Equation 04
F(s)=L{f(t)}=∫0∞e−stf(t) dtF(s) = \mathcal{L}\{f(t)\} = \int_{0}^{\infty} e^{-st} f(t) \, \text{d}t

Symbols

tt
time
ss
σ+jω\sigma + j\omega, where σ,ω∈R\sigma, \omega \in \mathbb{R}
e−ste^{-st}
the exponential decay factor that weights the function f(t)f(t)
Equivalent Circuit 05
A=V0VdA = \frac{V_0}{V_d}

Symbols

RinR_\text{in}
Input resistance, very high
RoutR_\text{out}
Output resistance, very low
AA
Gain, ranges from 10510^5 to 10710^7
VdV_{d}
voltage difference between input terminals
VinV_{in}
voltage input, ranges between 5V5V and 18V18V
Non-Inverting Amplifier 06
Av=v0vin=1+R2R1A_v = \frac{v_0}{v_\text{in}} = 1 + \frac{R_2}{R_1}

Symbols

AvA_v
Closed-loop voltage gain
Impedances 07
[Zp]=[ZsZmZmZmZsZmZmZmZs][Z_p] = \begin{bmatrix} Z_s & Z_m & Z_m \\ Z_m & Z_s & Z_m \\ Z_m & Z_m & Z_s \\ \end{bmatrix}

Symbols

ZmZ_m
the impedance caused by mutual coupling between phases
ZsZ_s
the impedance in a single phase
Copper Loss 08
Pcopper=I2RP_\text{copper} = I^2 R

Symbols

II
current flow
RR
resistance of winding
Impedance Transformation 09
Z2Z1=(N2N1)2\frac{Z_2}{Z_1} = \left(\frac{N_2}{N_1}\right)^2

Symbols

Z1Z_1
impedance of the primary coil
Z2Z_2
impedance of the load in the secondary coil
Transmission Parameters 10
[V1I1]=[ABCD][V2−I2]\begin{bmatrix} V_1 \\ I_1 \end{bmatrix} = \begin{bmatrix} A & B \\ C & D \\ \end{bmatrix} \begin{bmatrix} V_2 \\ -I_2 \end{bmatrix}

Symbols

AA
Voltage gain. Open circuit transfer function. Dimensionless.
BB
Transfer impedance. Short circuit transfer impedance.
CC
Transfer admittance. Open circuit transfer admittance.
DD
Current gain. Short circuit current ratio. Dimensionless.

S3 / Applied Statistics

Equation 01
χ2=νs2σ2\chi^2 = \frac{\nu s^2}{\sigma^2}

Symbols

ν\nu
degrees of freedom
s2s^2
sample variance
σ2\sigma^2
population variance
For Two-Tailed Tests 02
H0H_0

Symbols

H0H_0
μ=μ0\mu = \mu_0
H1H_1
μ≠μ0\mu \neq \mu_0
For Right-Tailed Tests 03
H0H_0

Symbols

H0H_0
μ≤μ0\mu \leq \mu_0
H1H_1
μ>μ0\mu \gt \mu_0
For Left-Tailed Tests 04
H0H_0

Symbols

H0H_0
μ≥μ0\mu \geq \mu_0
H1H_1
μ<μ0\mu \lt \mu_0
Equation 05
P(x)=p(1−p)x−1P(x) = p(1-p)^{x-1}

Symbols

pp
probability of success in a single trial
Test Statistic 06
χ2=∑i=1k(Oi−Ei)2Ei∼χν2\chi^2 = \sum_{i=1}^{k} \frac{(O_i - E_i)^2}{E_i} \quad \sim \chi^2_\nu

Symbols

OiO_i
observed frequency for class ii
EiE_i
expected frequency for class ii under H0H_0
kk
number of non-empty classes
Degrees of Freedom 07
ν=k−p−1\nu = k - p - 1

Symbols

pp
number of parameters estimated from the data
Hypergeometric Experiment 08
P(x)=kCx×  N−kCn−xNCnP(x) = \frac{^kC_x \times\; ^{N-k}C_{n-x}}{^NC_n}

Symbols

kCx^kC_x
Number of ways to choose xx successes from kksuccesses.
N−kCn−x^{N-k}C_{n-x}
Number of ways to choose n−xn-x failures from N−kN-kfailures.
NCn^NC_n
Total number of ways to choose nn items from NNitems.
Model 09
yi=α+βxi+εiy_i = \alpha + \beta x_i + \varepsilon_i

Symbols

α\alpha
intercept (value of YY when X=0X = 0)
β\beta
slope (rate of change of YY with respect to XX)
εi\varepsilon_i
random error, assumed εi∼N(0,σ2)\varepsilon_i \sim N(0, \sigma^2)
Analysis of Variance (ANOVA) for Regression 10
n–1n–1

Symbols

yiy_i
Actual observed value of the dependent variable for observation ii
yi^\hat{y_i}
Predicted value of yiy_ifrom the regression line
yˉ\bar{y}
Mean of all observed yiy_ivalues (overall average)
For Two-Tailed Tests 11
2×P(Z>∣z∣)2 \times P(Z \gt \left\lvert z \right\rvert)

Symbols

H0H_0
μ=μ0\mu = \mu_0
H1H_1
μ≠μ0\mu \neq \mu_0
For Right-Tailed Tests 12
P(Z>z)P(Z \gt z)

Symbols

H0H_0
μ≤μ0\mu \leq \mu_0
H1H_1
μ>μ0\mu \gt \mu_0
For Left-Tailed Tests 13
P(Z<−z)P(Z \lt -z)

Symbols

H0H_0
μ≥μ0\mu \geq \mu_0
H1H_1
μ<μ0\mu \lt \mu_0
Equation 14
P(x)=(x−1r−1)pr(1−p)x−rP(x) = \binom{x-1}{r-1} p^r (1-p)^{x-r}

Symbols

pp
probability of success in a single trial
rr
number of successes required
Definition 15
f(t)=Γ(ν+12)νπ Γ(ν2)(1+t2ν)−ν+12f(t) = \frac{\Gamma\left(\frac{\nu+1}{2}\right)}{\sqrt{\nu\pi}\,\Gamma\left(\frac{\nu}{2}\right)} \left(1 + \frac{t^2}{\nu}\right)^{-\frac{\nu+1}{2}}

Symbols

tt
the value of the random variable
ν\nu
the degrees of freedom (typically, ν=n−1\nu = n - 1 for a sample of size nn)
Γ\Gamma
the gamma function

S3 / Artificial Intelligence

Consistent 01
h(n)≤c(n,a,n′)+h(n′)h(n) \leq c(n, a, n') + h(n')

Symbols

n′n'
the successor node of nn after applying action aa
h(n)h(n)
the heuristic of a node nn
h(n′)h(n')
the heuristic of a node n′n'
c(n,a,n′)c(n, a, n')
the cost of the action aa from node nn to node n′n'
Comparison 02
h(n)h(n)

Symbols

bb
branching factor
dd
depth of the shallowest goal
mm
maximum depth of the search tree
kk
beam width
Equation 03
M=(S,A,T,R)M=(S,A,T,R)

Symbols

SS
the set of states
AA
the set of actions
T:S×A×S→R≥0T: S \times A \times S \rightarrow \mathbb{R}_{\ge 0}
the transition function
R:S×A→R≥0R: S \times A \rightarrow \mathbb{R}_{\ge 0}
the reward function
Transition Function 04
P(s′∣s,a)P(s'|s,a)

Symbols

ss
current state
aa
action taken
s′s'
next state
Reward Function 05
R(s,a,s′)R(s,a,s')

Symbols

ss
current state
aa
action taken
s′s'
next state
Discounting Rewards 06
Gt=∑k=0∞γkRt+k+1G_t = \sum_{k=0}^{\infty} \gamma^k R_{t+k+1}

Symbols

tt
the current time step
Rt+k+1R_{t+k+1}
the reward received at time step t+k+1t+k+1
Bellman Optimality Equation 07
Q∗(s,a)=∑s′T(s,a,s′)[R(s,a,s′)+γV∗(s′)]V∗(s)=max⁡aQ∗(s,a)\begin{equation} \nonumber \begin{split} Q^*(s,a) & = \sum_{s'} T(s, a, s') \Big[ R(s,a,s') + \gamma V^*(s') \Big] \\ V^*(s) \quad & = \max_a Q^*(s,a) \end{split} \end{equation}

Symbols

V∗(s)V^*(s)
the optimal value of ss
Q∗(s,a)Q^*(s,a)
the optimal action-value of aa at ss, the maximum reward for taking an action aa in state ss
Chance Node: Random Event 08
V=∑iP(i)×ViV = \sum_i P(i) \times V_i

Symbols

P(i)P(i)
probability of outcome ii
ViV_i
value resulting from that outcome
Value Iteration 09
Qk+1(s,a)=∑s′T(s,a,s′)[R(s,a,s′)+γVk(s′)]Vk+1(s)=max⁡aQk+1(s)\begin{equation} \nonumber \begin{split} Q_{k+1}(s,a) & = \sum_{s'} T(s, a, s') \Big[ R(s,a,s') + \gamma V_k(s') \Big] \\ V_{k+1}(s) \quad & = \max_a Q_{k+1}(s) \end{split} \end{equation}

Symbols

γ\gamma
discount factor
θ\theta
small threshold for stopping
Q-Value Iteration 10
Qk+1(s,a)=∑s′T(s,a,s′)[R(s,a,s′)+γmax⁡a′Qk(s′,a′)]Q_{k+1}(s,a) = \sum_{s'} T(s,a,s') \Big[ R(s,a,s') + \gamma \max_{a'} Q_k(s',a') \Big]

Symbols

Qk(s,a)Q_k(s,a)
estimate at iteration kk,
T(s,a,s′)T(s,a,s')
transition probability,
R(s,a,s′)R(s,a,s')
immediate reward,
γ\gamma
discount factor.
Sample-Based Policy Evaluation 11
Vπ(s)≈1N(s)∑i=1N(s)Gi(s)V_{\pi}(s) \approx \dfrac{1}{N(s)} \sum_{i=1}^{N(s)} G_i(s)

Symbols

Gi(s)G_i(s)
the discounted sum of rewards from a visit to ss
N(s)N(s)
the number of samples for ss
Comparison 12
O(V)O(V)

Symbols

bb
branching factor
dd
depth of the shallowest solution
mm
maximum depth of the state space
ll
depth limit
CC
cost of the optimal solution
εε
minimum action cost

S3 / Data Communication and Networking

AM 01
s(t)=Ac(1+km(t))cos⁡(ωct)s(t) = A_c (1 + km(t)) \cos(\omega_c t)

Symbols

AcA_c
Amplitude of the carrier signal
kk
Modulation index
m(t)m(t)
Modulation signal
ωc\omega_c
Angular frequency of the carrier signal
FM 02
θ(t)=2πΔf∫−∞tm(τ)dτ\theta(t) = 2\pi \Delta_f \int_{-\infty}^t m(\tau) \text{d}\tau

Symbols

Δp\Delta_p
Phase modulation index
AcA_c
Amplitude of the carrier signal
m(t)m(t)
Modulation signal
ωc\omega_c
Angular frequency of the carrier signal
PM 03
θ(t)=Δpm(t)\theta(t) = \Delta_p m(t)

Symbols

Δp\Delta_p
Phase modulation index
AcA_c
Amplitude of the carrier signal
m(t)m(t)
Modulation signal
ωc\omega_c
Angular frequency of the carrier signal
Mathematical Representation 04
s(t)=A×cos(2πft+θi)s(t) = A × cos(2πft + θᵢ)

Symbols

s(t)s(t)
modulated signal
AA
amplitude
ff
carrier frequency
θiθᵢ
phase angle that varies according to the digital input
tt
time
Free Space Loss 05
Free space loss=PtPr=(4πfdc)2\text{Free space loss} = \frac{P_t}{P_r} = \left(\frac{4\pi f d}{c} \right)^2

Symbols

PtP_t
Power at transmission
PrP_r
Power at reception
ff
Frequency of the signal
dd
Distance between attennas
cc
Speed of light in vacuum

S3 / Differential Equations

Equation 01
f(x,y,u,ux,uy,uxx,uxy,…)=0f(x,y,u,u_x,u_y,u_{xx},u_{xy},\ldots) = 0

Symbols

x,yx,y
independent variables
u(x,y)u(x,y)
dependent variable
uxu_x
first partial derivative w.r.t. xx
uyu_y
first partial derivative w.r.t. yy
uxxu_{xx}
2nd partial derivative w.r.t. xx
uyyu_{yy}
2nd partial derivative w.r.t. yy
uxyu_{xy}
partial derivative of uxu_x w.r.t. yy
uyxu_{yx}
partial derivative of uyu_y w.r.t. xx
Two Dimensional Heat Flow 02
r2∂2T∂r2+r∂T∂r+∂2T∂θ2=0r^2 \frac{\partial^2 T}{\partial r^2} + r\frac{\partial T}{\partial r} + \frac{\partial^2 T}{\partial \theta^2} = 0

