Comments (3)
Indeed, good catch. We need to set x0=init
in the gmres
call. Also I'm not sure if forwarding the kwargs is a good idea in the end. This could lead to us breaking user code if we make changes to argument names...
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That's true... Should we explicitly call the arguments from the underlying solvers in jax.scipy.sparse.linalg
? The main point of these wrappers is to add the ridge
argument... Should we write the with_ridge
as a decorator over the underlying solvers?
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I guess one of my early motivations was to have matvec support even for direct linear solvers (by materializing the matrix in memory).
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Related Issues (20)
- OSQP solvers with fun set and no init_params have misleading error message HOT 8
- BoxOSQP does not work without equality constraints HOT 5
- Type precision issue in BoxOSQP HOT 10
- Garbage collection issues HOT 1
- Wrong failure diagnostic print outs from `ZoomLineSearch` under `vmap` HOT 3
- Attempted boolean conversion of traced array - for hager-zhang HOT 2
- Expression tree API like CVXPY HOT 3
- Unnecessary recompilation of _while_loop_lax HOT 8
- Add type annotations
- Consider switching to pyproject.toml
- OSQP crashing on unexpected params HOT 3
- `verbose=False` is not working as expected for `NonlinearCG` HOT 1
- Stochastic L-BFGS algorithm implementation
- Stopping condition 'madsen-nielsen' incorrect
- unit test failures on aarch64 linux with scipy 1.12
- `LevenbergMarquardt` implementation does not accept PyTree parameters
- diag(JTJ) can be more efficient
- JAXOPT Projected Gradient
- "invalid escape sequence" warning in `BoxOSQP` docstring
- Error when taking gradient wrt parameters in BoxOSQP
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