API Documentation

This section provides detailed API documentation for all LINE Solver Python modules.

API Sections:

Overview

Core Modules - Networks, nodes, classes, distributions, solvers
Cache Algorithms - Cache performance analysis
Loss Networks - Analysis of networks with blocking
Layered Stochastic Networks - Layered queueing network analysis
Markov Chain Utilities - CTMC and DTMC analysis tools
Matrix-Analytic Methods - QBD processes and matrix-analytic solutions
Non-Product-Form Networks - Approximations for intractable queueing networks
Polling Systems - Analysis of gated, exhaustive, and k-limited polling
Product-Form Queueing Networks - MVA, convolution, and normalizing constant methods
Queueing Systems - Single-station queueing system analysis
Reinforcement Learning - RL utilities for queueing control
Stochastic Network Utilities - Network analysis and transformation utilities
Utilities and Constants - Helper functions, constants, and enumerations

Category	Python Path	Description
Cache API	line_solver.api.cache	Cache analysis algorithms
Constants	line_solver.constants	Enumerations and global constants
Core	line_solver.lang	Network, Node, and JobClass definitions
Distributions	line_solver.distributions	Exp, Erlang, PH, MAP, etc.
Layered Networks	line_solver.layered	LayeredNetwork, Task, Activity
LossN API	line_solver.api.lossn	Loss network algorithms
LSN API	line_solver.api.lsn	Layered stochastic network utilities
MAM API	line_solver.api.mam	Matrix-analytic methods
MC API	line_solver.api.mc	Markov chain analysis
NPFQN API	line_solver.api.npfqn	Non-product-form network approximations
PFQN API	line_solver.api.pfqn	Product-form queueing network algorithms
Polling API	line_solver.api.polling	Polling system algorithms
Qsys API	line_solver.api.qsys	Single queueing system formulas
RL API	line_solver.api.rl	Reinforcement learning utilities
SN API	line_solver.api.sn	Stochastic network utilities
Solvers	line_solver.solvers	MVA, CTMC, Fluid, MAM, etc.