Expanding Logarithms Calculator

Expand logarithmic expressions using product, quotient, and power properties

Expand Logarithmic Expressions

Product Property: log₍ₙ₎(a × b) = log₍ₙ₎(a) + log₍ₙ₎(b)
Current expression: 4 × 125 = 500

Expansion Result

Original Expression
log₍4₎(4 × 125)
Expanded Form
log₍4₎(4) + log₍4₎(125)
Original Value
4.482892
Expanded Calculation
4.482892

Step-by-Step Expansion

1.Original: log₍4₎(500)
2.Factor: 500 = 4 × 125
3.Apply product rule: log₍4₎(a × b) = log₍4₎(a) + log₍4₎(b)
4.Result: log₍4₎(4) + log₍4₎(125)

Logarithm Properties

Product Rule

log(a × b) = log(a) + log(b)

Quotient Rule

log(a ÷ b) = log(a) - log(b)

Power Rule

log(a^k) = k × log(a)

Key Concepts

Definition

log_n(x) = y means n^y = x

Special Cases

log(1) = 0, log(base) = 1

Common Bases

10 (common), e (natural), 2 (binary)

Domain

Logarithms are defined only for positive numbers

Understanding Logarithm Expansion

What is Logarithm Expansion?

Logarithm expansion is the process of breaking down complex logarithmic expressions into simpler components using the fundamental properties of logarithms. This technique is invaluable for solving logarithmic equations and simplifying calculations.

Why Expand Logarithms?

  • Simplify complex logarithmic expressions
  • Solve logarithmic equations more easily
  • Calculate approximate values manually
  • Understand the structure of logarithmic functions

The Three Key Properties

1. Product Property

log₍ₙ₎(a × b) = log₍ₙ₎(a) + log₍ₙ₎(b)

Multiplication inside log becomes addition of logs

2. Quotient Property

log₍ₙ₎(a ÷ b) = log₍ₙ₎(a) - log₍ₙ₎(b)

Division inside log becomes subtraction of logs

3. Power Property

log₍ₙ₎(a^k) = k × log₍ₙ₎(a)

Exponent inside log becomes coefficient outside

Real-World Applications

Science & Engineering

pH calculations, Richter scale, signal processing, and exponential growth models

Finance & Economics

Compound interest calculations, logarithmic utility functions, and risk assessment

Computer Science

Algorithm complexity analysis, information theory, and data compression