Alex's Notes

  • Excalidraw
    • 2d_feasible_region
    • 5_9_a
    • 5_9_f
    • 5_9_g
    • boolean decision trees.excalidraw
    • decision tree example.excalidraw
    • Drawing 2024-02-10 12.42.01.excalidraw
    • ex_3_1
    • ex_3_1_dfs
    • ex_3_2_a
    • ex_3_2_a_dfs
    • ex_3_2_b
    • ex_3_2_b_dfs
    • ex_3_3
    • ex_3_4_1
    • ex_3_4_2
    • ex_4_1
    • ex_4_1_tree
    • ex_4_2
    • ex_4_8_cex
    • ex_5_1
    • ex_5_1_mst
    • ex_5_2
    • ex_5_2_mst
    • ex_7_10
    • ex_7_10_flow
    • ex_7_17
    • ex_7_17_bottleneck_counter_example
    • ex_7_17_flow
    • ex_7_17_res
    • external_fragmenation.excalidraw
    • feasible_region_with_security_region.excalidraw
    • feasible_region.excalidraw
    • Getting the right fit
    • gridworld_example.excalidraw
    • h_function
    • learning_system_diagram
    • Life graph basic.excalidraw
    • Life graph contors.excalidraw
    • Life graph.excalidraw
    • memory_hardware.excalidraw
    • memory_management_example.excalidraw
    • meta_3_4_1
    • meta_3_4_2
    • multi_level_page_tables.excalidraw
    • Naice Bayes
    • Page tables multi.excalidraw
    • Page tables.excalidraw
    • reduction
    • results_explanation
    • semi-wall-game.excalidraw
    • simple_cycle
    • simple_graph
    • simple_markov_chain
    • simple_path
    • sink_counter_example
    • SVM example
    • thread_data_structures.excalidraw
    • xor_embedding
    • xor_mapped_embedding
  • general
    • Least-recently used (LRU)
    • 2-SAT algorithm using SCC
    • 3-SAT is NP-complete
    • A finite tree that has more than one vertex must have at least two leaf vertices
    • A vertex with the highest post order number lies in a source SCC
    • Access control list (ACL) filters
    • Accuracy
    • Action-advantage function (RL)
    • Activation function
    • Acyclic graph
    • Additive increase Multiplicative Decrease (AIMD)
    • Address Resolution Protocol (ARP)
    • Address space (OS)
    • Adjacency list format (graph)
    • All linear programmes can be represented in standard form
    • Angur
    • Anonymous Functions
    • Application Programming Interface (API)
    • Arithmetic mean
    • Arithmetic mean is greater than or equal to the geometric mean
    • ARP cache
    • Array (data structure)
    • ARTEMIS
    • Associative array
    • Associativity
    • ASwatch
    • Asynchronous programming
    • Atomic instruction
    • Automatic Repeat Request (ARQ)
    • Autonomous system (AS)
    • Autonomous system number (ASN)
    • Backward propagation of errors (Back propagation)
    • Backwards compatibility
    • Bagging
    • Balance between library providers and users
    • Balanced cut problem
    • Battle of the sexes
    • Bayes rule
    • Bayeses optimal classifier
    • Bayesian network
    • Bayesian network if and only if it satisfies the local Markov Property
    • Bellman equation
    • Bellman-Ford algorithm
    • BGP Blackholing
    • BGP Communities
    • BGP Flowspec
    • BGP Hijacking
    • BGP squatting
    • Big-O notation
    • Big-Omega notation
    • Big-Theta notation
    • Binary operation
    • Binary step
    • Binomial coefficient
    • Bipartite graph
    • Bit
    • Bitrate
    • Bitrate adaption
    • Bitwise operations in python
    • Blackholing (BH)
    • Blackholing attack
    • Boarder gateway protocol (BGP)
    • Boolean function
    • Boolean variable
    • Boosting
    • Breadth-first search (BFS)
    • Bridge
    • Broadcast (networks)
    • Broken tea cup
    • Buddy Allocator
    • Byte
    • Cache
    • Cache coherence
    • Calculate polynomial regression coefficients for MSE
    • Carmichael number
    • Causal consistency
    • Chain Hashing
    • Chain multiply problem
    • Chain rule (probability)
    • Check if a linear programme is solvable
    • Checked exceptions
    • Checking if a linear programme is feasible
    • Checkpointing
    • Checksum
    • Checksum in layer 4
    • Chinese remainder theorem
    • Classes in Python
