Nonlinear Functions and Jacobian Types

DocStringExtensions.TypeDefinition()

A representation of an interval nonlinear system of equations f, defined by:

\[f(t,p) = u = 0\]

and all of its related functions. For all cases, p are the parameters and t is the interval variable.

Constructor

IntervalNonlinearFunction{iip, specialize}(f;
                           analytic = __has_analytic(f) ? f.analytic : nothing,
                           sys = __has_sys(f) ? f.sys : nothing)

Note that only the function f itself is required. This function should be given as f!(u,t,p) or u = f(t,p). See the section on iip for more details on in-place vs out-of-place handling.

All of the remaining functions are optional for improving or accelerating the usage of f. These include:

• analytic(p): used to pass an analytical solution function for the analytical solution of the ODE. Generally only used for testing and development of the solvers.

iip: In-Place vs Out-Of-Place

For more details on this argument, see the ODEFunction documentation.

specialize: Controlling Compilation and Specialization

For more details on this argument, see the ODEFunction documentation.

Fields

The fields of the IntervalNonlinearFunction type directly match the names of the inputs.

DocStringExtensions.TypeDefinition()

A representation of a nonlinear system of equations f, defined by:

\[0 = f(u,p)\]

and all of its related functions, such as the Jacobian of f, its gradient with respect to time, and more. For all cases, u0 is the initial condition, p are the parameters, and t is the independent variable.

Constructor

NonlinearFunction{iip, specialize}(f;
                           analytic = __has_analytic(f) ? f.analytic : nothing,
                           jac = __has_jac(f) ? f.jac : nothing,
                           jvp = __has_jvp(f) ? f.jvp : nothing,
                           vjp = __has_vjp(f) ? f.vjp : nothing,
                           jac_prototype = __has_jac_prototype(f) ? f.jac_prototype : nothing,
                           sparsity = __has_sparsity(f) ? f.sparsity : jac_prototype,
                           paramjac = __has_paramjac(f) ? f.paramjac : nothing,
                           colorvec = __has_colorvec(f) ? f.colorvec : nothing,
                           sys = __has_sys(f) ? f.sys : nothing)

Note that only the function f itself is required. This function should be given as f!(du,u,p) or du = f(u,p). See the section on iip for more details on in-place vs out-of-place handling.

All of the remaining functions are optional for improving or accelerating the usage of f. These include:

• analytic(u0,p): used to pass an analytical solution function for the analytical solution of the ODE. Generally only used for testing and development of the solvers.
• jac(J,u,p) or J=jac(u,p): returns $\frac{df}{du}$
• jvp(Jv,v,u,p) or Jv=jvp(v,u,p): returns the directional derivative $\frac{df}{du} v$
• vjp(Jv,v,u,p) or Jv=vjp(v,u,p): returns the adjoint derivative $\frac{df}{du}^\ast v$
• jac_prototype: a prototype matrix matching the type that matches the Jacobian. For example, if the Jacobian is tridiagonal, then an appropriately sized Tridiagonal matrix can be used as the prototype and integrators will specialize on this structure where possible. Non-structured sparsity patterns should use a SparseMatrixCSC with a correct sparsity pattern for the Jacobian. The default is nothing, which means a dense Jacobian.
• paramjac(pJ,u,p): returns the parameter Jacobian $\frac{df}{dp}$.
• colorvec: a color vector according to the SparseDiffTools.jl definition for the sparsity pattern of the jac_prototype. This specializes the Jacobian construction when using finite differences and automatic differentiation to be computed in an accelerated manner based on the sparsity pattern. Defaults to nothing, which means a color vector will be internally computed on demand when required. The cost of this operation is highly dependent on the sparsity pattern.

iip: In-Place vs Out-Of-Place

For more details on this argument, see the ODEFunction documentation.

specialize: Controlling Compilation and Specialization

For more details on this argument, see the ODEFunction documentation.

Fields

The fields of the NonlinearFunction type directly match the names of the inputs.