Comments (2)
Using jnp.mean
(which corresponds to dividing the loss term by the number of samples) is pretty standard as well. In JAXopt, we tend to use that convention because then the loss can be used on a mini batch and jax.grad
gives a stochastic gradient.
from jaxopt.
Thanks for the explanation! That why you have a len(y_tr)
factor in
from jaxopt.
Related Issues (20)
- drop markdown for notebook examples? HOT 2
- Using `vmap` for root finding with a vector of parameters HOT 8
- LBFGSB produces NaN for certain conditions HOT 1
- Document solver attributes
- Add usage examples for the documentations HOT 4
- LevenbergMarquardt do not seems to work with non-flat input. HOT 1
- Parallel execution of multiple optimization processes HOT 3
- OSQP should inherit from IterativeSolver HOT 1
- Number of gradient evaluations not applicable to scipy's gradient-free optimizers
- Constrained Optimization and Spanning Tree Polytope HOT 4
- ScipyMinimize(method="TNC") is broken HOT 2
- PolyakSGD should take an optional parameter f_star
- Implicitly differentiate the KKT conditions HOT 5
- 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
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from jaxopt.