    • Classification problems
    • Client
    • Client-Server model
    • Clique (graph)
    • Clique of a given size problem
    • Clique of a given size problem is in NP
    • Clique of a given size problem is NP-complete
    • Cliques in G are independent sets in the complement
    • Clustering Problem
    • Cocktail party problem
    • Coding Principles
    • Cohesion
    • Comment conventions
    • Complement graph
    • Complete graph
    • Computational Folk theorem
    • Concept
    • Concept class
    • Concurrency
    • Conditional entropy
    • Conditional Independence
    • Conditional probability
    • Conditional variables (Mutex)
    • Congestion control in TCP
    • Conjunctive normal form (CNF)
    • Connected (graph)
    • Connected components (graph)
    • Connection between OSI and IPS models
    • Consistency model
    • Consistent clustering
    • Consistent hashing
    • Consistent learner
    • Consumer Price Index (CPI)
    • Content delivery network (CDN)
    • Context content conclusion (CCC)
    • Context switch (CPU)
    • Conventions
    • Coprime
    • Copy on write (COW)
    • Cost complexity pruning for decision trees (CPP)
    • Count to infinity problem
    • Coupling
    • CPU register
    • Credit assignment problem
    • Cross validation
    • Crossover (genetic algorithms)
    • CRUD API
    • Cut (graph)
    • Cut property
    • Cycle (graph)
    • Cycles in a graph via the DFS tree
    • Data - Object Anti-Symmetry
    • Data structure
    • DDoS reflection and amplification
    • Deadlock
    • Decision tree
    • Declarative Language
    • Default Gateway
    • Degree (graph)
    • Degrees of freedom
    • Demand paging
    • Dependency Inversion Principle (DIP)
    • Dependency Trees (Bayesian Network)
    • Depth-first search (DFS)
    • Descriptor table
    • Design Patterns
    • Device driver
    • DFS for finding strongly connected components
    • DFS to find connected components in an undirected graph
    • DFS to find path in a directed graph
    • DFS to find path in an undirected graph
    • DFS tree (algorithm)
    • Difference between an IP and MAC address
    • Dijkstra's algorithm
    • Dimensionality reduction
    • Direct memory access (DMA)
    • Directed acyclic graph (DAG)
    • Directed graph
    • Discounted rewards
    • Distance vector routing algorithms
    • Distributed algorithm
    • Distributed Denial-of-Service (DDoS)
    • Distributed file system (DFS)
    • Distributed shared memory (DSM)
    • DNS censorship
    • DNS injection
    • DNS records
    • Domain Name System (DNS)
    • Dot product
    • DSN-based content delivery
    • Dual linear programme
    • Duplex
    • Dynamic Adaptive Streaming over HTTP (DASH)
    • Dynamic Host Configuration Protocol (DHCP)
    • Dynamic Programming
    • Eager learner
    • Earliest deadline first (EDF)
    • Edge weights
    • Edmonds-Karp algorithm
    • Eigenvector and Eigenvalue
    • Elimination and Nash Equilibrium
    • Encapsulation
    • End to end principle
    • Ensemble learning
    • epsilon-exhausted version space
    • Epsilon-greedy exploration
    • Equivalent tree definitions
    • Ergodic Markov chain
    • Ergodic Markov chain limiting distrubution
    • Ergodic Markov chains have a unique stationary distribution
    • Error code
    • Error function (modelling)
    • Error Handling
    • Error rate (modelling)
    • Euclid's rule
    • Euclidean algorithm
    • Euler's theorem (modular arithmetic)
    • Euler's totient function
    • Eulers product formula (totient function)
    • Every min-cut has no flow going backwards along it in a max-flow
    • Every min-cut is at full capacity in a max-flow
    • Evolutionary Architecture model (EvoArch)
    • Exact prefix hijacking
    • Exception
    • Exclusive or
    • Existance of Nash equilibrium
    • Existence of a Fermat witness if and only if composite
    • Expectation Maximisation
    • Expected value
    • Explore exploit dilemma
    • Extended Euclidean algorithm