Symbols

TT
temperature
x,yx,y
spatial coordinates
r,θr, \theta
polar coordinates

S3 / Engineering Thermodynamics

Entropy 01
dS≥δQT\text{d}S \geq \frac{\delta Q}{T}

Symbols

δQ\delta Q
differential heat transfer into the system
TT
absolute temperature at the boundary
2nd Law
Efficiency 02
η=1−1rp(k−1)/k\eta = 1 - \frac{1}{r_p^{(k-1)/k}}

Symbols

rpr_p
pressure ratio
kk
specific heat ratio (cp/cvc_p / c_v), approximately 1.4 for air
Equation 03
E=U+12mC2+mgzE = U + \frac{1}{2}mC^2 + mgz

Symbols

UU
internal energy
12mv2\frac{1}{2}mv^2
kinetic energy of the system
mgzmgz
the potential energy of the system
Energy
For Closed Systems 04
ΔQ−ΔW=ΔEsys=ΔU+ΔKE+ΔPEΔU=m(h2−h1)ΔKE=12m(C22−C12)ΔPE=mg(z2−z1)\begin{equation} \nonumber \begin{split} \Delta Q - \Delta W & = \Delta E_{\text{sys}} = \Delta U + \Delta KE + \Delta PE \\ \Delta U & = m(h_2 - h_1) \\ \Delta KE & = \frac{1}{2}m \left(C_2^2 - C_1^2\right) \\ \Delta PE & = mg(z_2 - z_1) \end{split} \end{equation}

Symbols

ΔQ\Delta Q
net heat transfer to the system
ΔW\Delta W
net work transfer from the system
ΔEsys\Delta E_{\text{sys}}
change in total energy
ΔU\Delta U
change in internal energy
ΔKE\Delta KE
change in kinetic energy
ΔPE\Delta PE
change in potential energy
h1,h2h_1, h_2
specific enthalpy
mm
mass of the system
C1,C2C_1, C_2
velocity of the system
gg
gravitational acceleration
z1,z2z_1, z_2
elevation of the system
For Open Systems 05
θ=h+12C2+gz\theta = h + \frac{1}{2}C^2 + gz

Symbols

θ\theta
total specific energy
h=u+Pνh = u + P\nu
enthalpy
uu
specific internal energy
PνP\nu
specific flow work
CC
velocity of the flow
gg
gravatitaional acceleration
zz
elevation of the flow system
For Open Systems 06
Q−W+minθin−moutθout=ΔEsysQ - W + m_{\text{in}}\theta_{\text{in}} - m_{\text{out}}\theta_{\text{out}} = \Delta E_{\text{sys}}

Symbols

QQ
heat transfer
WW
work transfer
minm_{\text{in}}
mass flow rate of the inlet
θin\theta_{\text{in}}
total specific energy of the inlet
moutm_{\text{out}}
mass flow rate of the outlet
θout\theta_{\text{out}}
total specific energy of the outlet
ΔEsys\Delta E_{\text{sys}}
change in total energy of the system
Steady Flow Energy Equation (SFEE) 07
q−w=he−hi+12(Ce2−Ci2)+g(ze−zi)q - w = h_e - h_i + \frac{1}{2}\Big(C_e^2 - C_i^2\Big) + g(z_e - z_i)

Symbols

qq
specific heat transfer
ww
specific work transfer
he,hih_e, h_i
specific enthalpy at the outlet and inlet
Ce,CiC_e, C_i
flow velocity at the outlet and inlet
gg
gravitational acceleration
ze,ziz_e,z_i
elevation at the outlet and inlet
For Reversible Process 08
ΔS=δQT\Delta S = \frac{\delta Q}{T}

Symbols

ΔS\Delta S
the change in entropy
δQ\delta Q
the heat transfer
TT
the absolute temperature
Entropy
Carnot Heat Engine 09
QHQL=THTL\frac{Q_H}{Q_L} = \frac{T_H}{T_L}

Symbols

THT_H
temperature of hot reservoir in Kelvin
TLT_L
temperature of cold reservoir in Kelvin
QHQ_H
heat taken from hot reservoir
QLQ_L
heat rejected to cold reservoir
Carnot Efficiency 10
ηCarnot=1−TLTH\eta_\text{Carnot} = 1 - \frac{T_L}{T_H}

Symbols

TLT_L
temperature of the cold reservoir (K)
THT_H
temperature of the hot reservoir (K)
Fourier’s Law 11
q˙=−kdTdx\dot{q} = -k \frac{dT}{dx}

Symbols

q˙\dot{q}
heat flux
kk
thermal conductivity
dTdx\frac{dT}{dx}
temperature gradient
Newton’s Law of Cooling 12
q˙=h(Ts−T∞)\dot{q} = h (T_s - T_\infty)

Symbols

q˙\dot{q}
convective heat flux
hh
heat transfer coefficient
TsT_s
surface temperature
T∞T_\infty
fluid temperature
Radiation 13
q˙=σεσf(T14−T24)\dot{q} = \sigma \varepsilon \sigma f (T_1^4 - T_2^4)

Symbols

q˙\dot{q}
net radiative heat flux
σ\sigma
Stefan-Boltzmann constant
ε\varepsilon
emissivity
ff
geometrical factor
Temperature Distribution for Plane Slab 14
∂T∂t=kρcdT2dx2+qg˙ρc\frac{\partial T}{\partial t} = \frac{k}{\rho c} \frac{\text{d}T^2}{\text{d}x^2} + \frac{\dot{q_g}}{\rho c}

Symbols

kk
thermal conductivity
ρ\rho
density
Ideal Gas Law 15
PV=mRT=RTvPV = mRT = \frac{RT}{v}

Symbols

PP
pressure
VV
pressure
mm
mass
RR
specific gas constant
TT
absolute temperature
vv
specific volume
Ideal Gases 16
PV=mRTPV = mRT

Symbols

PP
Pressure
VV
Volume
mm
Mass of the gas
RR
Gas constant
TT
Temperature
Specific Heat Capacity at Constant Volume 17
u=CvTu = C_v T

Symbols

CvC_v
Specific heat capacity at constant volume
TT
Temperature
uu
Specific internal energy
Specific Heat Capacity at Constant Pressure 18
h=CpTh = C_p T

Symbols

CpC_p
Specific heat capacity at constant pressure
PP
Pressure
hh
Specific enthalpy
3rd Law 19
lim⁡T→0S=S0\lim_{T \to 0} S = S_0

Symbols

SS
the entropy of the system
S0S_0
the minimum entropy value
TT
the absolute temperature
Open Feedwater Heater 20
yh6+(1−y)h2=h3y h_6 + (1-y) h_2 = h_3

Symbols

yy
extraction fraction
1−y1-y
main flow fraction
h6h_6
enthalpy of extracted steam
h2h_2
feedwater entering heater
h3h_3
saturated liquid exiting heater

S3 / Operating Systems

Equation 01
Speedup(N)=1S+1−SN\text{Speedup}(N) = \cfrac{1}{S + \frac{1 - S}{N}}

Symbols

NN
number of processing cores
SS
serial portion of the task
P=1−SP = 1- S
parallel portion of the task
Shortest Job First 02
τn+1=αtn+(1−α)τn\tau_{n+1} = \alpha t_n + (1 - \alpha) \tau_n

Symbols

tnt_n
duration of nn-th CPU burst
τn\tau_n
predicted duration of nn-th CPU burst
α\alpha
weight in [0,1][0,1]

S4 / Computer Networks

Back-Off Algorithm 01
0≤r<2k,k=min⁡(n,10)0 \leq r < 2^k, \quad k = \min(n, 10)

Symbols

rr
randomly chosen integer back-off delay, in slot times
nn
number of transmission attempts
CSMA/CD
Distance Vector Routing 02
D(x,y)=min⁡v{c(x,v)+D(v,y)}D(x, y) = \min_{v} \bigl\{ c(x, v) + D(v, y) \bigr\}

Symbols

xx
current router
yy
destination
vv
neighbor of xx
c(x,v)c(x, v)
link cost from xx to vv
Routing
Flow Control 03
Sender window≤min⁡(rwnd,cwnd)\text{Sender window} \leq \min(\text{rwnd}, \text{cwnd})

Symbols

rwnd\text{rwnd}
receiver window. Used for flow control.
cwnd\text{cwnd}
congestion window. Used for congestion control.
TCP

S4 / Graph Theory

Deletion–Contraction Method 01
P(G,x)=P(G−e,x)−P(G/e,x)P(G, x) = P(G - e, x) - P(G/e, x)

Symbols

ee
an edge
(G−e)(G-e)
resulting graph when ee is removed from GG
(G/e)(G/e)
resulting graph when ednpoints of eeis merged

S4 / Internet of Things

Equation 01
D=⌊VinVref×(2n−1)⌋D = \left\lfloor \frac{V_\text{in}}{V_\text{ref}} \times (2^n - 1) \right\rfloor

Symbols

DD
digital output value
VinV_\text{in}
input voltage
VrefV_\text{ref}
reference voltage
nn
resolution in bits
Counter-Ramp ADC 02
Tconv=D×TclkT_{conv} = D \times T_{clk}

Symbols

DD
steps to reach VinV_{in}
TclkT_{clk}
clock period
Equation 03
Vout=D2n−1×VrefV_{out} = \frac{D}{2^n - 1} \times V_{ref}

Symbols

VoutV_{out}
output voltage
DD
digital input value
nn
resolution in bits
VrefV_{ref}
reference voltage
Current-Limiting Resistor 04
R=VCC−VfIfR = \frac{V_{CC} - V_f}{I_f}

Symbols

VCCV_{CC}
supply voltage
VfV_f
LED forward voltage
IfI_f
desired forward current
Tachometer 05
RPM=fpulseN×60\text{RPM} = \frac{f_{pulse}}{N} \times 60

Symbols

fpulsef_{pulse}
pulse frequency in Hz
NN
pulses per revolution
Closed-Loop Speed Control 06
u(t)=Kp e(t)+Ki∫e(t) dt+Kddedtu(t) = K_p\, e(t) + K_i \int e(t)\, \text{d}t + K_d \frac{\text{d}e}{\text{d}t}

Symbols

e(t)e(t)
error (setpoint minus measured speed)
KpK_p
proportional gain (correction proportional to current error)
KiK_i
integral gain (correction for accumulated error, eliminates steady-state offset)
KdK_d
derivative gain (correction proportional to rate of error change, damps overshoot)
Forbidden Band 07
VILV_{IL}

Symbols

VOLV_{OL}
maximum voltage an output guarantees when driving LOW
VOHV_{OH}
minimum voltage an output guarantees when driving HIGH
VILV_{IL}
maximum voltage an input will reliably interpret as LOW
VIHV_{IH}
minimum voltage an input will reliably interpret as HIGH
Schmitt Trigger Inputs 08
VthV_{th}

Symbols

VT+V_{T+}
upper threshold. Output switches HIGH when input rises above this.
VT−V_{T-}
lower threshold. Output switches LOW when input falls below this.
Capture Mode 09
Tpulse=(t2−t1)×PfclkT_{pulse} = (t_2 - t_1) \times \frac{P}{f_{clk}}

Symbols

PP
prescaler

S4 / Linear Algebra

Equation 01
A=PJP−1A = PJP^{-1}

Symbols

JJ
the Jordan form of AA
PP
an invertible matrix where the columns are generalized eigenvectors of AA
Jordan Form 02
J=diag(B(λ1,m1),B(λ2,m2),…,B(λk,mk))J = \text{diag}(B(λ_1, m_1), B(λ_2, m_2), \dots , B(λ_k, m_k))

Symbols

λi\lambda_i
an eigenvalue of AA for i=1,2,…,mi=1,2,\dots,m
mi∈Rm_i \in \mathbb{R}
such that ∑mi=n\sum m_i = n
Change of Basis 03
A′=Q−1APA' = Q^{-1} A P

Symbols

Pn×nP_{n\times n}
the change-of-basis matrix that satisfies (vi)(i=1)n(v_i)_{(i = 1)}^n
Qm×mQ_{m\times m}
the change-of-basis matrix that satisfies (wj)(j=1)m(w_j)_{(j = 1)}^m
Equation 04
A=UΣVHA = U \Sigma V^H

Symbols

U,VU, V
unitary
Σ\Sigma
singular values on the diagonal
Calculation 05
AV=UΣAV= UΣ

Symbols

V∈Cn×nV \in \mathbb{C}_{n\times n}
consists of vv’s
U∈Cm×mU \in \mathbb{C}_{m\times m}
consists of uu’s
Σ∈Rn×m\Sigma \in \mathbb{R}_{n\times m}
consists of eigenvalues on the diagonal and 0s elsewhere

S4 / Software Engineering

COCOMO 01
PM=a×(KDSI)b\text{PM} = a \times (\text{KDSI})^{b}

Symbols

PM\text{PM}
effort in person-months
KDSI\text{KDSI}
thousands of delivered source instructions
a,ba, b
constants that vary by mode
COCOMO 02
TDEV=c×(PM)d\text{TDEV} = c \times (\text{PM})^{d}

