Reference and Selective Reading

You do not need to read the source books front-to-back for this module. Use the concept pages and practice pages first. Open these local chunks only when you need alternate exposition, more worked examples, or a deeper exercise lane.

Source Roles

Source	Role	Why it is here
The Algorithm Design Manual (Skiena)	Primary teaching source	Best operational explanations of paradigm selection, war-story debugging narratives, and practical algorithm intuition
Introduction to Algorithms (CLRS)	Formal support	Strongest formal definitions of asymptotic notation, rigorous loop-invariant proofs, Master Theorem cases, greedy and DP frameworks, and amortized analysis
Algorithms (Sedgewick)	Implementation support	Cleaner implementation perspectives; rarely needed in Module 1 but valuable for Module 2
Grokking Algorithms (Bhargava)	Accessible alternate	Good for a very low-friction first pass on recursion, D&C, and DP
Competitive Programming (Halim)	Exercise and triage support	Compact paradigm guides and exercise volume

Read Only If Stuck

Cluster 1: Asymptotic Analysis and Mathematical Foundations

Cluster 2: Algorithm Correctness and Termination

Cluster 3: Design Paradigms (Search, D&C, Greedy, Backtracking)

Cluster 4: Dynamic Programming

Cluster 5: Analysis in Practice

Optional Deep Dive

CLRS: Akra-Bazzi recurrences - generalization of the Master Theorem for uneven splits.
CLRS: Proof of the continuous master theorem - a full proof, not operationally required but useful for deeper understanding.
Skiena: Strassen matrix multiplication (recurrence analysis) - faster-than-cubic recurrence with practical trade-offs.
Skiena: Edit distance varieties - DP variants built off the same recurrence.
Skiena: Longest common subsequence context - where LCS-style DPs appear in algorithm design catalogs.
CLRS: Randomized algorithms (Chapter 5 material) - optional, for when you want a bridge to later probabilistic analysis.

Concept-to-Source Map

Primary concept	Best source if stuck	Why this source
Big-O, Omega, and Theta bound growth	CLRS: Asymptotic notation formal definitions	Cleanest formal treatment with worked bound proofs
Loops and summations	Skiena: Logarithms and summations	Summations applied directly to algorithm analysis
Recurrences and Master Theorem	CLRS: Master method	Canonical reference with worked cases
Loop invariants	CLRS: Insertion sort	The textbook loop-invariant proof
Recursive correctness	Skiena: Induction and recursion	Strongest bridge from induction to recursion
Divide-and-conquer	Skiena: Divide and conquer	Operational template with several examples
Greedy with exchange	CLRS: Activity selection	Model exchange-argument proof
Optimal substructure + overlap	CLRS: Elements of dynamic programming	Formal treatment of the two properties
Memoization vs tabulation	CLRS: Rod cutting	Both implementations side-by-side
Debugging complex algorithms	Skiena: War story - And then I failed	Realistic failure narrative with the repair
Choosing a paradigm	Skiena: Modeling the problem	How to reshape a messy problem into paradigm-ready form

Source Roles​

Read Only If Stuck​

Cluster 1: Asymptotic Analysis and Mathematical Foundations​

Cluster 2: Algorithm Correctness and Termination​

Cluster 3: Design Paradigms (Search, D&C, Greedy, Backtracking)​

Cluster 4: Dynamic Programming​

Cluster 5: Analysis in Practice​

Optional Deep Dive​

Concept-to-Source Map​