Hi, I am not sure that this linesearch is implemented in PETSc itself. To implement it, you would need the ability to perform projections onto your feasible region, which is what I am assuming you mean by undershoot or overshoot. Once the projections are involved, however, there are lots of potential problems to consider in the general case. Here I am assuming that x_k is feasible with respect to your feasible region. If not, then there are even more options. For the particular form of line search you have (sufficient decrease conditions) you will want the projection in two places: f(\Pi_C(x_k + \lambda d_k) < f(x_k) + \alpha grad f(x_k)^T (\Pi_C(x_k + \lambda d_k) - x_k) where \Pi_C is the projection of the argument onto the feasible region C, a convex set. The last term makes sure you measure descent against the projected direction, rather than the full direction. The projected direction may not be a descent direction, so you need a fix when \alpha grad f(x_k)^T (\Pi_C(x_k + \lambda d_k) - x_k) >= 0. I would need to dig out some papers for the appropriate fix, which if I recall correctly involves the \min operation. Unless your direction is the steepest descent direction for f(x_k), there is no guarantee that you will satisfy the sufficient decrease condition for any value of \lambda and you would need to impose a finite lower bound on \lambda and have a backup that uses the steepest descent direction. I recall an alternative line search that uses a convex combination of the steepest descent direction and d_k, but would have to find the papers on it. Second order sufficiency (e.g., the equivalent of the strong Wolfe conditions) is probably off the table for now when you have projections. As you can see, there are lots of options (even before adding monotone versus non monotone considerations); the PETSc developers would have to say which ones are relevant and whether or not the projected line searches are separate from the normal line searches. Todd.
On Sep 22, 2017, at 8:22 AM, Lulu Liu <[email protected]> wrote:
Hi,
I am trying to find a lambda by the linesearch such that f([x_{k}+\lambda d_{k}])<f(x_{k})+\alpha\lambda grad f(x_{k})^{T}d_{k} where [x_{k}+lambda d_{k}] means the projection that cuts off the undershoot or overshoot of x_{k}+lambda d_{k}.
How to add the above projection into the linesearch in PETSc?
Thanks!
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