Symbols

TDEV\text{TDEV}
time to develop, in months.
c,dc,d
constants that vary by mode.
Productivity Adjustment 03
PMadjusted=PMbase×m\text{PM}_{adjusted} = \text{PM}_{base} \times m

Symbols

PMbase\text{PM}_{base}
effort from the COCOMO formula with standard mode parameters
mm
productivity multiplier
COCOMO II 04
Effort=A×(Size)B×∏EMi\text{Effort} = A \times (\text{Size})^{B} \times \prod \text{EM}_i

Symbols

AA
calibration constant. ≈2.94 in the standard model. Calibrated from the organization's historical project data.
Size\text{Size}
measured in KSLOC (thousands of source lines of code) or function points
BB
scaling exponent. B=0.91+0.01×∑SFiB = 0.91 + 0.01 \times \sum \text{SF}_i, where SFi\text {SF}_i is each Scale Factor's rating.
EMi\text{EM}_i
effort multipliers (17 cost drivers)
Timing Analysis 05
U=∑iCiTi≤1U = \sum_{i} \frac{C_i}{T_i} \leq 1

Symbols

CiC_i
execution time of process ii
TiT_i
period of process ii
Real-Time

S4 / Theory of Computing

Regular Grammar 01
B→aB → a

Symbols

B,CB, C
non-terminals
aa
a terminal
Equation 02
αAβ→αXβ\alpha A \beta \to \alpha X \beta

Symbols

A∈VA \in V
single non-terminal on LHS surrounded by context
X∈(V∪Σ)+X \in (V \cup \Sigma)^+
non-empty replacement
α,β∈(V∪Σ)∗\alpha, \beta \in (V \cup \Sigma)^*
arbitrary context strings
Equation 03
M=(Q,Σ,q0,A,δ)M = (Q, \Sigma, q_0, A, \delta)

Symbols

QQ
a finite set of states
Σ\Sigma
a finite input alphabet
q0∈Qq_0 \in Q
the start state
A⊆QA \subseteq Q
the set of accepting states
δ:Q×Σ→Q\delta : Q \times \Sigma → Q
the transition function
Equation 05
(Q,Σ,Γ,q0,Z0,A,δ)(Q, \Sigma, \Gamma, q_0, Z_0, A, \delta)

Symbols

QQ
states
Σ\Sigma
input alphabet
Γ\Gamma
stack alphabet
q0q_0
initial state
Z0Z_0
initial stack symbol
AA
accepting states
δ\delta
transition function
Configuration 06
(q,x,α)(q, x, \alpha)

Symbols

qq
current state
xx
remaining input
α\alpha
stack contents
Equation 07
T=(Q,Σ,Γ,q0,δ)T = (Q, \Sigma, \Gamma, q_0, \delta)

Symbols

QQ
finite set of states
Σ⊃{ ha,hr }\Sigma \supset \set{ h_a, h_r }
input alphabet
Γ⊃{ Λ }\Gamma \supset \set{ \Lambda }
tape alphabet
q0q_0
start state
δ:Q×Σ→Q×Γ×{ L,R,S }δ: Q \times \Sigma \rightarrow Q \times \Gamma \times \set{L,R,S}
transition function
n-Tape Turing Machine 08
M=(Q,Σ,Γ,δ,q0,F)M = (Q, \Sigma, \Gamma, \delta, q_0, F)

Symbols

QQ
finite set of states
Σ\Sigma
input alphabet
Γ\Gamma
tape alphabet (Σ⊆Γ\Sigma \subseteq \Gamma)
q0q_0
initial state
FF
set of accepting states
δ:Q×Γn→Q×Γn×{L,R,S}n\delta: Q \times \Gamma^n \rightarrow Q \times \Gamma^n \times \{L, R, S\}^n
transition function

S5 / Advanced Networking

Sampling 01
fs>2fmax⁡f_s > 2f_{\max}

Symbols

fsf_s
sampling frequency
fmax⁡f_{\max}
highest input frequency
Differential PCM 02
en=xn−x^ne_n = x_n - \hat{x}_n

Symbols

ene_n
prediction error
xnx_n
actual sample
x^n\hat{x}_n
predicted sample

S5 / Business Economics and Financial Accounting

Taxation 01
Consumer burden=EsEd+Es×taxProducer burden=EdEd+Es×tax\begin{aligned} \text{Consumer burden} &= \frac{E_s}{E_d + E_s} \times \text{tax} \\ \text{Producer burden} &= \frac{E_d}{E_d + E_s} \times \text{tax} \end{aligned}

Symbols

EdE_d
Price elasticity of demand (absolute value)
E2E_2
Price elasticity of supply
Consumer Surplus 02
CS=12×Q∗×(Pmax⁡−P∗)CS = \frac{1}{2} \times Q^* \times (P_{\max} - P^*)

Symbols

Q∗Q^*
equilibrium quantity
Pmax⁡P_{\max}
choke price, the price at which quantity demanded is 0
P∗P^*
equilibrium price
Producer Surplus 03
PS=12×Q∗×(P∗−Pmin⁡)PS = \frac{1}{2} \times Q^* \times (P^* - P_{\min})

Symbols

Pmin⁡P_{\min}
the price at which quantity supplied is 0
Output and Price 04
Qm<QcandPm>PcQ_m < Q_c \quad \text{and} \quad P_m > P_c

Symbols

QmQ_m
monopoly quantity
QcQ_c
competitive quantity
PmP_m
monopoly price
PcP_c
competitive price
Monopoly
Marginal Cost Pricing 05
MR=MCMR = MC

Symbols

MRMR
marginal revenue, revenue from 1 additional unit sold
MCMC
marginal cost, cost of producing 1 additional unit
ATCATC
average total cost, total cost per unit of output
Monopoly
Total Revenue 06
TR=P×QTR = P \times Q

Symbols

TRTR
Total Revenue
PP
Price
QQ
Quantity sold
Marginal Revenue Product 07
MRPL=MPL×P\text{MRP}_L = \text{MP}_L \times P

Symbols

MRPL\text{MRP}_L
Marginal Revenue Product of Labour
MPL\text{MP}_L
Marginal Product of Labour
PP
Price of output

S5 / Calculus

Singularities Inside and Outside 01
∫Cf(z) dz=2πi∑ca\int_C f(z)\,\text{d}z = 2\pi i \sum c_a

Symbols

cac_a
coefficient of 1z−a\dfrac{1} {z - a}, summed over the singular points aa inside CC
Laurent's Theorem 02
cn=12πi∫∣ζ−α∣=sf(ζ)(ζ−α)n+1 dζc_n = \frac{1}{2\pi i}\int_{|\zeta-\alpha|=s} \frac{f(\zeta)}{(\zeta-\alpha)^{n+1}}\,\text{d}\zeta

Symbols

nn
an integer
ss
any radius with r<s<Rr < s < R
Equation 03
log⁡z=ln⁡∣z∣+iarg⁡z={ ln⁡∣z∣+i(Arg⁡z+2kπ):k∈Z },z≠0\log z = \ln|z| + i\arg z = \{\,\ln|z| + i(\operatorname{Arg} z + 2k\pi) : k \in \mathbb{Z}\,\}, \qquad z \ne 0

Symbols

Arg⁡z\operatorname{Arg} z
principal argument of zz, the unique argument with −π<Arg⁡z≤π-\pi < \operatorname{Arg} z \le \pi
arg⁡z\arg z
{Arg⁡z+2kπ:k∈Z}\{\operatorname{Arg} z + 2k\pi : k \in \mathbb{Z}\}
Logarithm
Equation 04
∑n=0∞cn(z−α)n\sum_{n=0}^{\infty} c_n (z-\alpha)^n

Symbols

α\alpha
center, a fixed complex number
cnc_n
coefficients, fixed complex numbers
zz
a complex variable
Rectangular Coordinates 05
P=(x,y,z)P = (x, y, z)

Symbols

xx
signed distance from the yzyz-plane
yy
signed distance from the zxzx-plane
zz
signed distance from the xyxy-plane
Cylindrical Coordinates 06
x=rcos⁡θ,y=rsin⁡θ,z=z,r≥0x = r\cos\theta, \quad y = r\sin\theta, \quad z = z, \quad r \ge 0

Symbols

rr
distance from the zz-axis
θ\theta
angle in the xyxy-plane from the positive xx-axis
zz
signed height above the xyxy-plane
Spherical Coordinates 07
x=ρsin⁡ϕcos⁡θ,y=ρsin⁡ϕsin⁡θ,z=ρcos⁡ϕx = \rho\sin\phi\cos\theta, \quad y = \rho\sin\phi\sin\theta, \quad z = \rho\cos\phi

Symbols

ρ\rho
distance from the origin to the point
θ\theta
angle in the xyxy-plane from the positive xx-axis
ϕ\phi
angle from the positive zz-axis
Area Formula 08
A(D)=12∮Cx dy−y dxA(D) = \frac{1}{2}\oint_C x\,\text{d}y - y\,\text{d}x
Parametric Form 09
r(u,v)=x(u,v)i^+y(u,v)j^+z(u,v)k^,(u,v)∈R\boldsymbol{r}(u,v) = x(u,v)\hat{i} + y(u,v)\hat{j} + z(u,v)\hat{k}, \quad (u,v) \in R

Symbols

RR
region in the uvuv-plane
x,y,zx, y, z
component functions of uu and vv
Surface

S5 / Computer Security

Encryption 01
c=25−mmod  26c = 25 - m \mod 26

Symbols

mm
plaintext letter, encoded as an integer 0 (a) to 25 (z)
cc
ciphertext letter, same encoding
Choosing a Generator 02
g(p−1)/qi≢1(modp)for every ig^{(p-1)/q_i} \not\equiv 1 \pmod p \quad \text{for every } i

Symbols

qiq_i
a distinct prime factor of p−1p - 1
Noninterference 03
∀L,H1,H2.LowOutput⁡(H1,L)=LowOutput⁡(H2,L)\forall L, H_1, H_2.\quad \operatorname{LowOutput}(H_1, L) = \operatorname{LowOutput}(H_2, L)

Symbols

LL
the low input
H1,H2H_1, H_2
any 2 possible high inputs
LowOutput⁡(H,L)\operatorname{LowOutput}(H, L)
the low output produced for inputs HH and LL
Equation 04
K=KDF(S,salt,info,L)K = \text{KDF}(S, \text{salt}, \text{info}, L)

Symbols

SS
input secret, such as a password or a Diffie-Hellmanshared secret
salt\text{salt}
non-secret random value
info\text{info}
context label binding the key to its purpose
LL
required output length
Key Derivation from Passwords 05
K=PBKDF2(HMAC,password,salt,c,L)K = \text{PBKDF2}(\text{HMAC}, \text{password}, \text{salt}, c, L)

Symbols

cc
iteration count
Equation 06
si=c1si−1⊕c2si−2⊕⋯⊕cnsi−n,i≥ns_i = c_1 s_{i-1} \oplus c_2 s_{i-2} \oplus \cdots \oplus c_n s_{i-n}, \quad i \ge n

Symbols

c1,…,cn∈{0,1}c_1, \ldots, c_n \in \{0, 1\}
the feedback taps, fixed for a given LFSR.
s0,…,sn−1s_0, \ldots, s_{n-1}
the initial state, acting as the secret key or seed.
Combining LFSRs 07
ki=f(si(1),si(2),…,si(m))k_i = f\left(s_i^{(1)}, s_i^{(2)}, \ldots, s_i^{(m)}\right)

Symbols

si(j)s_i^{(j)}
the output bit of the jj-th LFSR at step ii.
ff
a fixed nonlinear function of the mmcomponent outputs.
HMAC 08
HMACk(m)=H((k′⊕opad) ∥ H((k′⊕ipad) ∥ m))\text{HMAC}_k(m) = H\big((k' \oplus \text{opad}) \,\|\, H((k' \oplus \text{ipad}) \,\|\, m)\big)

Symbols

k′k'
key kkpadded with zeros to the hash block size, or hashed first if longer than a block
ipad\text{ipad}
the byte `0x36` repeated to the block size
opad\text{opad}
the byte `0x5c` repeated to the block size
Encryption 09
cσ(i)=mic_{\sigma(i)} = m_i

Symbols

mim_i
plaintext character at position iiin the block
cjc_j
ciphertext character at position jjin the block
Computational Indistinguishability 10
∣Pr⁡[D(Xn)=1]−Pr⁡[D(Yn)=1]∣\Big\lvert \Pr[D(X_n) = 1] - \Pr[D(Y_n) = 1] \Big\rvert

Symbols

XnX_n
first distribution at security parameter nn
YnY_n
second distribution at security parameter nn
DD
PPT distinguisher
Pr⁡[D(Xn)=1]\Pr[D(X_n) = 1]
probability that DD outputs 11 for a sample from XnX_n
Pr⁡[D(Yn)=1]\Pr[D(Y_n) = 1]
probability that DD outputs 11 for a sample from YnY_n
nn
security parameter
Key Scheduling Algorithm 11
S[i]=i,i=0,…,255j=0for i=0 to 255:j=(j+S[i]+K[i mod len(K)]) mod 256swap S[i] and S[j]\begin{aligned} &S[i] = i, \quad i = 0, \ldots, 255 \\ &j = 0 \\ &\text{for } i = 0 \text{ to } 255: \\ &\quad j = (j + S[i] + K[i \bmod \text{len}(K)]) \bmod 256 \\ &\quad \text{swap } S[i] \text{ and } S[j] \end{aligned}

