Module core 

Framework: traits, state shapes, the iteration driver, and the termination layer. The slot taxonomy is:

• problem — what the user implements about their objective: CostFunction, Gradient, Residual / Jacobian (least squares), and Hessian (second order). Future: operators (matrix-free).
• numdiff — finite-difference derivative synthesis: the FiniteDiff wrapper adds Gradient / Jacobian / Hessian to a problem that only exposes function values.
• constraint — constraint markers carried on the problem (tenet 4 in AGENTS.md): BoxConstraints and LinearInequalityConstraints.
• barrier — the LogBarrier adapter that rewrites a linearly-constrained problem as the unconstrained log-barrier objective consumed by the BarrierMethod.
• state — what a solver carries between iterations: State for the minimum, GradientState / SimplexState when a solver carries richer info that termination criteria can read.
• solver — the Solver trait every concrete solver implements. Lifecycle is init once, then repeated next_iter, with an optional terminate hook.
• termination — the framework-level TerminationCriterion trait plus shipped criteria. Each criterion bounds on the minimum state shape it needs (tenet 3), so mismatches are compile errors rather than runtime no-ops.
• executor — the driver: Executor / Stepper / run_loop. The canonical iteration ordering is documented on the executor module.
• inner — the composition adapter: InnerExecutor wraps run_loop for outer solvers that drive an inner solver per outer iteration. See AGENTS.md “Solver composition” for the contracts.
• math — the small shared math layer (ScaledAdd, NormSquared, …) that backend-generic solvers depend on. Per tenet 5, this stays honest: only ops every backend can implement well live here. LA-heavy ops will live in a separate tier when the first solver wants them.

Modules

augmented_lagrangian

Augmented-Lagrangian adapter for linear equality constraints.

barrier

Log-barrier adapter for linear inequality constraints.

constraint

Constraint markers carried on the problem (tenet 4 in AGENTS.md). BoxConstraints (interval bounds, consumed by projection-based solvers), LinearInequalityConstraints (A x ≤ b, consumed by the log-barrier method), and LinearEqualityConstraints (A x = b, consumed by the augmented-Lagrangian method).

executor

Iteration driver. The high-level entry point is Executor; Stepper exposes one-iteration-at-a-time control, and run_loop is the borrowed-problem variant used by composed solvers.

inner

Composition adapter: drive an inner solver from inside an outer solver’s next_iter.

math

Math abstraction the solvers depend on.

numdiff

Finite-difference derivative synthesis.

problem

Problem traits the user implements about their objective. Solvers bind on whichever subset they need (e.g. gradient descent requires CostFunction and Gradient; Nelder-Mead only needs CostFunction).

rng

Seedable, wasm-safe RNG used by stochastic solvers.

solver

The Solver trait every concrete solver implements. See the trait contract for the lifecycle (init once, then repeated next_iter, with an optional terminate hook) and the executor module for the canonical iteration ordering.

state

Solver state shapes.

termination

Termination layer: the TerminationCriterion trait and the framework-level criteria solvers can be terminated by. Each criterion bounds on the minimum state shape it needs (tenet 3 in AGENTS.md), so mismatches are compile errors rather than runtime no-ops.