    • External fragmentation
    • F1 score
    • Fast API
    • Fast retransmit
    • Fast-Flux Service Networks (FFSN)
    • Fermat witness
    • Fermat's little theorem
    • File Transfer Protocol (FTP)
    • Filtering (feature selection)
    • Find connected components in a undirected graph
    • Find path in a directed graph
    • Find path in undirected graph
    • Find strongly connected components for an undirected graph
    • Finding rouge networks (FIRE)
    • Finding the maximum likelihood estimation for normally distributed noise is the same as minimising mean squared error
    • Finite Markov Decision Process
    • Firewall
    • First in first out (FIFO) queue
    • First-class object
    • Flow
    • Flow control in TCP
    • Flow network
    • Flows are maximal if there is no augmenting path
    • Fold (cross validation)
    • Folk Theorem
    • Ford-Fulkerson Algorithm
    • Forest (graph)
    • Formatting conventions
    • Fourier Matrix
    • Fragmentation
    • Frame (networks)
    • Function
    • Function codomain
    • Function conventions
    • Function domain
    • Function image
    • Functions in Python
    • Game theory
    • Gateway
    • Gaussian kernel (SVM)
    • Genetic algorithm (meta)
    • Geometric mean
    • Gini index
    • Go back N
    • Gradient decent
    • Graph
    • Graph representations
    • Great Firewall of China (GFW)
    • Greatest common divisor
    • Gridworld
    • Grim trigger strategy
    • Halting problem
    • Happens with high probability
    • Hardware protection levels
    • Hash function
    • Hash table
    • Haussler Theorem
    • Head of line (HOL) blocking
    • Heap (OS)
    • Hill climbing
    • Host (networks)
    • Hot potato routing
    • How I think about work
    • How post order relates to strongly connected components
    • HTTP redirection
    • Hub
    • Hyper Text Transfer Protocol (HTTP)
    • Hyperbolic tangent (tanh)
    • Hyperplane
    • Hypertext Transfer Protocol Secure (HTTPS)
    • If a point in a linear programme has equal objective function to a point in its dual linear programme they are both optimal
    • If two variables are independent conditional entropy excludes the dependent
    • If two variables are independent joint entropy is additive
    • Image Segmentation
    • Image segmentation by max flow
    • Imperative Programming
    • Impossibility Theorem
    • Imposters syndrome
    • Imposture attack (IM)
    • Increasing sequence
    • Incremental learning
    • Independent component analysis
    • Independent events
    • Independent identically distributed samples
    • Independent set (graph)
    • Independent set of a given size
    • Independent set of a given size is in NP
    • Independent set of a given size is NP-complete
    • Indicator function
    • Induced subgraph
    • Inductive bias
    • Infeasible linear programme
    • Inference
    • Information entropy
    • Instance-based learning
    • Integer linear programming is NP-hard
    • Integer linear programming problem
    • Integrated Memory Controller (IMC)
    • Inter-process communication (IPC)
    • Interdomain routing
    • Interface
    • Interface definition language (IDL)
    • Interior gateway protocol (IGP)
    • Internal fragmentation
    • Internet
    • Internet engineering task force (IETF)
    • Internet Exchange Points (IXPs)
    • Internet Protocol (IP)
    • Internet Protocol (IPv4)
    • Internet Protocol Stack (IPS) 4 layers
    • Internet Protocol Stack (IPS) 5 layers
    • Internet protocol stack hourglass shape
    • Internet Service Provider (ISP)
    • Intradomain routing
    • Inverse of the Fourier matrix
    • Inverted page tables (IPT)
    • IP Anycast
    • Iris
    • Irreducible
    • Irreducible error
    • Irreducible Markov chain
    • Irrelevant feature
    • Iterative algorithms
    • Iterative Dichotomiser 3 (ID3)
    • Joint distribution
    • Joint Entropy
    • k-colourings problem (graphs)
    • k-means clustering
    • k-nearest neighbour
