Checklist

Algorithms study checklist

An algorithms checklist for proving correctness, reasoning about time-space tradeoffs, and framing unfamiliar problems.

Built for Algorithms · CS students practicing proofs and optimization strategies.

Progress

0 of 13 tasks complete

Lock the analysis tools

Reason about cost rigorously.

Master asymptotic notationUse Big-O, Omega, and Theta precisely, not interchangeably.~45 minSolve recurrencesApply the recursion tree and master theorem to divide-and-conquer costs.~55 minReason about time-space tradeoffsCompare approaches that trade memory for speed and justify the choice.~40 min

Master core paradigms

The strategies most problems reduce to.

Practice divide and conquerImplement and analyze merge sort and binary search variants.~55 minBuild greedy intuitionIdentify when a greedy choice is provably optimal versus when it fails.~50 minMaster dynamic programmingDefine the state, recurrence, and base case before coding.~60 minStudy graph algorithmsShortest paths and minimum spanning trees with their invariants.~55 min

Develop correctness proofs

Justify why an algorithm works.

Write loop invariantsState the invariant and prove initialization, maintenance, and termination.~50 minProve greedy optimalityUse an exchange argument to show the greedy choice is safe.~50 minProve DP recurrences correctShow the recurrence captures all cases and has no overlap errors.~45 min

Frame and practice

Translate problems and drill.

Practice problem framingRestate each problem, identify the paradigm, and sketch before coding.~50 minDo timed mixed problem setsRotate paradigms so you recognize patterns under pressure.~55 minKeep an approach logRecord the cue that signaled each paradigm for faster future recognition.~30 min

Common mistakes

Using Big-O and Theta interchangeably and stating loose bounds as tight.
Coding a dynamic program before defining the state and recurrence.
Assuming a greedy approach is optimal without an exchange argument.
Skipping loop invariants and being unable to argue correctness.
Jumping to code before framing the problem and identifying the paradigm.

Pro tips

Define the DP state, recurrence, and base case in words before writing any code.
Prove greedy choices with an exchange argument rather than trusting intuition.
State loop invariants explicitly and check initialization, maintenance, and termination.
Keep an approach log noting which cue signaled each paradigm.
Always frame and sketch a problem before coding to avoid wrong-paradigm rewrites.

FAQ

How should I start the Algorithms study checklist?

Start with the first phase, then run one timed Study Spaces sprint before adding more tasks. The goal is execution, not a perfect plan.

What should I do if I fall behind?

Copy the remaining tasks, pick the highest-score or highest-deadline item, and restart with one focused block.

How often should I review progress?

Review after each sprint and once at the end of the week so the next session starts with a clear first task.

Browse resources

Use it now

Turn this page into a live sprint

Start the matching room for Algorithms, then use the sprint plan as the first task and recap script.

Algorithms study checklist
Focus target: Algorithms
Block 1 (25 min): closed-book recall or one timed practice set.
Break (5 min): mark confusing items without opening a new task.
Block 2 (25 min): correct misses and write the next first step.
Done: one score/error note plus one queued task for tomorrow.

Start Algorithms roomResource list Compare methods