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warp.optim.linear.LinearSolverState#

class warp.optim.linear.LinearSolverState(
A,
b,
x,
tol=None,
atol=None,
maxiter=0,
M=None,
callback=None,
check_every=10,
use_cuda_graph=True,
)[source]#

Pre-allocated state for a linear iterative solver.

Holds all temporary buffers required by the solver plus a reference to the original system (A, b, x, optional M). Calling the state runs the solver, optionally substituting a new compatible matrix, right-hand-side, solution vector, or preconditioner. This avoids repeated buffer allocation when the same solver is applied many times to systems that share the same shape, batch count, dtype, and device.

Parameters:

  • A (array | BsrMatrix | LinearOperator) – the linear system’s left-hand-side

  • b (array) – the linear system’s right-hand-side

  • x (array) – initial guess and solution vector

  • tol (float | None) – relative tolerance for the residual, as a ratio of the right-hand-side norm

  • atol (float | None) – absolute tolerance for the residual

  • maxiter (float | None) – maximum number of iterations to perform before aborting. Defaults to the system size.

  • M (array | BsrMatrix | LinearOperator | None) – optional preconditioner.

  • callback (Callable | None) – function to be called every check_every iteration with the current iteration number, residual and tolerance.

  • check_every (int) – number of iterations every which to call callback and check the residual against the tolerance.

  • use_cuda_graph (bool) – whether to capture the solver iteration as a CUDA graph for reduced launch overhead.

Instances are not directly constructible — use the solver-specific subclasses CG, CR, BiCGSTAB, and GMRES, or obtain one by calling the corresponding solver function (cg(), cr(), bicgstab(), gmres()) with run=False.

__init__(
A,
b,
x,
tol=None,
atol=None,
maxiter=0,
M=None,
callback=None,
check_every=10,
use_cuda_graph=True,
)[source]#
Parameters:

Methods

__init__(A, b, x[, tol, atol, maxiter, M, ...])