    • k-SAT is in NP
    • k-SAT is NP-complete for k greater than or equal to 3
    • k-satisfiability problem (k-SAT problem)
    • Kernel
    • Kernel trick
    • Knapsack Problem
    • Knapsack problem (without repetition)
    • Knapsack-search (without replacement)
    • Knapsack-search is NP
    • Knapsack-search is NP-complete
    • Kruskal's algorithm
    • Kullback–Leibler divergence
    • Lambda functions
    • Layer 1 Physical
    • Layer 2 Data Link
    • Layer 3 Network
    • Layer 4 Transport
    • Layer 5 Session
    • Layer 6 Presentation
    • Layer 7 Application
    • Lazy learner
    • Leaf (graph)
    • Learning rate convergence
    • Length of a probability
    • Linear dimensionality reduction
    • Linear programme
    • Linear programme standard form
    • Linear programming problem
    • Linear regression
    • Linearly separable
    • Link-state routing algorithms
    • Linked lists
    • Local Markov property
    • Logarithms
    • Logging in python
    • Logical and
    • Logical or
    • Loop (graph)
    • MAC address
    • Machine Learning
    • Man-in-the-middle attack (MM)
    • Many-one reduction (problem)
    • Margin for a linear separator
    • Marginalisation (probability)
    • Markdown
    • Markov chain
    • Markov decision process
    • Markup Language
    • Matrix
    • Max clique problem (graph)
    • Max clique problem is NP-hard
    • Max flow problem
    • Max independent set problem (graph)
    • Max independent set problem is NP-hard
    • Max-flow min-cut Theorem
    • Max-k-exact-satisfiability problem
    • Max-SAT is NP-hard
    • Max-SAT random approximation algorithm
    • Max-Satisfiability Problem
    • Maximum a posteriori probability estimate (MAP)
    • Maximum likelihood estimation (MLE)
    • Mean squared error (MSE)
    • Memory allocator
    • Memory controller
    • Memory frame
    • Memory Management Unit (MMU)
    • Memory page
    • Memory segment
    • Memory segmentation
    • Middleboxes
    • MIMIC (meta)
    • MIMIC by dependency trees
    • Min st-cut problem
    • Minimax-Q
    • Minimum Spanning Tree problem (MST)
    • Minimum Spanning Tree problem is in NP
    • Minimum vertex cover problem
    • Minimum vertex cover problem is NP-hard
    • Minmax decision
    • Minmax profile
    • Mistake bound
    • Mixed strategy
    • Model-based reinforcement learning
    • Modelling bias
    • Modelling framework
    • Modelling paradigm
    • Modular arithmetic
    • Modular exponent algorithm
    • Modular exponent problem
    • Modular inverse algorithm (extended Euclidean algorithm)
    • Modular inverse problem
    • Modular multiplicative inverse existence
    • Monitors
    • MPEG-DASH
    • Multi-level page tables
    • Multi-processing
    • Multi-threading
    • Multiplexing
    • Multiprotocol label switching (MPLS)
    • Mutability
    • Mutability in Python
    • Mutation (genetic algorithms)
    • Mutex
    • Mutual information
    • Mutual information is symmetric
    • Naive Bayes classifier
    • Namespaces
    • Naming conventions
    • Nash equilibrium
    • Neighbourhood (graph)
    • NeoVim Cheat Sheet
    • Network
    • Network Address Translation (NAT)
    • Network file system (NFS)
    • Network mask
    • Neural network
    • Node (IPv6)
    • Non-trivial Fermat witnesses are dense
    • Nondeterministic Polynomial time (NP)
    • Normal distribution
    • Northbridge Memory Controller
    • NP-Complete
    • NP-hard
    • Object
    • Objective function
    • Occam's razor
    • Ones complement
    • Open Closed Principle (OCP)
    • Open Shortest Path First (OSPF)
    • Open Systems interconnection (OSI) model
    • OpenFlow
    • Operating system (OS)
    • Optimisation problem
    • Optimistic exploration
    • Optimum play exists for 2-player zero-sum games with perfect information
    • Overfitting
    • P equals NP or P not equals NP
    • p-value
    • PAC learnable bound with VC-dimension
    • PAC-learnable if and only if finite VC dimension
    • Packets
