3. A feasible directions method for nonconvex optimization over linear constraints with a nonsmooth concave regularizer
Invited abstract in session TD-5: Nonsmooth optimization algorithms, stream Nonsmooth and nonconvex optimization algorithms.
Thursday, 14:10 - 15:50Room: M:N
Authors (first author is the speaker)
| 1. | Nadav Hallak
|
| The Technion | |
| 2. | Amir Beck
|
| School of Mathematical Sciences, Tel-Aviv University |
Abstract
This talk presents a feasible directions approach for the minimization of a continuous function over linear constraints in which the update directions belong to a predetermined finite set spanning the feasible set.
These directions are recurrently investigated in a cyclic semi-random order, where the stepsize of the update is determined via univariate optimization. The method achieves that any accumulation point is a stationary point, and enjoys a sublinear rate of convergence in expectation w.r.t a new optimality measure that acts as a proxy for the stationarity condition.
Keywords
- Linear and nonlinear optimization
- SS - Advances in Nonlinear Optimization and Applications
- Derivative-free optimization
Status: accepted
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