Symbols

ii
iterates once over every index in order.
jj
a running value seeded by the key, deciding which entry iiis swapped with.
RC4
Encryption 12
c=m+kmod  26c = m + k \mod 26

Symbols

mm
plaintext letter, encoded as an integer 0 (a) to 25 (z)
kk
the shift value, the secret key
Attacking 13
Δ[X,Yk]=12∑u∈V∣Pr⁡X←Dstd[X=u]−Pr⁡Yk←Dk[Yk=u]∣\Delta[X, Y_k] = \frac{1}{2}\sum_{u \in V} \left| \Pr_{X \leftarrow D_{\text{std}}}[X = u] - \Pr_{Y_k \leftarrow D_k}[Y_k = u] \right|

Symbols

XX
random variable distributed according to the standard character frequency
YkY_k
random variable distributed according to the shift kk
VV
the set of values which can occur for XX or YkY_kwith non-zero probability

S5 / Image Processing

CMYK 01
[0,1][0, 1]

Symbols

CC
cyan, absorbs red
MM
magenta, absorbs green
YY
yellow, absorbs blue
KK
key, black ink added to deepen dark tones and reduce colored ink use
Color
Indexed 02
Strue=24WHS_{\text{true}} = 24 W H

Symbols

W,HW, H
image width and height in pixels
SS
total storage in bits
Color
One-Dimensional DCT 03
X(k)=α(k)∑n=0N−1x(n)cos⁡(2n+1)kπ2NX(k) = \alpha(k) \sum_{n=0}^{N-1} x(n) \cos\frac{(2n+1)k\pi}{2N}

Symbols

nn
sample index
kk
frequency index
NN
number of samples
x(n)x(n)
sample value at index nn
X(k)X(k)
coefficient for frequency kk
α(0)\alpha(0)
1/N\sqrt{1 / N}
α(k) for k≥1\alpha(k) \ \text{for} \ k \geq 1
2/N\sqrt{2 / N}
Two-Dimensional DCT 04
f(x,y)=∑u=0N−1∑v=0N−1α(u) α(v) F(u,v)cos⁡(2x+1)uπ2Ncos⁡(2y+1)vπ2Nf(x, y) = \sum_{u=0}^{N-1} \sum_{v=0}^{N-1} \alpha(u)\,\alpha(v)\, F(u, v) \cos\frac{(2x+1)u\pi}{2N} \cos\frac{(2y+1)v\pi}{2N}

Symbols

x,yx, y
column and row indices
u,vu, v
horizontal and vertical frequency indices
f(x,y)f(x, y)
pixel value at column xx and row yy
F(u,v)F(u, v)
coefficient at horizontal frequency uu and vertical frequency vv
F(0,0)F(0, 0)
DC coefficient, equal to NNtimes the block mean
Ordered Dithering 05
Tij=Dij+0.5n2qij={1v>Tij0v≤Tij\begin{aligned} T_{ij} &= \frac{D_{ij} + 0.5}{n^2} \\ q_{ij} &= \begin{cases} 1 & v > T_{ij} \\ 0 & v \leq T_{ij} \end{cases} \end{aligned}

Symbols

TijT_{ij}
threshold at sub-pixel (i,j)(i,j)
DijD_{ij}
dither matrix entry at sub-pixel (i,j)(i,j)
qijq_{ij}
bi-level output at sub-pixel (i,j)(i,j)
nn
dither matrix order
Dithering
Equation 06
Btotal=Bavg×NB_\text{total} = B_\text{avg} \times N

Symbols

LL
number of grey levels
NN
number of pixels
lil_i
grey level ii
rir_i
number of pixels with grey level lil_i
P(li)P(l_i)
probability of grey level lil_i
Entropy
Average Code Length 07
Lavg≥BavgRcoding=Lavg−BavgL_\text{avg} \ge B_\text{avg} \qquad R_\text{coding} = L_\text{avg} - B_\text{avg}

Symbols

l(k)l(k)
codeword length of grey level kkin bits
LavgL_\text{avg}
average codeword length in bits per pixel
bb
size of the coded image in bits
RcodingR_\text{coding}
coding redundancy in bits per pixel
Entropy
Image Gradient 08
∇f(x,y)=[GxGy]=[∂f(x,y)∂x∂f(x,y)∂y]\nabla f(x,y) = \begin{bmatrix} G_x \\ G_y \end{bmatrix} = {\Large \begin{bmatrix} \frac{\partial f(x,y)}{\partial x} \\ \frac{\partial f(x,y)}{\partial y} \end{bmatrix}}

Symbols

f(x,y)f(x,y)
image intensity at (x,y)(x,y)
GxG_x
horizontal intensity derivative
GyG_y
vertical intensity derivative
Thresholded Edge Maps 09
g(x,y)={1∣∇f(x,y)∣≥T0∣∇f(x,y)∣<Tg(x,y) = \begin{cases} 1 & \lvert\nabla f(x,y)\rvert \geq T \\ 0 & \lvert\nabla f(x,y)\rvert < T \end{cases}

Symbols

g(x,y)g(x,y)
binary edge-map value
TT
minimum gradient magnitude accepted as an edge
Laplacian of Gaussian 10
LoG⁡(f)=∇2(Gσ∗f)\operatorname{LoG}(f) = \nabla^2(G_\sigma * f)

Symbols

GσG_\sigma
Gaussian kernel with standard deviation σ\sigma
∗*
convolution
Fixed Length Coding 11
l(k)=⌈log⁡2L⌉l(k) = \lceil \log_2 L \rceil

Symbols

LL
number of grey levels
l(k)l(k)
codeword length of grey level kk
Delta Coding 12
D(x,y)=I(x+1,y)−I(x,y)D(x,y) = I(x+1,y) - I(x,y)

Symbols

I(x,y)I(x,y)
grey level at column xx and row yy
D(x,y)D(x,y)
difference between a pixel and its right neighbour
Image Pyramid Coding 13
Gk=Lk+expand(Gk+1)G_k = L_k + \text{expand}(G_{k+1})

Symbols

II
original image
GkG_k
image at level kk, with G0G_0the full-resolution image
KK
index of the coarsest level
reduce\text{reduce}
Gaussian low-pass filtering followed by downsampling by 2 in each dimension
expand\text{expand}
upsampling by 2 in each dimension with interpolation
LkL_k
difference layer at level kk
Bit-Plane Coding 14
I(x,y)=∑k=0m−1ak(x,y) 2kI(x,y) = \sum_{k=0}^{m-1} a_k(x,y)\, 2^k

Symbols

ak(x,y)a_k(x,y)
bit kk of the pixel at (x,y)(x,y), with k=0k = 0the least significant bit
Bit-Plane Coding 15
am−1=gm−1,ak=gk⊕ak+1a_{m-1} = g_{m-1}, \qquad a_k = g_k \oplus a_{k+1}

Symbols

gkg_k
bit kkof the Gray coded pixel
⊕\oplus
exclusive OR
Psychovisual Redundancy 16
I′(x,y)=∑i,jh(i,j) I(x+i,y+j)∑i,jh(i,j)=1I'(x,y) = \sum_{i,j} h(i,j)\, I(x+i, y+j) \qquad \sum_{i,j} h(i,j) = 1

Symbols

m,m′m, m'
bits per pixel before and after re-quantizing
I′(x,y)I'(x,y)
pixel value after the operation
h(i,j)h(i,j)
Gaussian weight at offset (i,j)(i,j)
Filter Kernel 17
P(x,y)=∑i=−NN∑j=−MMH(i,j) I(x−i,y−j)P(x,y) = \sum_{i=-N}^{N}\sum_{j=-M}^{M} H(i,j)\,I(x-i,y-j)

Symbols

I(x,y)I(x,y)
input image intensity
P(x,y)P(x,y)
filtered output intensity
H(i,j)H(i,j)
kernel coefficient at offset (i,j)(i,j)
Equation 18
(x,y)(x,y)

Symbols

x,yx, y
spatial coordinates of a point in the image
I(x,y)I(x,y)
light intensity at that point, bounded between a minimum and maximum
Digital Image Model 19
[I(0,0)I(0,1)⋯I(0,n−1)I(1,0)I(1,1)⋯I(1,n−1)⋮⋮⋱⋮I(m−1,0)I(m−1,1)⋯I(m−1,n−1)]\begin{bmatrix} I(0,0) & I(0,1) & \cdots & I(0,n-1) \\ I(1,0) & I(1,1) & \cdots & I(1,n-1) \\ \vdots & \vdots & \ddots & \vdots \\ I(m-1,0) & I(m-1,1) & \cdots & I(m-1,n-1) \end{bmatrix}

Symbols

mm
number of rows
nn
number of columns
ii
row index, 0≤i≤m−10 \le i \le m-1
jj
column index, 0≤j≤n−10 \le j \le n-1
LL
number of intensity levels, usually L=2kL = 2^k for a bit depth kk
Objective Assessment 20
mean squared error=1MN∑x=1M∑y=1N(p(x,y)−r(x,y))2\text{mean squared error} = \frac{1}{MN}\sum_{x=1}^{M}\sum_{y=1}^{N}(p(x,y) - r(x,y))^2

Symbols

M,NM, N
image dimensions
p(x,y)p(x,y)
pixel value in the processed image
r(x,y)r(x,y)
pixel value in the source image
Size Independence 21
PSNR=10log⁡10MAX2MSE\text{PSNR} = 10\log_{10}\frac{\text{MAX}^2}{\text{MSE}}

Symbols

MAX\text{MAX}
maximum possible pixel value
Backward Mapping 22
kx=new widthsource widthky=new heightsource heightk_x = \frac{\text{new width}}{\text{source width}} \qquad k_y = \frac{\text{new height}}{\text{source height}}

Symbols

kx,kyk_x, k_y
horizontal and vertical scale factors
(sx,sy)(s_x, s_y)
source position, generally non-integer
Bilinear Interpolation 23
v= v00 (1−fx)(1−fy)+v10 fx (1−fy)+v01 (1−fx) fy+v11 fx fy\begin{aligned} v = \ & v_{00}\,(1 - f_x)(1 - f_y) \\ {}+{} & v_{10}\,f_x\,(1 - f_y) \\ {}+{} & v_{01}\,(1 - f_x)\,f_y \\ {}+{} & v_{11}\,f_x\,f_y \end{aligned}

Symbols

fx,fyf_x, f_y
fractional distances from the top-left neighbour, in [0,1)[0, 1)
v00v_{00}
top-left neighbour value, at (⌊sx⌋,⌊sy⌋)(\lfloor s_x \rfloor, \lfloor s_y \rfloor)
v10v_{10}
top-right, v01v_{01}: bottom-left, v11v_{11}: bottom-right
vv
interpolated value, rounded to an integer
Gaussian Pyramid 24
Gn+1=downsample⁡(H∗Gn)G_{n+1} = \operatorname{downsample}(H * G_n)

Symbols

G0G_0
the original image
Gn+1G_{n+1}
GnG_nfiltered with a Gaussian low-pass filter, then downsampled by 2
Gaussian Pyramid Filter 25
Gn+1(x,y)=∑i=−22∑j=−22H(i,j) Gn(2x−i,2y−j)G_{n+1}(x,y) = \sum_{i=-2}^{2}\sum_{j=-2}^{2} H(i,j)\,G_n(2x-i,2y-j)

Symbols

HH
2-D Gaussian-pyramid filter
GnG_n
input pyramid level
Gn+1G_{n+1}
filtered and downsampled pyramid level
Laplacian Pyramid 26
Ln=Gn−U(Gn+1)L_n = G_n - U(G_{n+1})

Symbols

Gn,Gn+1G_n, G_{n+1}
consecutive levels of the Gaussian pyramid above
U(Gn+1)U(G_{n+1})
Gn+1G_{n + 1} upsampled back to level nn's resolution
LnL_n
band-pass detail at level nn
Trellis Quantization 27
J=D+λRJ = D + \lambda R

Symbols

DD
squared error between the original and the dequantized coefficients
RR
number of bits the choice costs after run-length and Huffman coding
λ\lambda
weight that sets the exchange rate between error and bits
AC Coefficients 28
RS=16R+s\text{RS} = 16 R + s

Symbols

RR
number of zeros since the previous nonzero coefficient, from 0 to 15
ss
category of the nonzero coefficient, from 1 to 10
Decoding 29
f^(x,y)=clamp(round(IDCT(Fq(u,v) Q(u,v)))+128, 0, 255)\hat{f}(x, y) = \text{clamp}\left( \text{round}\left( \text{IDCT}\big(F_q(u, v)\, Q(u, v)\big) \right) + 128,\ 0,\ 255 \right)

