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Notation

This book uses consistent notation throughout. Below is a reference guide for the symbols and conventions used.

Sets and Number Systems

ℝ Real numbers
ℝn n-dimensional real vectors
ℝ+, ℝ+n Non-negative reals / non-negative real vectors
ℤ Integers
ℤ+, ℤ+n Non-negative integers / non-negative integer vectors
ℚ Rational numbers
{0,1}n Binary vectors of dimension n

Vectors and Matrices

x,y,z Decision variable vectors (bold lowercase)
a,b,c Parameter vectors (bold lowercase)
0 Zero vector
A,B Matrices (capital italic)
xi i-th component of vector x
aij Element in row i, column j of matrix A
x⊤ ⁡ Transpose of vector x
A⊤ ⁡ Transpose of matrix A
c⊤ ⁡x Dot product (inner product) of c and x
∥x∥ Norm of vector x

Optimization Problems

A standard linear program is written as:

max ⁡ c⊤ ⁡x  s.t. Ax ≤b x ≥0
max ⁡ , min ⁡ Maximize, minimize
s.t. Subject to (constraints follow)
≤, ≥, = Constraint types
x∗ Optimal solution
z∗ Optimal objective value

Summation and Indexing

∑ ⁡ i=1nxi Sum of x1 + x2 + ⋯ + xn
∑ ⁡ i∈Ixi Sum over all indices in set I
∏ ⁡ i=1nxi Product x1 ⋅ x2⋯xn
∀ ⁡i ∈ I For all i in set I
∃ ⁡ There exists

Functions and Operators

f(x) Function of x
∇ ⁡f Gradient of f
∇ ⁡2f Hessian matrix of f
argmin ⁡ xf(x) Value of x that minimizes f
conv ⁡ (S) Convex hull of set S
dom ⁡ (f) Domain of function f

Complexity Classes

Polynomial time (P) Polynomial-time solvable
NP-complete NP-Complete problem
NP-hard NP-Hard problem

Common Abbreviations

LP Linear Programming
IP Integer Programming
ILP Integer Linear Programming
BIP Binary Integer Programming
MILP Mixed-Integer Linear Programming
NLP Nonlinear Programming
MINLP Mixed-Integer Nonlinear Programming

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