We investigate the nonlinear regression problem under L2 loss (square loss) functions. Traditional nonlinear regression models often result in non-convex optimization problems with respect to the parameter set. We show that a convex nonlinear regression model exists for the traditional least squares problem, which can be a promising towards designing more complex systems with easier to train models.

翻译：我们研究了L2损失（平方损失）函数下的非线性回归问题。传统的非线性回归模型通常在参数集上导致非凸优化问题。我们证明，对于传统最小二乘问题，存在一种凸非线性回归模型，这在设计更易于训练的复杂系统方面具有前景。