FAQ
Data Structures help, frequently asked
Can you help with linked list assignments?
Yes. Singly, doubly, and circular linked lists in Java, Python, C++, and C with full implementations, ASCII diagrams of pointer rewiring during insert and delete, and JUnit or pytest tests covering 8 boundary cases: empty list, single element, head deletion, tail deletion, middle deletion, duplicate values, traversal during mutation, and stress tests at 100,000 elements. The C and C++ implementations come with Valgrind reports proving zero memory leaks and no use-after-free errors.
How do you implement AVL or red-black trees?
Both with all rotation cases handled explicitly. AVL: 4 cases on insert (LL, LR, RL, RR), 6 on delete (the 4 insert cases plus left-heavy delete and right-heavy delete). Red-black: 5 insert cases and 6 delete cases following the CLRS textbook treatment. Every implementation has post-operation invariant checks that print which property was violated when a unit test fails. Visualization is included as a printable in-order traversal with depth annotations so students can verify balance at every step.
Which hash collision strategy do you use?
Picked per assignment based on load factor and key distribution. Chaining works for load above 0.7 and adversarial key sets. Linear probing wins on cache-friendly access patterns with load below 0.5. Quadratic probing avoids primary clustering but requires a prime-sized table. Double hashing is the textbook choice for collision-heavy projects. Robin Hood hashing comes up in advanced courses. We benchmark insert and lookup latency for each strategy on the specific workload before committing.
Can you analyze the Big-O of my data structure?
Every method gets a 3-line annotation: best case, average case, worst case for both time and space. Where amortized analysis applies (dynamic arrays, splay trees, Fibonacci heaps), we provide both the worst-case-per-operation bound and the amortized bound with a derivation using the accounting or potential method. The analysis lives in JavaDoc or docstring comments so it stays attached to the code under autograder review.
Do you help with graph algorithms?
Yes. BFS, DFS, Dijkstra (with binary heap or Fibonacci heap), Bellman-Ford, Floyd-Warshall, Prim, Kruskal with Union-Find, topological sort, and Tarjan strongly connected components. Implementations come in both adjacency-list and adjacency-matrix forms with the right complexity for each representation. Version-control clone projects, shortest-path problem sets, and advanced algorithms assignments are all routine work. We pick the representation based on density: adjacency list for sparse graphs where E is much less than V squared (most real graphs), adjacency matrix for dense graphs (complete graphs, matrix-based shortest path algorithms). Edge weights stored as int, double, or a custom struct depending on whether the assignment requires fractional weights, negative weights (Bellman-Ford), or vector weights (multi-criteria shortest path).
What about heaps and priority queues?
Binary heap, d-ary heap, Fibonacci heap, leftist heap, and binomial heap covered. Standard operations: insert, delete-min or delete-max, decrease-key, merge. The sift-down off-by-one bug (last-leaf index miscalculation) is the most common silent failure in textbook implementations. Our code uses explicit parent and child index helpers with unit tests verifying the heap invariant after every operation.
How fast is data structures homework delivered?
12-hour average turnaround with Big-O annotations, JUnit or pytest tests, and a 1-page design document for any structure exceeding 200 lines. Rush delivery 4 to 6 hours for an additional fee. Pricing: $20 Debug and Explain per task, $30 Full Solution per task, $40 per hour Live Tutoring. All implementations pass the course autograder format and MOSS-safe stylistic variation.
Do you help with tries and suffix arrays?
Yes. Tries appear in spell-check assignments and autocomplete projects. Suffix arrays come up in advanced data structures and bioinformatics electives. We implement both with the right tradeoff explanation: tries excel at prefix lookups and dynamic insertion, suffix arrays excel at static text indexing with O(n log n) construction via DC3 or SA-IS algorithms. Memory layouts and complexity are annotated for both.
Can you debug a segfault in my C linked list?
GDB plus Valgrind to trace the dangling pointer, double free, or use-after-free. Common patterns: free(curr) before updating prev.next, missing NULL check on the head pointer in an empty-list deletion, freeing the same node twice during a doubly linked list removal that updates both prev and next pointers. We provide the GDB stack trace, the Valgrind error report, and the 3-line fix with a before-and-after pointer diagram.
Do you support Union-Find and segment trees?
Yes. Disjoint Set Union with union-by-rank and path compression gives near-O(1) amortized operations and shows up in Kruskal MST, Tarjan offline LCA, and connectivity projects. Segment trees with lazy propagation handle range updates and range queries in O(log n) and appear in competitive programming and advanced algorithms work. Both come with worked complexity proofs and stress tests at 100,000 operations. The Union-Find amortization proof uses the inverse Ackermann function bound (alpha(n)) which we walk through with the standard textbook argument. Segment trees come in 2 variants: point update with range query (the simple version) and range update with range query (requires lazy propagation), and we implement the variant the assignment specifies.
