Formula Cheat Sheet
36 formulas · last-minute reference for Module 2
Linear Algebra
4 formulasDiagonalization
A` diagonalizable ⇔ sum of geometric multiplicities = `n
A` symmetric (`Aᵀ = A`) ⇒ orthogonally diagonalizable: `B…
Aᵏ = PDᵏP⁻¹`; `Dᵏ = diag(λᵢᵏ)
2×2 Shortcut
det(A) = ac − b²`, `tr(A) = a + c
Differential Calculus
5 formulasTaylor Expansion (2nd order)
f(x) = f(x₀) + ∇f(x₀)ᵀ(x − x₀) + ½(x − x₀)ᵀH_f(x₀)(x − x₀) +
Convexity / Concavity (on convex domain C)
f` convex ⇔ `H_f(x)` PSD for all `x ∈ C
f` concave ⇔ `H_f(x)` NSD for all `x ∈ C
Chain Rule
z = f(x(t), y(t))` ⇒ `dz/dt = f_x·x' + f_y·y'
Dini's Implicit Function Theorem
φ'(x₀) = − (∂g/∂x)(x₀, y₀) / (∂g/∂y)(x₀, y₀)
Integral Calculus
3 formulasImproper Integrals
∫₁^∞ 1/xᵖ dx` converges iff `p > 1
∫₀¹ 1/xᵖ dx` converges iff `p < 1
∫₁^∞ 1/(x(ln x)^p) dx` converges iff `p > 1
Probability
12 formulasBasic Properties
P(∅) = 0`; `P(Eᶜ) = 1 − P(E)
Conditional Probability
P(A|B) = P(A ∩ B)/P(B)`, `P(B) > 0
Independence
A ⊥ B ⇔ P(A ∩ B) = P(A)P(B)
Measures / Set Function Hierarchy
M(A ∪ B) + M(A ∩ B) = M(A) + M(B)
Linearity / Variance / Covariance
V(X) = E[X²] − (E[X])²
V(αX + β) = α²V(X)
V(X + Y) = V(X) + V(Y) + 2Cov(X, Y)
Cov(X, Y) = E[XY] − E[X]E[Y]
Cov(αX + β, γY + δ) = αγ·Cov(X, Y)
X ⊥ Y ⇒ E[XY] = E[X]E[Y]`, `Cov = 0`, `V(X+Y) = V(X) + V(Y)
|ρ| = 1` ⇔ linear relation `Y = aX + b
Standard Normal / Tips
Z = (X − μ)/σ ~ N(0,1)` when `X ~ N(μ, σ²)
Mathematical Finance
12 formulasNPV / IRR
NPV(r) = −C₀ + Σ_{k=1}^n CFₖ/(1+r)ᵏ
Classify stationary point `x₀` (after `∇f(x₀) = 0`)
Compute Hessian H = ∇²f(x₀)
Classify quadratic form `q(x) = xᵀAx`
Compute eigenvalues of A (or principal minors)
2×2 fast path (`A = [[a,b],[b,c]]`, `Δ = ac − b²`, `T = a + c`)
Δ > 0, T > 0 → PD
Improper integral convergence (positive `f`)
Find asymptotic equivalent f(x) ~ C/xᵖ (or C/(x−a)ᵖ)
IRR localization (from sample `G(i)` values, investment)
G is strictly decreasing.
Convex vs concave optimization
f on convex domain C
Bond price under yield shift
Know D(y), P(y), proposed Δy:
Annuity formula selection
Payment timing?
Bayes workflow
Given causes {Eᵢ} partitioning Ω, event A observed.
Expected value / variance shortcuts
Sum of independent Xᵢ:
§8 Numerical Benchmarks
Φ(1) ≈ 0.841`, `Φ(1.96) ≈ 0.975`, `Φ(2) ≈ 0.977