BSCS4021 · workspace

Advanced Algorithms

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Weekly outline

Syllabus

Week topics from the course map

00W00

Topic

Incomplete

01W01

Greedy Algorithms: Storing Files on Tape; Scheduling Classes; Stable Matchings

Incomplete

02W02

Matroids: A Generic Optimization Problem, Motivating the Definition, Examples of Matroids, Scheduling with Deadlines

Incomplete

03W03

Dynamic Programming: Longest Increasing Subsequence, Edit Distance, Subset Sum, Optimal BSTs

Incomplete

04W04

Maximum Flows: Flows, Cuts, Maxflow-Mincut, Augmenting Paths, Bipartite Matchings, Other Settings

Incomplete

05W05

Applications of Flows: Exam Scheduling, Baseball Elimination, Project Selection

Incomplete

06W06

NP-hardness: P, NP, NP-hardness, NP-completeness, Reductions and SAT, 3SAT, Maximum Independent Set, Graph Coloring, Subset Sum

Incomplete

07W07

Approximation Algorithms: Introduction to Approximation Frameworks, Vertex Cover via Maximal Matchings, Vertex Cover via LP rounding, TSP, Set Cover

Incomplete

08W08

Randomized Algorithms – Monte Carlo v. Las Vegas, Min-Cut Algorithm, MAX SAT via the Probabilistic Methods, 2SAT via Markov Chains, Primality Testing

Incomplete

09W09

Exact Algorithms – Branch and Bound, An Inclusion-Exclusion approach to Hamiltonian Path, Dynamic Programming for TSP, Local Search

Incomplete

010W10

Parameterized Algorithms – Closest String, Iterative Compression for FVS, Randomized Algorithm for k-Path, DP over subsets - Set Cover

Incomplete

011W11

Kernelization – Vertex Cover, Matrix Rigidity, Feedback Arc Set on Tournaments, Max Sat, Edge Clique Cover

Incomplete

012W12

Practical Approaches to Coping with Hardness – SAT Solvers, SAT reductions, LP solvers, LP reductions

Incomplete

Study tools

Memorize first

Compact recall strip

Open notes

3 min

Advanced Data Structures — Segment Trees, Fenwick Trees, Disjoint Sets

Open this first for the core logic and traps.

Dive in

3 min

Approximation Algorithms

Open this first for the core logic and traps.

Dive in

3 min

Exact Algorithms — Branch & Bound, DP for TSP, Local Search

Open this first for the core logic and traps.

Dive in

Practice Dock

One drill at a time, with Theo hints, teacher lanes, and a replay queue.

Use this when the theory is already clear and the goal is fast, clean retrieval under pressure. Mark weak questions, copy a revision pack, and move on.

TheoTeacherTextbookSpeedrun

Ask less. Recall more.

Open Practice Dock

SM-2 Active

Spaced Repetition

Flashcards

Spaced repetition engine for long-term retention of core concepts.

Start session

Not Available

Interactive Simulations

Visual Labs

Exam Mode

Adaptive Practice

Mock Exam

Set paper

Code Protocol

OPPE Simulator

Secure Comm-Link Terminal

Secure Comm-Link // Playlist Connected

Uplink 12ms

Syllabus Synchronization: Active

Open current cue

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Foundational

Diploma

BSc Degree

BS Degree

PG / MTech

BSCS4021

BS Degree

4 Credits

Advanced Algorithms

To introduce advanced ideas in design of algorithms; To study the performance guarantees of algorithms; To introduce methods for coping with NP-har...

Deployment Hub External Docs

Execution Protocol

Module 0

Topic

Module 1

Greedy Algorithms: Storing Files on Tape; Scheduling Classes; Stable Matchings

Module 2

Matroids: A Generic Optimization Problem, Motivating the Definition, Examples of Matroids, Scheduling with Deadlines

Module 3

Dynamic Programming: Longest Increasing Subsequence, Edit Distance, Subset Sum, Optimal BSTs

Module 4

Maximum Flows: Flows, Cuts, Maxflow-Mincut, Augmenting Paths, Bipartite Matchings, Other Settings

Module 5

Applications of Flows: Exam Scheduling, Baseball Elimination, Project Selection

Module 6

NP-hardness: P, NP, NP-hardness, NP-completeness, Reductions and SAT, 3SAT, Maximum Independent Set, Graph Coloring, Subset Sum