Symbols

Fq(u,v)F_q(u, v)
quantized coefficient from the entropy decoder
Q(u,v)Q(u, v)
quantization table entry
IDCT\text{IDCT}
inverse 2D DCT of the block
clamp(a,0,255)\text{clamp}(a, 0, 255)
aa limited to the range [0,255][0, 255]
Block 30
xi=⌈X⋅HiHmax⁡⌉yi=⌈Y⋅ViVmax⁡⌉x_i = \left\lceil X \cdot \frac{H_i}{H_{\max}} \right\rceil \qquad y_i = \left\lceil Y \cdot \frac{V_i}{V_{\max}} \right\rceil

Symbols

xi,yix_i, y_i
dimensions of component iiin samples
Hmax⁡,Vmax⁡H_{\max}, V_{\max}
largest sampling factors across all components
Discrete Cosine Transform 31
f(x,y)=14∑u=07∑v=07C(u)C(v)F(u,v)cos⁡(2x+1)uπ16cos⁡(2y+1)vπ16f(x, y) = \frac{1}{4} \sum_{u=0}^{7} \sum_{v=0}^{7} C(u) C(v) F(u, v) \cos\frac{(2x+1)u\pi}{16} \cos\frac{(2y+1)v\pi}{16}

Symbols

x,yx, y
column and row of a sample in the block
u,vu, v
horizontal and vertical frequency indices, from 0 to 7
C(k)C(k)
12\frac{1}{\sqrt{2}} for k=0k = 0, otherwise 1
F(0,0)F(0, 0)
DC coefficient, equal to 8 times the block mean
F(u,v) for (u,v)≠(0,0)F(u, v) \ \text{for} \ (u, v) \neq (0, 0)
AC coefficients
Spatial Filtering 32
(M∘I)(x,y)=∑i=−NN∑j=−MMM(i,j) I(x+i,y+j)(M \circ I)(x,y) = \sum_{i=-N}^{N} \sum_{j=-M}^{M} M(i,j)\, I(x+i, y+j)

Symbols

M(i,j)M(i,j)
mask, typically odd sized with dimensions (2N+1)×(2M+1)(2N+1) \times (2M+1)
I(x,y)I(x,y)
input image
Gaussian Noise 33
p(z)=12π σexp⁡[−(z−μ)22σ2]p(z) = \frac{1}{\sqrt{2\pi}\,\sigma} \exp\left[-\frac{(z-\mu)^2}{2\sigma^2}\right]

Symbols

zz
noise value added to a pixel
μ\mu
mean, taken as 00so the noise neither brightens nor darkens on average
σ\sigma
standard deviation, sets the noise strength
Noise
Uniform Noise 34
p(z)={1b−aa≤z≤b0otherwisep(z) = \begin{cases} \dfrac{1}{b-a} & a \le z \le b \\[2mm] 0 & \text{otherwise} \end{cases}

Symbols

aa
lower bound of the noise range
bb
upper bound of the noise range
Noise
Salt and Pepper Noise 35
p(z)={Paz=zmin⁡Pbz=zmax⁡1−Pa−Pbpixel unchangedp(z) = \begin{cases} P_a & z = z_{\min} \\ P_b & z = z_{\max} \\ 1 - P_a - P_b & \text{pixel unchanged} \end{cases}

Symbols

zmin⁡,zmax⁡z_{\min}, z_{\max}
darkest and brightest intensity values
PaP_a
probability a pixel is set to zmin⁡z_{\min}, seen as pepper
PbP_b
probability a pixel is set to zmax⁡z_{\max}, seen as salt
Noise
Frame Averaging 36
Output=1n∑i=1nFramei=F+1n∑i=1nNi\text{Output} = \frac{1}{n}\sum_{i=1}^{n} \text{Frame}_i = F + \frac{1}{n}\sum_{i=1}^{n} N_i

Symbols

FF
static source scene frame
NiN_i
noise in frame ii
Noise
Lens Equation 37
1f=1u+1v\frac{1}{f} = \frac{1}{u} + \frac{1}{v}

Symbols

ff
focal distance of the lens
uu
distance to the object
vv
distance to the image
Grey Level Histogram 38
P(k)=N(k)nP(k) = \frac{N(k)}{n}

Symbols

N(k)N(k)
number of pixels with intensity kk
nn
total number of pixels
Characteristics 39
μ=∑kk P(k)σ2=∑k(k−μ)2P(k)\mu = \sum_k k\,P(k) \qquad \sigma^2 = \sum_k (k - \mu)^2 P(k)

Symbols

μ\mu
mean intensity
σ2\sigma^2
variance of intensity
Brightness and Contrast 40
s=ars = a r

Symbols

rr
input intensity
ss
output intensity
cc
shift constant, added to move brightness
aa
gain constant, multiplied to change contrast
Point Processing Functions 41
s=round⁡((L−1) A(r))s = \operatorname{round}\big((L-1)\,A(r)\big)

Symbols

LL
number of grey levels
a,ba, b
lower and upper bounds of the chosen range
cc
scaling constant
(r1,s1),(r2,s2)(r_1, s_1), (r_2, s_2)
control points of the piecewise linear curve
rmin⁡,rmax⁡r_{\min}, r_{\max}
limits of the input range
smin⁡,smax⁡s_{\min}, s_{\max}
limits of the output range
kk
bit-plane index, with 00the least significant bit
A(r)A(r)
accumulated histogram
Histogram Matching 42
LUT(g)=arg⁡min⁡r ∣Cr(r)−Ct(g)∣\text{LUT}(g) = \arg\min_{r} \, \lvert C_r(r) - C_t(g) \rvert

Symbols

Ct(g)C_t(g)
accumulated probability of test level ≤g\le g
Cr(r)C_r(r)
accumulated probability of reference level ≤r\le r
Equation 43
height=NrR\text{height} = \frac{N_r}{R}

Symbols

RR
resolution in dots per unit length
NcN_c
number of columns
NrN_r
number of rows
Δx:Δy\Delta x : \Delta y
pixel aspect ratio

S5 / Numerical Methods

Standard Form 01
y′′=f(x,y,y′),a≤x≤b,y(a)=α, y(b)=βy'' = f(x, y, y'), \quad a \leq x \leq b, \quad y(a) = \alpha,\ y(b) = \beta
Computing $L$ 03
lki=aki−∑j=1i−1lijlkjlii,i=1,2,…,k−1l_{ki} = \frac{a_{ki} - \sum\limits_{j=1}^{i-1} l_{ij}l_{kj}}{l_{ii}}, \quad i = 1, 2, \ldots, k-1
Computing $L$ 04
lkk=akk−∑j=1k−1lkj2l_{kk} = \sqrt{a_{kk} - \sum\limits_{j=1}^{k-1} l_{kj}^2}
General Form 05
min⁡xf(x)subject togi(x)=ci,i=1,…,nhj(x)≤dj,j=1,…,m\begin{aligned} \min_x \quad & f(x) \\ \text{subject to} \quad & g_i(x) = c_i, \quad i = 1, \ldots, n \\ & h_j(x) \leq d_j, \quad j = 1, \ldots, m \end{aligned}

Symbols

gi(x)=cig_i(x) = c_i
equality constraints
hj(x)≤djh_j(x) \leq d_j
inequality constraints
Standard Form 06
z=f(x)=a1x1+a2x2+⋯+anxnz = f(x) = a_1 x_1 + a_2 x_2 + \cdots + a_n x_n
Standard Form 07
ai1x1+ai2x2+⋯+ainxn≤bia_{i1} x_1 + a_{i2} x_2 + \cdots + a_{in} x_n \leq b_i
Equation 08
Sj(x)=aj+bj(x−xj)+cj(x−xj)2+dj(x−xj)3,x∈[xj,xj+1]S_j(x)=a_j+b_j(x-x_j)+c_j(x-x_j)^2+d_j(x-x_j)^3, \qquad x\in[x_j,x_{j+1}]
Equation 09
Sj(xj)=f(xj),Sj(xj+1)=f(xj+1)S_j(x_j)=f(x_j), \qquad S_j(x_{j+1})=f(x_{j+1})
Construction 10
hj=xj+1−xj,aj=f(xj),δj=aj+1−ajhjh_j=x_{j+1}-x_j, \qquad a_j=f(x_j), \qquad \delta_j=\frac{a_{j+1}-a_j}{h_j}
Construction 11
hj−1cj−1+2(hj−1+hj)cj+hjcj+1=3(δj−δj−1),j=1,…,n−1h_{j-1}c_{j-1}+2(h_{j-1}+h_j)c_j+h_jc_{j+1} =3(\delta_j-\delta_{j-1}), \qquad j=1,\ldots,n-1
Construction 12
bj=δj−hj3(2cj+cj+1),dj=cj+1−cj3hjb_j=\delta_j-\frac{h_j}{3}(2c_j+c_{j+1}), \qquad d_j=\frac{c_{j+1}-c_j}{3h_j}
Construction 14
2h0c0+h0c1=3(δ0−f′(x0)),2h_0c_0+h_0c_1=3\bigl(\delta_0-f'(x_0)\bigr),
Error 15
M=max⁡a≤x≤b∣f(4)(x)∣M=\max_{a\le x\le b}|f^{(4)}(x)|
Error 16
∣f(x)−S(x)∣≤5M384max⁡0≤j≤n−1(xj+1−xj)4|f(x)-S(x)|\le\frac{5M}{384} \max_{0\le j\le n-1}(x_{j+1}-x_j)^4
Mesh Points 17
ti=a+ih,i=0,1,…,Nt_i = a + ih, \quad i = 0, 1, \ldots, N
Difference Equation 18
y(ti+1)=y(ti)+hf(ti,y(ti))+h22y′′(ξi),ξi∈(ti,ti+1)y(t_{i+1}) = y(t_i) + h f(t_i, y(t_i)) + \frac{h^2}{2} y''(\xi_i), \quad \xi_i \in (t_i, t_{i+1})
Difference Equation 19
w0=αwi+1=wi+hf(ti,wi),i=0,1,…,N−1\begin{aligned} w_0 &= \alpha \\ w_{i+1} &= w_i + h f(t_i, w_i), \quad i = 0, 1, \ldots, N-1 \end{aligned}
Theoretical Error Bound 20
∣y(ti)−wi∣≤hM2L(eL(ti−a)−1)|y(t_i) - w_i| \leq \frac{hM}{2L}\left( e^{L(t_i - a)} - 1 \right)

