> For the complete documentation index, see [llms.txt](https://docs.algenta.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.algenta.ai/optimization-and-simulation.md).

# Optimization & simulation

- [Optimization & decision programs](https://docs.algenta.ai/optimization-and-simulation/optimization.md): Solve linear, integer, assignment and network-flow programs with compiled kernels on your own machine — and be told when no answer exists.
- [Optimization benchmark plane](https://docs.algenta.ai/optimization-and-simulation/optimization-model-formulation.md): A flat typed LP/MIP formulation container, its refusal-first validator and x>=0 lowering, a solve bridge that re-audits every answer in the caller's own coordinates, and a judge that scores a solved o
- [Routing, scheduling & trade-offs](https://docs.algenta.ai/optimization-and-simulation/optimization-heuristics.md): Vehicle routes, schedules, convex quadratic programs, Pareto fronts and decisions under uncertainty — searched on your own machine, reporting the bound you have rather than claiming an optimum.
- [General-purpose optimization kernels](https://docs.algenta.ai/optimization-and-simulation/optimization-suite-kernels.md): Constrained nonlinear optimization by penalty/barrier/Lagrangian methods, four metaheuristic search strategies, scalar root-finding and minimization on polynomials, and ten modern deep-learning optimi
- [Discrete-event simulation & queueing](https://docs.algenta.ai/optimization-and-simulation/discrete-event-simulation.md): Discrete-event simulation with a totally-ordered event calendar, resource pools with priority queues, warm-up detection that reports what it discarded
- [Strategy comparison & simulation](https://docs.algenta.ai/optimization-and-simulation/strategy-simulation.md): Compare strategies on common random numbers, rank them with the multiplicity corrected, and get "no significant difference" as an answer instead of a coin flip.
- [Contest simulation & service timing](https://docs.algenta.ai/optimization-and-simulation/contest-strategy.md): Simulate N competitors over discrete rounds and compare service-timing options under common random numbers, with simultaneous intervals and an explicit abstain.
- [Reproducible evolutionary search](https://docs.algenta.ai/optimization-and-simulation/reproducible-search.md): Evolutionary parameter search whose every draw is addressable, so a fitted result replays bit-exactly on any machine and any core count.
- [Reinforcement learning and stochastic simulation kernels](https://docs.algenta.ai/optimization-and-simulation/reinforcement-learning-and-stochastic-simulation-kernels.md): Tabular Q-learning, SARSA and value iteration over an explicit MDP, a linear-Gaussian Kalman filter, reservoir and weighted stream sampling, and constant-memory Count-Min/HyperLogLog sketches.
- [Asset degradation state](https://docs.algenta.ai/optimization-and-simulation/degradation-state.md): Track an asset whose performance decays with usage age — a two-state filter with wear clamping, load correction and a cliff term for the non-linear end of life.


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