Module 7

Approximation Algorithms: Introduction to Approximation Frameworks, Vertex Cover via Maximal Matchings, Vertex Cover via LP rounding, TSP, Set Cover

Module 8

Randomized Algorithms – Monte Carlo v. Las Vegas, Min-Cut Algorithm, MAX SAT via the Probabilistic Methods, 2SAT via Markov Chains, Primality Testing

Module 9

Exact Algorithms – Branch and Bound, An Inclusion-Exclusion approach to Hamiltonian Path, Dynamic Programming for TSP, Local Search

Module 10

Parameterized Algorithms – Closest String, Iterative Compression for FVS, Randomized Algorithm for k-Path, DP over subsets - Set Cover

Module 11

Kernelization – Vertex Cover, Matrix Rigidity, Feedback Arc Set on Tournaments, Max Sat, Edge Clique Cover

Module 12

Syllabus

Topic

Greedy Algorithms: Storing Files on Tape; Scheduling Classes; Stable Matchings

Matroids: A Generic Optimization Problem, Motivating the Definition, Examples of Matroids, Scheduling with Deadlines

Dynamic Programming: Longest Increasing Subsequence, Edit Distance, Subset Sum, Optimal BSTs

Maximum Flows: Flows, Cuts, Maxflow-Mincut, Augmenting Paths, Bipartite Matchings, Other Settings

Applications of Flows: Exam Scheduling, Baseball Elimination, Project Selection

NP-hardness: P, NP, NP-hardness, NP-completeness, Reductions and SAT, 3SAT, Maximum Independent Set, Graph Coloring, Subset Sum

Approximation Algorithms: Introduction to Approximation Frameworks, Vertex Cover via Maximal Matchings, Vertex Cover via LP rounding, TSP, Set Cover

Randomized Algorithms – Monte Carlo v. Las Vegas, Min-Cut Algorithm, MAX SAT via the Probabilistic Methods, 2SAT via Markov Chains, Primality Testing

Exact Algorithms – Branch and Bound, An Inclusion-Exclusion approach to Hamiltonian Path, Dynamic Programming for TSP, Local Search

Parameterized Algorithms – Closest String, Iterative Compression for FVS, Randomized Algorithm for k-Path, DP over subsets - Set Cover

Kernelization – Vertex Cover, Matrix Rigidity, Feedback Arc Set on Tournaments, Max Sat, Edge Clique Cover

Practical Approaches to Coping with Hardness – SAT Solvers, SAT reductions, LP solvers, LP reductions

Compact recall strip

One drill at a time, with Theo hints, teacher lanes, and a replay queue.

Flashcards

Visual Labs

Mock Exam

OPPE Simulator

Secure Comm-Link Terminal

Syllabus Matrix Registry

Master Registry

Foundational

Diploma

BSc Degree

BS Degree

PG / MTech

Advanced Algorithms

Execution Protocol

Topic

Greedy Algorithms: Storing Files on Tape; Scheduling Classes; Stable Matchings

Matroids: A Generic Optimization Problem, Motivating the Definition, Examples of Matroids, Scheduling with Deadlines

Dynamic Programming: Longest Increasing Subsequence, Edit Distance, Subset Sum, Optimal BSTs

Maximum Flows: Flows, Cuts, Maxflow-Mincut, Augmenting Paths, Bipartite Matchings, Other Settings

Applications of Flows: Exam Scheduling, Baseball Elimination, Project Selection

NP-hardness: P, NP, NP-hardness, NP-completeness, Reductions and SAT, 3SAT, Maximum Independent Set, Graph Coloring, Subset Sum

Approximation Algorithms: Introduction to Approximation Frameworks, Vertex Cover via Maximal Matchings, Vertex Cover via LP rounding, TSP, Set Cover

Randomized Algorithms – Monte Carlo v. Las Vegas, Min-Cut Algorithm, MAX SAT via the Probabilistic Methods, 2SAT via Markov Chains, Primality Testing

Exact Algorithms – Branch and Bound, An Inclusion-Exclusion approach to Hamiltonian Path, Dynamic Programming for TSP, Local Search

Parameterized Algorithms – Closest String, Iterative Compression for FVS, Randomized Algorithm for k-Path, DP over subsets - Set Cover

Kernelization – Vertex Cover, Matrix Rigidity, Feedback Arc Set on Tournaments, Max Sat, Edge Clique Cover

Practical Approaches to Coping with Hardness – SAT Solvers, SAT reductions, LP solvers, LP reductions

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