Optimizing Multinomial Logit Profit Functions
多项Logit模型常用于产品线问题中计算购买概率，但利润函数通常非凹，标准搜索易陷入局部最优。本文提出一种简单方法，通过从相关凹函数的最优解“路径”追踪到真实最优解，来缓解该问题。
The multinomial logit model is a standard approach for determining the probability of purchase in product line problems. When the purchase probabilities are multiplied by product contribution margins, the resulting profit function is generally nonconcave. Because of this, standard nonlinear search procedures may terminate at a local optimum which is far from the global optimum. We present a simple procedure designed to alleviate this problem. The key idea of this procedure is to find a “path” of prices from the global optimum of a related, but concave logit profit function, to the global optimum of the true (but nonconcave) logit profit function.