times. The extended path algorithm is a commonly used and fairly general method for solving dynamic nonlinear models with … rational expectations. This algorithm can be used for a wide range of cases, including for models with occasionally binding … computational improvements to the algorithm that speed up the calculations via vectorisations of the Jacobian matrix and residual …

We propose a new, highly effective and easy-to-implement algorithm for solving large-scale mean-variance optimization … smaller than the number of assets, which is almost always the case. Our novel algorithm is built on the novel representation … of mean-variance models in terms of the support vector data description --- an unsupervised machine learning algorithm …

We propose an actor-critic reinforcement learning (RL) algorithm for the optimal execution problem. We consider the …. We develop a finite-time error analysis of our algorithm and show that it converges linearly under suitable conditions on … the learning rates. We test our algorithm in three different types of market simulators built on the Almgren-Chriss model …

The Expectation-Maximization (EM) algorithm is a very popular optimization tool for mixture problems and in particular … for model-based clustering problems. However, while the algorithm is convenient to implement and numerically very stable …

replies to all probabilistic beliefs over the set. We present an algorithm that computes all minimal sCURB sets in any given … finite game. Runtime measurements on two-player games (where the concepts of CURB and sCURB coincide) show that the algorithm … is considerably faster than the earlier developed algorithm, that of Benisch et al. (2006). -- curb sets ; rational …