  Multi-objective optimization problems can be found in many real-world
applications, where the objectives often conflict each other and cannot be
optimized by a single solution. In the past few decades, numerous methods have
been proposed to find Pareto solutions that represent optimal trade-offs among
the objectives for a given problem. However, these existing methods could have
high computational complexity or may not have good theoretical properties for
solving a general differentiable multi-objective optimization problem. In this
work, by leveraging the smooth optimization technique, we propose a lightweight
and efficient smooth Tchebycheff scalarization approach for gradient-based
multi-objective optimization. It has good theoretical properties for finding
all Pareto solutions with valid trade-off preferences, while enjoying
significantly lower computational complexity compared to other methods.
Experimental results on various real-world application problems fully
demonstrate the effectiveness of our proposed method.