Symbols

MM
bound on ∣y′′∣|y''| over [a,b][a, b]
LL
Lipschitz constant of ff
Computational Error Bound 21
u0=α+δ0ui+1=ui+hf(ti,ui)+δi+1,∣δi∣<δ\begin{aligned} u_0 &= \alpha + \delta_0 \\ u_{i+1} &= u_i + h f(t_i, u_i) + \delta_{i+1}, \quad |\delta_i| < \delta \end{aligned}
Computational Error Bound 22
∣y(ti)−ui∣≤1L(hM2+δh)(eL(ti−a)−1)+∣δ0∣ eL(ti−a)|y(t_i) - u_i| \leq \frac{1}{L}\left( \frac{hM}{2} + \frac{\delta}{h} \right)\left( e^{L(t_i - a)} - 1 \right) + |\delta_0|\, e^{L(t_i - a)}
Computational Error Bound 23
h=2δMh = \sqrt{\frac{2\delta}{M}}
Fixed-Point Iteration 25
x⃗(k)=G(x⃗(k−1)),k≥1\vec{x}^{(k)} = G(\vec{x}^{(k-1)}), \quad k \geq 1
Fixed-Point Theorem in n-Space 26
∣∂gi(x⃗)∂xj∣≤Kn,∀x⃗∈D,  i,j=1,2,…,n\left|\frac{\partial g_i(\vec{x})}{\partial x_j}\right| \leq \frac{K}{n}, \quad \forall \vec{x} \in D, \; i, j = 1, 2, \ldots, n
Solve the System 27
f1(x1,x2,x3)=3x1−cos⁡(x2x3)−12f2(x1,x2,x3)=x12−81(x2+0.1)2+sin⁡x3+1.06f3(x1,x2,x3)=e−x1x2+20x3+10π−33\begin{aligned} f_1(x_1, x_2, x_3) &= 3x_1 - \cos(x_2 x_3) - \tfrac{1}{2} \\ f_2(x_1, x_2, x_3) &= x_1^2 - 81(x_2 + 0.1)^2 + \sin x_3 + 1.06 \\ f_3(x_1, x_2, x_3) &= e^{-x_1 x_2} + 20x_3 + \tfrac{10\pi - 3}{3} \end{aligned}
Rearrangement Choice 29
g1(x)=1−x3,g2(x)=1x2+1g_1(x) = 1 - x^3, \qquad g_2(x) = \frac{1}{x^2 + 1}
Forward Elimination 30
Rj−aj1a11R1→Rj,j=2,3,…,nR_j - \frac{a_{j1}}{a_{11}} R_1 \to R_j, \quad j = 2, 3, \ldots, n
Forward Elimination 31
Rj−aji(i)aii(i)Ri→Rj,j=i+1,i+2,…,nR_j - \frac{a_{ji}^{(i)}}{a_{ii}^{(i)}} R_i \to R_j, \quad j = i+1, i+2, \ldots, n
Forward Elimination 32
(a11a12⋯a1nb1(1)0a22(1)⋯a2nb2(2)⋮⋱⋮⋮00⋯ann(n−1)bn(n−1))\begin{pmatrix} a_{11} & a_{12} & \cdots & a_{1n} & b_1^{(1)} \\ 0 & a_{22}^{(1)} & \cdots & a_{2n} & b_2^{(2)} \\ \vdots & & \ddots & \vdots & \vdots \\ 0 & 0 & \cdots & a_{nn}^{(n-1)} & b_n^{(n-1)} \end{pmatrix}
Back Substitution 33
xn=bn(n−1)ann(n−1)x_n = \frac{b_n^{(n-1)}}{a_{nn}^{(n-1)}}
Back Substitution 34
xi=bi(i−1)−∑j=i+1naij(i−1)xjaii(i−1),i=n−1,n−2,…,1x_i = \frac{b_i^{(i-1)} - \sum\limits_{j=i+1}^{n} a_{ij}^{(i-1)}x_j}{a_{ii}^{(i-1)}}, \quad i = n-1, n-2, \ldots, 1
Scaled Partial Pivoting 35
sj=max⁡1≤k≤n∣ajk∣s_j = \max_{1 \leq k \leq n} |a_{jk}|
Ill Conditioning 36
(1111.0001)(xy)=(22.0001)\begin{pmatrix} 1 & 1 \\ 1 & 1.0001 \end{pmatrix}\begin{pmatrix}x\\y\end{pmatrix} = \begin{pmatrix}2\\2.0001\end{pmatrix}
Ill Conditioning 37
(1111.0001)(xy)=(22.0002)\begin{pmatrix} 1 & 1 \\ 1 & 1.0001 \end{pmatrix}\begin{pmatrix}x\\y\end{pmatrix} = \begin{pmatrix}2\\2.0002\end{pmatrix}
Condition Number 38
κ(A)=∥A∥ ∥A−1∥\kappa(A) = \|A\| \, \|A^{-1}\|
Condition Number 39
{x+y=2x+1.001y=2{x+y=2x+1.001y=2.001\begin{cases} x + y = 2 \\ x + 1.001y = 2 \end{cases} \qquad \begin{cases} x + y = 2 \\ x + 1.001y = 2.001 \end{cases}
Example 1 40
2x1+x2−x3=8−3x1−x2+2x3=−11−2x1+x2+2x3=−3\begin{aligned} 2x_1 + x_2 - x_3 &= 8 \\ -3x_1 - x_2 + 2x_3 &= -11 \\ -2x_1 + x_2 + 2x_3 &= -3 \end{aligned}
Example 2 41
3x2+x3=92x1+2x2−x3=34x1+2x2+x3=11\begin{aligned} 3x_2 + x_3 &= 9 \\ 2x_1 + 2x_2 - x_3 &= 3 \\ 4x_1 + 2x_2 + x_3 &= 11 \end{aligned}
Example 3 42
4x1+100x2−50x3=54x1+x2+2x3=42x1−x3=1\begin{aligned} 4x_1 + 100x_2 - 50x_3 &= 54 \\ x_1 + x_2 + 2x_3 &= 4 \\ 2x_1 - x_3 &= 1 \end{aligned}
Example 4 43
2x1−x2+x3=3x1+x2+4x3=15x1+2x2−2x3=−1\begin{aligned} 2x_1 - x_2 + x_3 &= 3 \\ x_1 + x_2 + 4x_3 &= 15 \\ x_1 + 2x_2 - 2x_3 &= -1 \end{aligned}
Equation 44
y′(t)=f(t,y),a≤t≤b,y(a)=αy'(t) = f(t, y), \quad a \leq t \leq b, \quad y(a) = \alpha
Lipschitz Condition 45
∣f(t,y1)−f(t,y2)∣≤L∣y1−y2∣|f(t, y_1) - f(t, y_2)| \leq L|y_1 - y_2|
Stable Solution 46
z′(t)=f(t,z)+δ(t),a≤t≤b,z(a)=α+δ0z'(t) = f(t, z) + \delta(t), \quad a \leq t \leq b, \quad z(a) = \alpha + \delta_0
Stable Solution 47
t∈[a,b]t \in [a, b]

Symbols

kk
stability constant, independent of ε\varepsilon
Equation 48
x0<x1<⋯<xn,x_0 < x_1 < \cdots < x_n,
Equation 49
ϕ(xi)=yi,i=0,1,…,n\phi(x_i)=y_i, \qquad i=0,1,\ldots,n
Polynomial Interpolation 50
pn(xi)=yip_n(x_i)=y_i
Polynomial Interpolation 51
pn(x)=∑i=0nyiLi(x),Li(x)=∏j=0j≠inx−xjxi−xjp_n(x)=\sum_{i=0}^{n}y_iL_i(x), \qquad L_i(x)=\prod_{\substack{j=0\\j\ne i}}^{n}\frac{x-x_j}{x_i-x_j}
Polynomial Interpolation 52
Li(xj)={1,i=j,0,i≠j.L_i(x_j)= \begin{cases} 1, & i=j,\\ 0, & i\ne j. \end{cases}
Other Forms 53
ϕ(x)=a02+∑k=1m(akcos⁡(kx)+bksin⁡(kx))\phi(x)=\frac{a_0}{2}+\sum_{k=1}^{m}\left(a_k\cos(kx)+b_k\sin(kx)\right)
Other Forms 54
ϕ(x)=p(x)q(x)\phi(x)=\frac{p(x)}{q(x)}
Equation 55
y′′=p(x)y′+q(x)y+r(x),a≤x≤b,y(a)=α, y(b)=βy'' = p(x)y' + q(x)y + r(x), \quad a \leq x \leq b, \quad y(a) = \alpha,\ y(b) = \beta
Linear BVP
Superposition Idea 56
y1′′=p(x)y1′+q(x)y1+r(x)y_1'' = p(x)y_1' + q(x)y_1 + r(x)
Linear BVP
Superposition Idea 57
y2′′=p(x)y2′+q(x)y2y_2'' = p(x)y_2' + q(x)y_2
Linear BVP
Auxiliary IVPs 58
y1(a)=α, y1′(a)=0,y2(a)=0, y2′(a)=1y_1(a) = \alpha,\ y_1'(a) = 0, \qquad y_2(a) = 0,\ y_2'(a) = 1
Linear BVP
Auxiliary IVPs 59
y1′′=p(x)y1′+q(x)y1+r(x),y1(a)=α, y1′(a)=0y2′′=p(x)y2′+q(x)y2,y2(a)=0, y2′(a)=1y_1'' = p(x)y_1' + q(x)y_1 + r(x), \quad y_1(a) = \alpha,\ y_1'(a) = 0 \\[8pt] y_2'' = p(x)y_2' + q(x)y_2, \quad y_2(a) = 0,\ y_2'(a) = 1
Linear BVP
Combining Solutions 60
c=β−y1(b)y2(b),y(x)=y1(x)+β−y1(b)y2(b) y2(x)c = \frac{\beta - y_1(b)}{y_2(b)}, \qquad y(x) = y_1(x) + \frac{\beta - y_1(b)}{y_2(b)}\,y_2(x)
Linear BVP
Mesh Points 61
xi=a+ih,i=0,1,…,N+1x_i = a + ih, \quad i = 0, 1, \ldots, N+1
Linear BVP
Centered Difference Formulas 62
y′′(xi)=y(xi+1)−2y(xi)+y(xi−1)h2+O(h2),y′(xi)=y(xi+1)−y(xi−1)2h+O(h2)y''(x_i) = \frac{y(x_{i+1}) - 2y(x_i) + y(x_{i-1})}{h^2} + O(h^2), \qquad y'(x_i) = \frac{y(x_{i+1}) - y(x_{i-1})}{2h} + O(h^2)
Linear BVP
Linear Difference Equation 63
(1+h2p(xi))wi−1−(2+h2q(xi))wi+(1−h2p(xi))wi+1=h2r(xi)\left(1 + \frac{h}{2}p(x_i)\right)w_{i-1} - \big(2 + h^2 q(x_i)\big)w_i + \left(1 - \frac{h}{2}p(x_i)\right)w_{i+1} = h^2 r(x_i)
Linear BVP
Matrix Form 65
ai=−1−h2p(xi),di=2+h2q(xi),ci=−1+h2p(xi)A=(d1c1a2d2c2⋱⋱⋱aNdN)w=(w1w2⋮wN),b=(−h2r(x1)−a1α−h2r(x2)⋮−h2r(xN)−cNβ)a_i = -1 - \frac{h}{2}p(x_i), \quad d_i = 2 + h^2q(x_i), \quad c_i = -1 + \frac{h}{2}p(x_i) \\[8pt] A = \begin{pmatrix} d_1 & c_1 & & \\ a_2 & d_2 & c_2 & \\ & \ddots & \ddots & \ddots \\ & & a_N & d_N \end{pmatrix} \\[8pt] w = \begin{pmatrix}w_1\\w_2\\\vdots\\w_N\end{pmatrix}, \quad b = \begin{pmatrix}-h^2r(x_1)-a_1\alpha\\-h^2r(x_2)\\\vdots\\-h^2r(x_N)-c_N\beta\end{pmatrix}
Linear BVP
Equation 66
a11x1+a12x2+⋯+a1nxn=b1a21x1+a22x2+⋯+a2nxn=b2⋮am1x1+am2x2+⋯+amnxn=bm\begin{aligned} a_{11}x_1 + a_{12}x_2 + \cdots + a_{1n}x_n &= b_1 \\ a_{21}x_1 + a_{22}x_2 + \cdots + a_{2n}x_n &= b_2 \\ \vdots \\ a_{m1}x_1 + a_{m2}x_2 + \cdots + a_{mn}x_n &= b_m \end{aligned}
Equation 67
A=(a11a12⋯a1na21a22⋯a2n⋮⋮⋱⋮am1am2⋯amn),x⃗=(x1x2⋮xn),b⃗=(b1b2⋮bm)A = \begin{pmatrix} a_{11} & a_{12} & \cdots & a_{1n} \\ a_{21} & a_{22} & \cdots & a_{2n} \\ \vdots & \vdots & \ddots & \vdots \\ a_{m1} & a_{m2} & \cdots & a_{mn} \end{pmatrix}, \quad \vec{x} = \begin{pmatrix} x_1 \\ x_2 \\ \vdots \\ x_n \end{pmatrix}, \quad \vec{b} = \begin{pmatrix} b_1 \\ b_2 \\ \vdots \\ b_m \end{pmatrix}

Symbols

AA
coefficient matrix
[A∣b⃗][A \mid \vec{b}]
augmented matrix
Worked Example 68
(a){x+y=32x−y=0(b){x+y=32x+2y=6(c){x+y=32x+2y=7\text{(a)} \begin{cases} x + y = 3 \\ 2x - y = 0 \end{cases} \qquad \text{(b)} \begin{cases} x + y = 3 \\ 2x + 2y = 6 \end{cases} \qquad \text{(c)} \begin{cases} x + y = 3 \\ 2x + 2y = 7 \end{cases}
Using Gaussian Elimination 70
mji=aji(i)aii(i)m_{ji} = \frac{a_{ji}^{(i)}}{a_{ii}^{(i)}}
Using Gaussian Elimination 71
U=(a11(1)a12(1)⋯a1n(1)0a22(2)⋯a2n(2)⋮⋮⋱⋮0⋯⋯ann(n))  and  L=(10⋯0m211⋯⋮⋮⋮⋱0mn1⋯mn,n−11)U = \begin{pmatrix} a_{11}^{(1)} & a_{12}^{(1)} & \cdots & a_{1n}^{(1)} \\ 0 & a_{22}^{(2)} & \cdots & a_{2n}^{(2)} \\ \vdots & \vdots & \ddots & \vdots \\ 0 & \cdots & \cdots & a_{nn}^{(n)} \end{pmatrix} \; \text{and} \; L = \begin{pmatrix} 1 & 0 & \cdots & 0 \\ m_{21} & 1 & \cdots & \vdots \\ \vdots & \vdots & \ddots & 0 \\ m_{n1} & \cdots & m_{n,n-1} & 1 \end{pmatrix}
Direct Computation Method 72
aij=∑k=1nlikukja_{ij} = \sum_{k=1}^{n} l_{ik} u_{kj}
Direct Computation Method 73
uij=aij−∑k=1i−1likukju_{ij} = a_{ij} - \sum_{k=1}^{i-1} l_{ik} u_{kj}
Direct Computation Method 74
lji=1uii[aji−∑k=1i−1ljkuki]l_{ji} = \frac{1}{u_{ii}} \left[ a_{ji} - \sum_{k=1}^{i-1} l_{jk} u_{ki} \right]
Finding the Inverse 75
Ax⃗j=e⃗j,j=1,…,nA\vec{x}_j = \vec{e}_j, \quad j = 1, \ldots, n
Finding the Determinant 76
det⁡A=det⁡Ldet⁡U\det A = \det L \det U
Finding the Determinant 77
det⁡A=∏i=1nuii\det A = \prod_{i=1}^{n} u_{ii}
Finding the Determinant 78
det⁡A=(−1)s∏i=1nuii\det A = (-1)^{s} \prod_{i=1}^{n} u_{ii}
Example 1 79
A=(21−1−3−12−212)A = \begin{pmatrix} 2 & 1 & -1 \\ -3 & -1 & 2 \\ -2 & 1 & 2 \end{pmatrix}
Example 2 80
b⃗=(101)\vec{b} = \begin{pmatrix} 1 \\ 0 \\ 1 \end{pmatrix}
Example 3 81
A=(121385279)A = \begin{pmatrix} 1 & 2 & 1 \\ 3 & 8 & 5 \\ 2 & 7 & 9 \end{pmatrix}
Example 4 82
A=(21−1−3−12−212)A = \begin{pmatrix} 2 & 1 & -1 \\ -3 & -1 & 2 \\ -2 & 1 & 2 \end{pmatrix}
Equation 83
PA=LUPA = LU

