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Conference papers

Generalized Abs-Linear Learning by Mixed Binary Quadratic Optimization

Abstract : We consider predictor functions in generalized abs-linear form, which generalize neural nets with hinge activation. To train them with respect to a given data set of feature-label pairs, one has to minimize the average loss, which is a multi-piecewise linear or quadratic function of the weights, i.e. coefficients of the abs-linear form. We suggest to attack this nonsmooth, global optimization problem via successive piecewise linearization, which allows the application of mixed binary convex quadratic optimization codes amongst other methods. These solve the sequence of abs-linear model problems with a proximal term. Preliminary experiments on a simple regression problem verify the validity of the approach but require a large number of Simplex pivots by the solver Gurobi.
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Contributor : Angel Adrian Rojas Jimenez Connect in order to contact the contributor
Submitted on : Monday, September 21, 2020 - 11:54:48 PM
Last modification on : Wednesday, September 22, 2021 - 2:08:01 PM
Long-term archiving on: : Thursday, December 3, 2020 - 3:32:37 PM


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  • HAL Id : hal-02945038, version 1



Andreas Griewank, Angel Adrian Rojas Jimenez. Generalized Abs-Linear Learning by Mixed Binary Quadratic Optimization. Proceedings of CARI 2020, Oct 2020, Thes, Senegal. ⟨hal-02945038⟩



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