The quadratic bound (QB) principle proposed by Böhning and Lindsay in 1988 is an important special case of the majorization–minimization or minorization-maximization optimization principle. The quadratic upper-bound (QUB) principle is pertinent to minimization; the analogous quadratic lower-bound principle is pertinent to maximization. Unfortunately, in minimizing a loss , the QUB principle is limited by the difficulty of finding a constant positive definite matrix such that is positive semidefinite for all . This paper proposes a generalization of the QB principle that avoids this limitation. In particular, we construct QUB algorithms by replacing the matrix by a continuous matrix-valued function that dominates the Hessian and depends on the both the current iterate and the next potential iterate . In practice, we require to be diagonal with its diagonal entries separated in . In other words, the th diagonal entry of depends on only through its th entry . Theoretical analysis confirms that this class of generalized QB algorithms enjoys global convergence in favorable circumstances. For the scalar case, the tangency condition that the second derivative equals the bound function at the current point promotes superlinear convergence. Several numerical experiments implemented in Julia illustrate the power of the generalized QB principle.
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This content will become publicly available on April 1, 2027
Don’t Follow Reinforcement Learning Blindly: Lower Sample Complexity of Learning Optimal Inventory Control Policies with Fixed Ordering Cost
We study the sample complexity of offline learning for a class of structured Markov decision processes (MDPs) describing the inventory control system with fixed ordering cost/setup cost, a fundamental problem in supply chains. We find that a naive plug-in sampling-based approach applied to the inventory MDPs leads to strictly lower sample complexity bounds compared to the optimal bounds recently obtained for the general MDPs. More specifically, in the infinite-horizon discounted cost setting, we obtain an sample complexity bound, where corresponds to the number of state-action pairs in a generic MDP with state space and action space . As such, improves on the optimal generic reinforcement learning (RL) bound (when directly applying here) by a factor of , and is able to completely remove the dependence on state and action cardinality. In the infinite-horizon average cost setting, we obtain an bound, improving on the generic optimal RL bound (when directly applying here) by a factor of , and hence removing the mixing time dependence. By carefully leveraging the structural properties of the inventory dynamics in various settings, we are able to improve on those “best-possible” bounds developed in the RL literature. Our results demonstrate the drawbacks one could face by blindly following RL algorithms and the necessity of designing sample efficient algorithms that properly incorporate the special structures of the inventory systems.
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- Award ID(s):
- 2312205
- PAR ID:
- 10708761
- Publisher / Repository:
- POMS
- Date Published:
- Journal Name:
- Production and Operations Management
- Volume:
- 35
- Issue:
- 4
- ISSN:
- 1059-1478
- Page Range / eLocation ID:
- 1449 to 1468
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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