Symbols

LL
unit lower triangular, n×nn \times n with all diagonal entries 11
UU
upper triangular, n×nn \times n
PP
permutation matrix, n×nn \times n
Recursive Leading-Row-Column Algorithm 84
B(k)=(α(k)(r(k))Tc(k)C(k))B^{(k)} = \begin{pmatrix} \alpha^{(k)} & \left(r^{(k)}\right)^T \\ c^{(k)} & C^{(k)} \end{pmatrix}

Symbols

A(k)A^{(k)}
matrix entering recursion depth kk
B(k)B^{(k)}
matrix after the row swap
α(k)\alpha^{(k)}
pivot
(r(k))T\left(r^{(k)}\right)^T
rest of the pivot row
c(k)c^{(k)}
rest of the pivot column
C(k)C^{(k)}
trailing block
ℓ(k)\ell^{(k)}
multiplier vector
S(k)S^{(k)}
row swap at recursion depth kk
Schur Complement 85
ℓ(k)=c(k)α(k),A(k+1)=C(k)−ℓ(k)(r(k))T\ell^{(k)} = \frac{c^{(k)}}{\alpha^{(k)}}, \qquad A^{(k+1)} = C^{(k)} - \ell^{(k)}\left(r^{(k)}\right)^T
Base Case 86
P(k)=L(k)=(1),U(k)=A(k)P^{(k)} = L^{(k)} = (1), \qquad U^{(k)} = A^{(k)}
Recursive Step 87
P(k)=(10⃗T0⃗P(k+1))S(k),L(k)=(10⃗TP(k+1)ℓ(k)L(k+1)),U(k)=(α(k)(r(k))T0⃗U(k+1))P^{(k)} = \begin{pmatrix} 1 & \vec{0}^T \\ \vec{0} & P^{(k+1)} \end{pmatrix}S^{(k)}, \quad L^{(k)} = \begin{pmatrix} 1 & \vec{0}^T \\ P^{(k+1)}\ell^{(k)} & L^{(k+1)} \end{pmatrix}, \quad U^{(k)} = \begin{pmatrix} \alpha^{(k)} & \left(r^{(k)}\right)^T \\ \vec{0} & U^{(k+1)} \end{pmatrix}
Example 1 88
A=(055290688)A = \begin{pmatrix} 0 & 5 & 5 \\ 2 & 9 & 0 \\ 6 & 8 & 8 \end{pmatrix}
Diagonally Dominant Matrix 89
∣aii∣≥∑j=1,j≠in∣aij∣|a_{ii}| \geq \sum_{j=1, j \neq i}^{n} |a_{ij}|
Positive Definite Matrix 90
Ak=[a11⋯a1k⋮⋱⋮ak1⋯akk]A_k = \begin{bmatrix} a_{11} & \cdots & a_{1k} \\ \vdots & \ddots & \vdots \\ a_{k1} & \cdots & a_{kk} \end{bmatrix}
Positive Definite Matrix 91
A=(2−10−12−10−12)A = \begin{pmatrix} 2 & -1 & 0 \\ -1 & 2 & -1 \\ 0 & -1 & 2 \end{pmatrix}
Convergent Matrix 92
lim⁡k→∞Ak=B\lim_{k \to \infty} A^k = B
Worked Example 93
A=(72035−105−6)A = \begin{pmatrix} 7 & 2 & 0 \\ 3 & 5 & -1 \\ 0 & 5 & -6 \end{pmatrix}
Worked Example 94
B=(4−10−14−10−14)B = \begin{pmatrix} 4 & -1 & 0 \\ -1 & 4 & -1 \\ 0 & -1 & 4 \end{pmatrix}
Worked Example 95
C=(0.500.250.5)C = \begin{pmatrix} 0.5 & 0 \\ 0.25 & 0.5 \end{pmatrix}
Equation 96
wi+1=am−1wi+am−2wi−1+⋯+a0wi+1−m+h[bmf(ti+1,wi+1)+bm−1f(ti,wi)+⋯+b0f(ti+1−m,wi+1−m)]\begin{aligned} w_{i+1} = {}& a_{m-1} w_i + a_{m-2} w_{i-1} + \cdots + a_0 w_{i+1-m} \\ & + h\big[ b_m f(t_{i+1}, w_{i+1}) + b_{m-1} f(t_i, w_i) + \cdots + b_0 f(t_{i+1-m}, w_{i+1-m}) \big] \end{aligned}
Adams Methods 97
y(ti+1)=y(ti)+∫titi+1f(t,y(t)) dty(t_{i+1}) = y(t_i) + \int_{t_i}^{t_{i+1}} f(t, y(t))\, \text{d}t
Adams-Bashforth Explicit Methods 98
wi+1=wi+h2[3fi−fi−1]w_{i+1} = w_i + \frac{h}{2}\left[ 3 f_i - f_{i-1} \right]
Adams-Bashforth Explicit Methods 99
wi+1=wi+h12[23fi−16fi−1+5fi−2]w_{i+1} = w_i + \frac{h}{12}\left[ 23 f_i - 16 f_{i-1} + 5 f_{i-2} \right]
Adams-Bashforth Explicit Methods 100
wi+1=wi+h24[55fi−59fi−1+37fi−2−9fi−3]w_{i+1} = w_i + \frac{h}{24}\left[ 55 f_i - 59 f_{i-1} + 37 f_{i-2} - 9 f_{i-3} \right]
Adams-Bashforth Explicit Methods 101
wi+1=wi+h720[1901fi−2774fi−1+2616fi−2−1274fi−3+251fi−4]w_{i+1} = w_i + \frac{h}{720}\left[ 1901 f_i - 2774 f_{i-1} + 2616 f_{i-2} - 1274 f_{i-3} + 251 f_{i-4} \right]
Adams-Moulton Implicit Methods 102
wi+1=wi+h12[5fi+1+8fi−fi−1]w_{i+1} = w_i + \frac{h}{12}\left[ 5 f_{i+1} + 8 f_i - f_{i-1} \right]
Adams-Moulton Implicit Methods 103
wi+1=wi+h24[9fi+1+19fi−5fi−1+fi−2]w_{i+1} = w_i + \frac{h}{24}\left[ 9 f_{i+1} + 19 f_i - 5 f_{i-1} + f_{i-2} \right]
Adams-Moulton Implicit Methods 104
wi+1=wi+h720[251fi+1+646fi−264fi−1+106fi−2−19fi−3]w_{i+1} = w_i + \frac{h}{720}\left[ 251 f_{i+1} + 646 f_i - 264 f_{i-1} + 106 f_{i-2} - 19 f_{i-3} \right]
Iteration 105
G(x⃗)=x⃗−J(x⃗)−1F(x⃗)G(\vec{x}) = \vec{x} - J(\vec{x})^{-1}F(\vec{x})
Iteration 106
J(x⃗(k−1))y⃗(k−1)=−F(x⃗(k−1))J(\vec{x}^{(k-1)})\vec{y}^{(k-1)} = -F(\vec{x}^{(k-1)})
Example 2 107
x12+x22−4=0x1x2−1=0\begin{aligned} x_1^2 + x_2^2 - 4 &= 0 \\ x_1 x_2 - 1 &= 0 \end{aligned}
Equation 108
y′′=p(x)y′+q(x)y+r(x)y'' = p(x)y' + q(x)y + r(x)
Idea 109
y′′=f(x,y,y′),y(a)=α, y′(a)=ty'' = f(x,y,y'), \quad y(a) = \alpha,\ y'(a) = t
Newton Iteration 110
z′′=fy(x,y,y′)z+fy′(x,y,y′)z′,z(a)=0, z′(a)=1z'' = f_y(x,y,y')z + f_{y'}(x,y,y')z', \quad z(a) = 0,\ z'(a) = 1
Newton Iteration 111
tk=tk−1−y(b,tk−1)−βz(b,tk−1)t_k = t_{k-1} - \frac{y(b,t_{k-1}) - \beta}{z(b,t_{k-1})}
Nonlinear Difference Equation 112
w0=α,wN+1=β,−wi+1−2wi+wi−1h2+f(xi,wi,wi+1−wi−12h)=0w_0 = \alpha, \quad w_{N+1} = \beta, \quad -\frac{w_{i+1}-2w_i+w_{i-1}}{h^2} + f\left(x_i,w_i,\frac{w_{i+1}-w_{i-1}}{2h}\right) = 0
Nonlinear System 113
2w1−w2+h2f(x1,w1,w2−α2h)−α=0−wi−1+2wi−wi+1+h2f(xi,wi,wi+1−wi−12h)=0,i=2,…,N−1−wN−1+2wN+h2f(xN,wN,β−wN−12h)−β=02w_1-w_2+h^2f\left(x_1,w_1,\frac{w_2-\alpha}{2h}\right)-\alpha=0 \\[8pt] -w_{i-1}+2w_i-w_{i+1}+h^2f\left(x_i,w_i,\frac{w_{i+1}-w_{i-1}}{2h}\right)=0, \quad i=2,\ldots,N-1 \\[8pt] -w_{N-1}+2w_N+h^2f\left(x_N,w_N,\frac{\beta-w_{N-1}}{2h}\right)-\beta=0
Equation 114
f1(x1,x2,…,xn)=0f2(x1,x2,…,xn)=0  ⋮fn(x1,x2,…,xn)=0\begin{aligned} f_1(x_1, x_2, \ldots, x_n) &= 0 \\ f_2(x_1, x_2, \ldots, x_n) &= 0 \\ &\;\vdots \\ f_n(x_1, x_2, \ldots, x_n) &= 0 \end{aligned}
Equation 115
F(x⃗)=(f1(x⃗),f2(x⃗),…,fn(x⃗))TF(\vec{x}) = \big(f_1(\vec{x}), f_2(\vec{x}), \ldots, f_n(\vec{x})\big)^T