    • Page rank
    • Page rank algorithm
    • Page table
    • Page table entry
    • Paging system
    • Pairwise coprime
    • Palindrome
    • Parallelisation
    • Partition (set)
    • Passing variables to a function
    • Path (graph)
    • Pavlov strategy
    • PCI Express (PCIe)
    • Peer distributed application
    • Peer-peer model
    • Perceptron (neural network)
    • Perceptron rule
    • Perfect information
    • Periodic Markov chain
    • Periodic state (markov chain)
    • Peripheral component interconnect (PCI)
    • Physical Frame Number (PFN)
    • Physical memory
    • Pipe
    • Plausible threat
    • Policy (MDP)
    • Policy Iteration (MDP)
    • Polymorphism
    • Polynomial kernel (SVMs)
    • Polynomial regression
    • Polynomial time
    • Polynomial time is a subset of NP-complete
    • Polysemy
    • Port
    • Portable operating system interface (POSIX)
    • POSIX threads (PThreads)
    • Pre-commit hooks
    • Pre-pruning decision trees
    • Precision
    • Prediction
    • Preference bias
    • Prim's algorithm
    • Prime
    • Principle component analysis
    • Prisoner's dilemma
    • Probability distribution
    • Probably approximately correct learnable (PAC)
    • Proccess modes
    • Procedural Programming
    • Process
    • Process control block (PCB)
    • Process Identification (PID)
    • Product of roots of unity
    • Program counter (PC)
    • Programmed IO (PIO)
    • Programming paradigms
    • Proper vertex colouring
    • Protocol (networks)
    • Pseudo devices
    • Pseudo-header
    • Pseudo-polynomial time
    • Pure strategy
    • Python Built-in Functions
    • Pythonic
    • Q-function (RL)
    • Q-learning
    • Quality function (RL)
    • Quantization
    • Quick sort
    • Race condition
    • Random Access Memory (RAM)
    • Random component analysis
    • Reader-writer locks
    • Recall
    • Rectified linear unit (ReLU)
    • Recursion
    • Refactored
    • Reference counting in Python
    • Regression problems
    • Reinforcement learning
    • Reliable transmission of TCP messages
    • Remote direct memory access (RDMA)
    • Remote procedure calls (PRC)
    • Repeater
    • Request for Comments (RFC)
    • Residual Network (flow)
    • REST API
    • Restart hill climbing
    • Restriction Bias
    • Result types
    • Retail Price Index (RPI)
    • Return (RL)
    • Reverse directed graph
    • Reversible Markov chain
    • Rich clustering
    • Rivest-Shamir-Adleman algorithm (RSA algorithm)
    • Rooted tree
    • Round robin DNS (RRDNS)
    • Round trip time (RTT)
    • Route summarization
    • Router
    • Router (IPv6)
    • Routing
    • Routing Information Protocol (RIP)
    • Routing table
    • Rudrata cycle
    • Rudrata cycle problem
    • Rudrata path
    • Rudrata path problem
    • Run time complexity
    • Sample complexity
    • SAT is NP-complete
    • Satisfiability problem (SAT problem)
    • SBRI model
    • Scale-invariant clustering
    • Search problems
    • Secure Socket Layer (SSL)
    • Security region
    • Segment
    • Semaphores
    • Semi-wall stochastic game
    • Separation of concerns (SoC)
    • Sequence
    • Sequential consistency
    • Server
    • Session Initiation Protocol (SIP)
    • Side effect
    • Sigmoid function
    • Sigmoid kernel (SVM)
    • Sign function
    • Signed or unsigned integers
    • Simple Mail Transfer Protocol (SMTP)
    • Simplex method (linear programme)
    • Simulated Annealing
    • Simulated annealing ending probability
    • Single linkage clustering
    • Single Responsibility Principle (SRP)
    • Singleton
    • Slab allocator
    • Socket
    • Soft clustering
    • Software defined networks (SDN)
    • SOLID principles
    • Sorting problem
    • Spanning subgraph
    • Spanning Tree Protocol (STP)
    • Special case pattern
    • Special functions
    • Spinlocks
    • Spoofing
    • Spurious wakeups
    • st-cut
    • Stack (OS)
    • Stack Pointer (SP)
    • Standard deviation
