Problem-solving strategies for source coding in information theory
Use this skill when working on source-coding problems in information theory.
Source Coding Theorem
z3_solve.py prove "shannon_bound"Huffman Coding
sympy_compute.py simplify "expected_code_length"Kraft Inequality
z3_solve.py prove "kraft_inequality"Arithmetic Coding
Rate-Distortion Theory
sympy_compute.py minimize "I(X;X_hat)" --constraint "E[d] <= D"uv run python -c "print('Huffman codes for a=0.5, b=0.25, c=0.125, d=0.125: a=0, b=10, c=110, d=111')"
uv run python -m runtime.harness scripts/sympy_compute.py simplify "2**(-l1) + 2**(-l2) + 2**(-l3) + 2**(-l4)"
uv run python -m runtime.harness scripts/z3_solve.py prove "expected_length >= entropy"
From indexed textbooks:
See .claude/skills/math-mode/SKILL.md for full tool documentation.