solve_collocation_system(fun, t, y, h, Z0, scale, tol, LU_real, LU_complex, solve_lu)

Parameters

fun : callable

Right-hand side of the system.

t : float

Current time.

y : ndarray, shape (n,)

Current state.

h : float

Step to try.

Z0 : ndarray, shape (3, n)

Initial guess for the solution. It determines new values of y at t + h * C as y + Z0 , where C is the Radau method constants.

scale : ndarray, shape (n)

Problem tolerance scale, i.e. rtol * abs(y) + atol .

tol : float

Tolerance to which solve the system. This value is compared with the normalized by :None:None:`scale` error.

LU_real, LU_complex :

LU decompositions of the system Jacobians.

solve_lu : callable

Callable which solves a linear system given a LU decomposition. The signature is solve_lu(LU, b) .

Returns

converged : bool

Whether iterations converged.

n_iter : int

Number of completed iterations.

Z : ndarray, shape (3, n)

Found solution.

rate : float

The rate of convergence.

Solve the collocation system.

Examples

See :

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them

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GitHub : /scipy/integrate/_ivp/radau.py#48
type: <class 'function'>
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