    • Start and end point bias
    • Stationary distribution (Markov Chains)
    • Step function
    • Step function methods
    • Stirling's approximation
    • Stochastic games
    • Stochastic matrix
    • Stop and wait ARQ
    • Strict consistency
    • Strictly dominated strategy
    • Strong duality theorem (linear programme)
    • Strong duality theorem optimum (linear programme)
    • Strongly connected (directed graphs)
    • Strongly connected component graph (directed graph)
    • Strongly connected components (directed graphs)
    • Strongly relevant feature
    • Sub-prefix hijacking
    • Subgame perfect
    • Subgraph
    • Subnets
    • Subsequence
    • Subset-sum problem
    • Subset-sum problem is in NP
    • Subset-sum problem is NP-complete
    • Substring
    • Sum of roots of unity
    • Supervised learning
    • Support vector machines (SVM)
    • Switch
    • Switching
    • Symmetric Markov chain
    • Symmetric Markov chains have a uniform stationary distribution
    • Synchronization
    • Synonymy
    • System call
    • Taking the reverse respects going to the strongly connected component graph
    • TCP 3 way handshake
    • TCP connection teardown
    • TCP CUBIC
    • TCP Reno
    • Test Driven Development (TDD)
    • Testing conventions
    • Testing data
    • The 5S Philosophy
    • The curse of dimensionality
    • The dual dual linear programme is the original linear programme
    • The flow across an st-cut is equal to the value of the flow itself
    • The Halting problem is undecidable
    • The k-colourings problem is in NP
    • The k-colourings problem is NP-complete
    • The Law of Demeter
    • The perceptron rule using binary step converges in finite time if the dataset is linearly separable
    • The Satisfiability problem is in NP
    • The strongly connected component graph is a DAG
    • The strongly connected components are the same in a directed graph and its reverse
    • Thread
    • Tit for Tat
    • Topological sorting (DAG)
    • Traffic Engineering Framework
    • Traffic Scrubbing Service
    • Train Wrecks
    • Training data
    • Training error
    • Trampolining
    • Transforming discrete input for regression
    • Transitions (MDP)
    • Translation Lookaside Buffer (TLB)
    • Transmission control in TCP
    • Transmission Control Protocol (TCP)
    • Transport Layer Security (TLS)
    • Trap instruction
    • Traveling salesman problem
    • Traveling salesman problem (search)
    • Tree (graph)
    • Trie
    • True error
    • Two's complement
    • Type-0 hijacking
    • Type-N hijacking
    • Type-U hijacking
    • Unbounded linear programme
    • Unbounded linear programmes have infeasible duals
    • Undecidable problem
    • Underfitting
    • Unicast
    • Uniform distribution
    • Uniqueness of inverses
    • Unsupervised learning
    • Useful feature
    • User Datagram Protocol (UDP)
    • Using multiple git profiles
    • Value function (RL)
    • Value iteration (MDP)
    • Vapnik-Chervonenkis dimension
    • Variables in python
    • Variance
    • Version space
    • Vertex Colouring
    • Vertex cover
    • Vertex cover if and only if the complement is an independent set
    • Vertex cover of a given size
    • Vertex cover of a given size is NP
    • Vertex cover of a given size is NP-complete
    • Vertex degree sum in a graph
    • Virtual Local Area Networks (VLAN)
    • Virtual machine monitor (VMM)
    • Virtual memory
    • Virtual page number (VPN)
    • Virtualization
    • Voice over IP (VoIP)
    • Weak consistency
    • Weak duality theorem (linear programme)
    • Weak learner
    • Weakly relevant feature
    • Webgraph
    • When to Use Error Codes and Exceptions
    • Wrap 3rd party libraries
    • Wrapping (feature selection)
    • Zero-sum game
  • Introduction to Statistical Learning in Python
    • 1. Introduction
    • 2. Linear regression
    • 6. Non-linear methods
    • 9. Support Vector Machines
  • OMSCS
    • CS6200
      • Week 1 - Are you ready?
