Constrained

The constrained module in GTSAM provides constrained nonlinear optimization on top of factor graphs. It includes classes for representing constraints, building constrained problems, and solving them with penalty and augmented Lagrangian methods.

Core Problem Model¶

• ConstrainedOptProblem: Holds objective costs, equality constraints, and inequality constraints.

• ConstrainedOptProblem::AuxiliaryKeyGenerator: Generates keys for auxiliary variables used when transforming inequality constraints.

• NonlinearConstraint: Base class for nonlinear constraints represented as constrained NoiseModelFactor objects.

Equality Constraints¶

• NonlinearEqualityConstraint: Base class for constraints of the form h(x) = 0.

• ExpressionEqualityConstraint<T>: Equality constraint from an expression and right-hand side.

• ZeroCostConstraint: Equality constraint that enforces zero residual on a cost factor.

• NonlinearEqualityConstraints: Container graph for equality constraints.

Inequality Constraints¶

• NonlinearInequalityConstraint: Base class for constraints of the form g(x) <= 0.

• ScalarExpressionInequalityConstraint: Scalar expression-based inequality constraint.

• NonlinearInequalityConstraints: Container graph for inequality constraints.

• InequalityPenaltyFunction: Interface for ramp-like penalty mappings used with inequality constraints. Derived classes:

  • RampFunction

  • SmoothRampPoly2

  • SmoothRampPoly3

  • SoftPlusFunction

Optimizers¶

• ConstrainedOptimizerParams, ConstrainedOptimizerState, ConstrainedOptimizer: Shared base interfaces and iteration state for constrained solvers.

• PenaltyOptimizerParams, PenaltyOptimizerState, PenaltyOptimizer: Penalty method solver and its parameters/state.

• AugmentedLagrangianParams, AugmentedLagrangianState, AugmentedLagrangianOptimizer: Augmented Lagrangian solver and its parameters/state.

How the Pieces Fit Together¶

For a new user, it helps to think in two phases:

1. Build a constrained problem.

2. Run a constrained solver on that problem.

Inequality constraints can use different smooth penalty shapes via InequalityPenaltyFunction (ramp, smooth polynomial ramps, or softplus), which controls behavior near the active constraint boundary.

1) Build the Problem¶

This stage is about modeling: you separate what you want to minimize (objective terms) from what must hold (constraints), then combine them into a single ConstrainedOptProblem object that the solvers can consume.

2) Solve the Problem¶

This stage is algorithmic: pick a constrained solver, form iterative unconstrained subproblems internally, and solve those subproblems with a standard nonlinear optimizer until constraint violation and cost are reduced.