Symbols

FF
maps Rn\mathbb{R}^n into Rn\mathbb{R}^n
f1,f2,…,fnf_1, f_2, \ldots, f_n
coordinate functions of FF
Continuity from Bounded Partials 116
∣∂f(x⃗)∂xj∣≤K,j=1,2,…,n\left| \frac{\partial f(\vec{x})}{\partial x_j} \right| \le K, \quad j = 1, 2, \ldots, n
Example 1 117
3x1−cos⁡(x2x3)−12=0x12−81(x2+0.1)2+sin⁡x3+1.06=0e−x1x2+20x3+10π−33=0\begin{aligned} 3x_1 - \cos(x_2 x_3) - \tfrac{1}{2} &= 0 \\ x_1^2 - 81(x_2 + 0.1)^2 + \sin x_3 + 1.06 &= 0 \\ e^{-x_1 x_2} + 20x_3 + \tfrac{10\pi - 3}{3} &= 0 \end{aligned}
Example 2 118
x12+x22=4x12−x2=2\begin{aligned} x_1^2 + x_2^2 &= 4 \\ x_1^2 - x_2 &= 2 \end{aligned}
1-Norm 119
∥x⃗∥1=∑i=1n∣xi∣\lVert \vec{x} \rVert_1 = \sum_{i=1}^{n} |x_i|
Norms
2-Norm 120
∥x⃗∥2=(∑i=1nxi2)1/2\lVert \vec{x} \rVert_2 = \left( \sum_{i=1}^{n} x_i^2 \right)^{1/2}
Norms
Infinity Norm 121
∥x⃗∥∞=max⁡1≤i≤n∣xi∣\lVert \vec{x} \rVert_\infty = \max_{1 \leq i \leq n} |x_i|
Norms
Equivalent Norms 122
c1∥x⃗∥v1≤∥x⃗∥v2≤c2∥x⃗∥v1c_1 \lVert \vec{x} \rVert_{v1} \leq \lVert \vec{x} \rVert_{v2} \leq c_2 \lVert \vec{x} \rVert_{v1}
Norms
Equivalent Norms 123
∥x⃗∥∞≤∥x⃗∥2≤n ∥x⃗∥∞\lVert \vec{x} \rVert_\infty \leq \lVert \vec{x} \rVert_2 \leq \sqrt{n} \, \lVert \vec{x} \rVert_\infty
Norms
Equivalent Norms 124
∥x⃗∥∞≤∥x⃗∥1≤n ∥x⃗∥∞\lVert \vec{x} \rVert_\infty \leq \lVert \vec{x} \rVert_1 \leq n \, \lVert \vec{x} \rVert_\infty
Norms
Induced Matrix Norm 125
∥A∥M=sup⁡x⃗≠0⃗∥Ax⃗∥v∥x⃗∥v=sup⁡∥x⃗∥v=1∥Ax⃗∥v\lVert A \rVert_M = \sup_{\vec{x} \neq \vec{0}} \frac{\lVert A\vec{x} \rVert_v}{\lVert \vec{x} \rVert_v} = \sup_{\lVert \vec{x} \rVert_v = 1} \lVert A\vec{x} \rVert_v
Norms
Induced Matrix Norm 126
∥A∥∞=max⁡1≤i≤n∑j=1n∣aij∣(maximum row sum)\lVert A \rVert_\infty = \max_{1 \leq i \leq n} \sum_{j=1}^{n} |a_{ij}| \quad \text{(maximum row sum)}
Norms
Spectrum 127
σ(A)={λ  ;  Av⃗=λv⃗,v⃗∈Rn,λ is an eigenvalue of A}\sigma(A) = \{ \lambda \; ; \; A\vec{v} = \lambda \vec{v}, \vec{v} \in \mathbb{R}^n, \lambda \text{ is an eigenvalue of } A \}
Norms
Spectral Radius 128
ρ(A)=max⁡{∣λ∣  ;  λ∈σ(A)}\rho(A) = \max \{ |\lambda| \; ; \; \lambda \in \sigma(A) \}
Norms
Spectral Radius 129
∥A∥2=ρ(ATA)\lVert A \rVert_2 = \sqrt{\rho(A^TA)}
Norms
Worked Example 130
A=(1−234)A = \begin{pmatrix} 1 & -2 \\ 3 & 4 \end{pmatrix}
Norms
Midpoint Method 131
f(t,y)+h2f′(t,y)=f(t+h2, y+h2f(t,y))+O(h2)f(t, y) + \frac{h}{2} f'(t, y) = f\left( t + \frac{h}{2},\ y + \frac{h}{2} f(t, y) \right) + O(h^2)
Modified Euler Method 132
w0=αwi+1=wi+h2[f(ti,wi)+f(ti+1, wi+hf(ti,wi))]\begin{aligned} w_0 &= \alpha \\ w_{i+1} &= w_i + \frac{h}{2}\Big[ f(t_i, w_i) + f\big(t_{i+1},\ w_i + h f(t_i, w_i)\big) \Big] \end{aligned}
Order 4 Runge-Kutta 133
w0=αk1=h f(ti,wi)k2=h f(ti+h2, wi+12k1)k3=h f(ti+h2, wi+12k2)k4=h f(ti+1, wi+k3)wi+1=wi+16(k1+2k2+2k3+k4)\begin{aligned} w_0 &= \alpha \\ k_1 &= h\, f(t_i, w_i) \\ k_2 &= h\, f\left( t_i + \tfrac{h}{2},\ w_i + \tfrac{1}{2} k_1 \right) \\ k_3 &= h\, f\left( t_i + \tfrac{h}{2},\ w_i + \tfrac{1}{2} k_2 \right) \\ k_4 &= h\, f\left( t_{i+1},\ w_i + k_3 \right) \\ w_{i+1} &= w_i + \tfrac{1}{6}\left( k_1 + 2k_2 + 2k_3 + k_4 \right) \end{aligned}
Gradient 134
∇f(x,y)=∂f∂xi⃗+∂f∂yj⃗\nabla f(x, y) = \frac{\partial f}{\partial x}\vec{i} + \frac{\partial f}{\partial y}\vec{j}
Procedure 135
g(hi)=f(xi+hi∂f∂x(xi,yi), yi+hi∂f∂y(xi,yi))g(h_i) = f\left(x_i + h_i \frac{\partial f}{\partial x}(x_i,y_i),\ y_i + h_i \frac{\partial f}{\partial y}(x_i,y_i)\right)
Procedure 136
xi+1=xi+hi∂f∂x(xi,yi),yi+1=yi+hi∂f∂y(xi,yi)x_{i+1} = x_i + h_i \frac{\partial f}{\partial x}(x_i,y_i), \quad y_{i+1} = y_i + h_i \frac{\partial f}{\partial y}(x_i,y_i)
Worked Example 137
f(x,y)=2xy+2x−x2−2y2f(x,y) = 2xy + 2x - x^2 - 2y^2
Equation 138
u′(t)=f(t,u(t)),u(a)=α\mathbf{u}'(t) = \mathbf{f}(t, \mathbf{u}(t)), \qquad \mathbf{u}(a) = \boldsymbol{\alpha}
Equation 139
∥f(t,u)−f(t,z)∥≤L∥u−z∥\lVert \mathbf{f}(t, \mathbf{u}) - \mathbf{f}(t, \mathbf{z}) \rVert \leq L \lVert \mathbf{u} - \mathbf{z} \rVert
Reducing a Higher-Order IVP 140
y(m)(t)=f(t,y,y′,…,y(m−1)),a≤t≤by^{(m)}(t) = f\big(t, y, y', \ldots, y^{(m-1)}\big), \quad a \leq t \leq b
Reducing a Higher-Order IVP 141
u′=(u2u3⋮umf(t,u1,…,um)),u(a)=(α1α2⋮αm)\mathbf{u}' = \begin{pmatrix} u_2 \\ u_3 \\ \vdots \\ u_m \\ f(t, u_1, \ldots, u_m) \end{pmatrix}, \qquad \mathbf{u}(a) = \begin{pmatrix} \alpha_1 \\ \alpha_2 \\ \vdots \\ \alpha_m \end{pmatrix}
Example 1 142
y′′−2y′+2y=e2tsin⁡t,0≤t≤1,y(0)=−0.4, y′(0)=−0.6y'' - 2y' + 2y = e^{2t}\sin t, \quad 0 \leq t \leq 1, \quad y(0) = -0.4,\ y'(0) = -0.6
Equation 143
b1x1+c1x2=d1a2x1+b2x2+c2x3=d2 ⋮an−1xn−2+bn−1xn−1+cn−1xn=dn−1anxn−1+bnxn=dn\begin{aligned} b_1x_1 + c_1x_2 &= d_1 \\ a_2x_1 + b_2x_2 + c_2x_3 &= d_2 \\ &\ \vdots \\ a_{n-1}x_{n-2} + b_{n-1}x_{n-1} + c_{n-1}x_n &= d_{n-1} \\ a_nx_{n-1} + b_nx_n &= d_n \end{aligned}

Symbols

aia_i
subdiagonal entries
bib_i
main diagonal entries
cic_i
superdiagonal entries
did_i
right-hand-side entries
Equation 144
(b1c10⋯0a2b2c2⋱⋮0a3b3⋱0⋮⋱⋱⋱cn−10⋯0anbn)(x1x2⋮xn)=(d1d2⋮dn)\begin{pmatrix} b_1 & c_1 & 0 & \cdots & 0 \\ a_2 & b_2 & c_2 & \ddots & \vdots \\ 0 & a_3 & b_3 & \ddots & 0 \\ \vdots & \ddots & \ddots & \ddots & c_{n-1} \\ 0 & \cdots & 0 & a_n & b_n \end{pmatrix} \begin{pmatrix}x_1\\x_2\\\vdots\\x_n\end{pmatrix} = \begin{pmatrix}d_1\\d_2\\\vdots\\d_n\end{pmatrix}
Crout Factorization 146
L=(ℓ10⋯0a2ℓ2⋱⋮0a3⋱0⋮⋱⋱ℓn),U=(1u10⋯001u2⋱⋮⋮⋱⋱⋱00⋯01un−10⋯⋯01)L= \begin{pmatrix} \ell_1 & 0 & \cdots & 0 \\ a_2 & \ell_2 & \ddots & \vdots \\ 0 & a_3 & \ddots & 0 \\ \vdots & \ddots & \ddots & \ell_n \end{pmatrix}, \qquad U= \begin{pmatrix} 1 & u_1 & 0 & \cdots & 0 \\ 0 & 1 & u_2 & \ddots & \vdots \\ \vdots & \ddots & \ddots & \ddots & 0 \\ 0 & \cdots & 0 & 1 & u_{n-1} \\ 0 & \cdots & \cdots & 0 & 1 \end{pmatrix}
Crout Factorization 147
ℓ1=b1,u1=c1ℓ1,ℓi=bi−aiui−1,ui=ciℓi,i=2,…,n−1,ℓn=bn−anun−1.\begin{aligned} \ell_1 &= b_1, & u_1 &= \frac{c_1}{\ell_1}, \\ \ell_i &= b_i-a_i u_{i-1}, & u_i &= \frac{c_i}{\ell_i}, \quad i=2,\ldots,n-1, \\ \ell_n &= b_n-a_n u_{n-1}. \end{aligned}
Triangular Solves 148
y1=d1ℓ1,yi=di−aiyi−1ℓi,i=2,…,n.\begin{aligned} y_1 &= \frac{d_1}{\ell_1}, \\ y_i &= \frac{d_i-a_i y_{i-1}}{\ell_i}, \quad i=2,\ldots,n. \end{aligned}
Triangular Solves 149
xn=yn,xi=yi−uixi+1,i=n−1,n−2,…,1.\begin{aligned} x_n &= y_n, \\ x_i &= y_i-u_i x_{i+1}, \quad i=n-1,n-2,\ldots,1. \end{aligned}
Worked Example 150
2x1−x2=1−x1+2x2−x3=0−x2+2x3−x4=0−x3+2x4=1.\begin{aligned} 2x_1-x_2 &= 1 \\ -x_1+2x_2-x_3 &= 0 \\ -x_2+2x_3-x_4 &= 0 \\ -x_3+2x_4 &= 1. \end{aligned}

S5 / Operational Research

Payoff Matrix 01
A=(a11⋯a1n⋮⋱⋮am1⋯amn)A = \begin{pmatrix} a_{11} & \cdots & a_{1n} \\ \vdots & \ddots & \vdots \\ a_{m1} & \cdots & a_{mn} \end{pmatrix}

Symbols

mm
number of row player strategies
nn
number of column player strategies
aija_{ij}
row player's payoff when the row player picks ii and the column player picks jj
Linear Program 02
max⁡x∈Rnz=cTxs.t.Ax≤bx≥0\begin{aligned} \max_{x \in \mathbb{R}^n} \quad & z = c^T x \\ \text{s.t.} \quad & Ax \le b \\ & x \ge 0 \end{aligned}

Symbols

xx
decision variables
cc
objective coefficients
A=[aij]A = [a_{ij}]
constraint coefficients
bb
constraint bounds
Activity Limits 03
0≤xi≤tiN−tiC0 \leq x_i \leq t_i^{N} - t_i^{C}

Symbols

xix_i
time reduction for activity ii
tiNt_i^{N}
normal time for activity ii
tiCt_i^{C}
crash time for activity ii
Crashable Activity 04
ti>tiCt_i > t_i^{C}

Symbols

tit_i
activity's current duration
tiCt_i^{C}
activity's crash time
Cost Slope 05
Cost slopei=−CiN−CiCtiN−tiC\text{Cost slope}_i = -\frac{C_i^{N} - C_i^{C}} {t_i^{N} - t_i^{C}}

Symbols

CiNC_i^{N}
direct cost of activity iiat normal time
CiCC_i^{C}
direct cost of activity iiat crash time
Project Cost 06
CD=∑iCiN+∑ixisiC_D = \sum_i C_i^{N} + \sum_i x_i s_i

Symbols

CDC_D
total direct cost
xix_i
time reduction for activity ii
sis_i
cost slope of activity ii
Project Cost 07
CT=CD+rTC_T = C_D + rT

Symbols

CTC_T
total project cost
rr
indirect cost rate per time unit
TT
project duration
Crashing Offer 08
Cnet=ΔCD−rΔTC_{\text{net}} = \Delta C_D - r\Delta T

Symbols

CnetC_{\text{net}}
net additional cost
ΔCD\Delta C_D
added direct cost
ΔT\Delta T
reduction in project duration
Resource Profile 09
Rr,t=∑i∈AtriR_{r,t} = \sum_{i \in A_t} r_i

Symbols

AtA_t
activities running during time period tt
rir_i
amount of resource rr required by activity ii
Rr,tR_{r,t}
total demand for resource rr during time period tt

S5 / Secure App Dev

PCR Extend 01
PCRnew=H(PCRold∥m)PCR_{\text{new}} = H(PCR_{\text{old}} \parallel m)

Symbols

HH
hash function
mm
hash of the component being measured
∥\parallel
concatenation