      • Week 1 - Course material
      • Week 1 - Introduction to Operating systems
      • Week 2 - Processes and Process Management
      • Week 3 - Threading and concurrency
      • Week 4 - PThreads
      • Week 5 - Beyond Multiprocessing ... Multithreading and the SunOS Kernel
      • Week 5 - Implementing Lightweight Threads (paper)
      • Week 5 - Thread design Considerations
      • Week 6 - Thread Performance Considerations
      • Week 7 - Scheduling
      • Week 8 - Memory management
      • Week 9 - Inter-process communication
      • Week 10 - Synchronization Constructs
      • Week 11 - IO management
      • Week 12 - Virtualization
      • Week 13 - Remote Procedure Calls
      • Week 14 - Distributed file systems
      • Week 15 - Distributed shared memory
      • Week 16 - Datacenter technologies
    • CS6215
      • Week 1 - Dynamic Programming
      • Week 1 - Knapsack Problem
      • Week 2 - Chain Matrix Multiply
      • Week 2 - Shortest Paths
      • Week 3 - Fast Integer multiplication
      • Week 3 - Linear-Time Median
      • Week 3 - Solving Recurrences
      • Week 4 - Fast Fourier Transforms
      • Week 6 - 2-Satisfiability
      • Week 6 - Graph algorithms - strongly connected components
      • Week 6 - Minimum Spanning Tree
      • Week 7 - Edmonds-Karp algorithm
      • Week 7 - Ford-Fulkerson Algorithm
      • Week 7 - Image Segmentation
      • Week 7 - Max-flow Generalizations
      • Week 7 - Max-Flow Min-Cut
      • Week 8 - Bloom Filters
      • Week 8 - Modular Arithmetic
      • Week 8 - RSA
      • Week 9 - Algorithms for the exam
      • Week 10 - Graph problem complexity
      • Week 10 - NP overview
      • Week 10 - NP-completeness
      • Week 11 - Linear Programming
      • Week 12 - Halting problem
      • Week 12 - Knapsack complexity
      • Week 12 - Max-SAT approximation algorithm
      • Week 13 - Known NP-complete problems
      • Week 15 - Markov Chains
    • CS6250
      • Week 0 - Assumed knowledge
      • Week 1 - Introduction
      • Week 2 - Transport and application layer
      • Week 3 - Intradomain routing
      • Week 4 - AS relationships and interdomain routing
      • Week 5 - Router design and algorithms
      • Week 6 - Exam prep
      • Week 7 - Software defined networks
      • Week 8 - Security
      • Week 9 - Censorship
      • Week 10 - Video applications
      • Week 11 - CDNs and overlay networks
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  • Alex Wendland
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  • Online Masters of Science in Computer Science, Georgia Tech (OMSCS)
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Forest (graph)

Forest (graph)

Sep 26, 20231 min read

  • maths
  • graph-theory

Forest

A graph is a forest if each of its connected components is a tree.


Graph View

Backlinks

  • Week 6 - Graph algorithms - strongly connected components
  • DFS to find path in an undirected graph
  • DFS tree (algorithm)

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