Module optimization 

Optimization & Root Finding - Zero-Allocation Implementation

This module provides fixed-size stack-based optimization algorithms and root finding methods suitable for the #![no_std] environment of Qualia-DB.

Modules

metaheuristics

General-dimension metaheuristic optimizers (hill-climbing / simulated annealing / Artificial Bee Colony) — global search beyond the fixed-[f64;4] solvers below. Heap-using batch-analytics layer; the engine ontology alignment consumes it. General-dimension metaheuristic optimizers (CI-SKM ch 4/6) — global / non-convex search beyond the fixed-[f64;4] local solvers in this category. Generic local search + simulated annealing work over any state (continuous vectors or combinatorial structures via a neighbour closure — the engine ontology alignment consumes), plus a continuous population optimizer (Artificial Bee Colony).

Structs

BoundedNewtonRaphson

Bounded Newton-Raphson root finder

CurveFitState

Curve fitting state tracking

LevenbergMarquardtStack

Levenberg-Marquardt curve fitting optimizer

NelderMeadSimplex

Nelder-Mead simplex optimizer for unconstrained optimization

OptimizationState

Optimization state tracking

RootFindingState

Root finding state tracking

Traits

CurveFitFunction

Curve fitting function trait

ObjectiveFunction

Objective function trait for optimization

RootFunction

Root finding function trait