Optimization
Fast Approximate Dynamic Programming for Infinite-Horizon Markov Decision Processes
In this study, we consider the infinite-horizon, discounted cost, optimal control of stochastic nonlinear systems with separable cost and constraints in the state and input variables.
A Hierarchical Reinforcement Learning Based Optimization Framework for Large-scale Dynamic Pickup and Delivery Problems Yi Ma
To address this problem, existing methods partition the overall DPDP into fixed-size sub-problems by caching online generated orders and solve each sub-problem, or on this basis to utilize the predicted future orders to optimize each sub-problem further. However, the solution quality and efficiency of these methods are unsatisfactory, especially when the problem scale is very large.
Appendix to: B
Batch evaluation, an important element of modern computing, enables automatic dispatch of independent operations across multiple computational resources (e.g.
Unbalanced Optimal Transport through Non-negative Penalized Linear Regression
This paper addresses the problem of Unbalanced Optimal Transport (UOT) in which the marginal conditions are relaxed (using weighted penalties in lieu of equality) and no additional regularization is enforced on the OT plan.
Unbalanced Optimal Transport through Non-negative Penalized Linear Regression
This paper addresses the problem of Unbalanced Optimal Transport (UOT) in which the marginal conditions are relaxed (using weighted penalties in lieu of equality) and no additional regularization is enforced on the OT plan.
Supplementary Material: Fair Sparse Regression with Clustering: An Invex Relaxation for a Combinatorial Problem A Proof of Lemma 1 Lemma 1 F orpw, Zq PC, the functions f pw, Zq " x M1
We need to prove the following two inequalities. Thus, the inequality ( 19) holds trivially. Note that f p w, Z q " x M In this section, we will show that the MIQP presented in ( 4) is at least as hard to solve as a 0 1 Quadratic Program. It should be noted that MIQP ( 4) is stated for a fixed X. The Mixed Integer Quadratic Program (MIQP) ( 4) is NP-hard. " 0. Other cases will be at least as difficult as this case.
Fair Sparse Regression with Clustering: An Invex Relaxation for a Combinatorial Problem
In this paper, we study the problem of fair sparse regression on a biased dataset where bias depends upon a hidden binary attribute. The presence of a hidden attribute adds an extra layer of complexity to the problem by combining sparse regression and clustering with unknown binary labels. The corresponding optimization problem is combinatorial, but we propose a novel relaxation of it as an invex optimization problem. To the best of our knowledge, this is the first invex relaxation for a combinatorial problem. We show that the inclusion of the debi-asing/fairness constraint in our model has no adverse effect on the performance. Rather, it enables the recovery of the hidden attribute.
POMO: Policy Optimization with Multiple Optima for Reinforcement Learning
Empirically, the low-variance baseline of POMO makes RL training fast and stable, and it is more resistant to local minima compared to previous approaches.