A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper proposes efficient AL algorithms for optimizing product performance under environmental variability.
problem Optimizing product performance under varying environmental conditions.
method Formulated as Bayesian Quadrature Optimization problems for probabilistic threshold robustness measure using Gaussian Process model.
result Proposed algorithms provide credible intervals for probabilistic threshold robustness measure and demonstrate efficiency in real-world applications.
Deep sum-product networks learn faster than shallow models.
problem The speed of parameter optimization in sum-product networks.
method Theoretical analysis and empirical experiments on overparameterized sum-product networks.
result Gradient-based optimization in deep sum-product networks is equivalent to gradient ascent with adaptive and time-varying learning rates and additional momentum terms.
Study relationships between intrinsic and extrinsic invariants of Riemannian almost k-product manifolds.
problem Find a relationship between intrinsic and extrinsic invariants of Riemannian almost k-product manifolds isometrically immersed in another Riemannian manifold.
method Establish an optimal inequality involving mixed scalar curvature and square of mean curvature.
result Optimal inequality that includes mixed scalar curvature and square of mean curvature.
We use a control framework to analyze the digital vendor's profit maximization problem. The vendor captures market share by focusing costly effort on post-launch product maintenance, which influences user perception of the product and drives a revenue stream associated with product use. Our theoretical results show nec…
The study optimizes supply chain management through a dice-based model to predict cleaner production.
problem Uncertainty in supply chain management and economic predictions.
method A 4-component SC module (environmental, demand, economic, social uncertainties) ranked by weight, using Analytical Hierarchical Process and optimization of a weighted cost function.
result Identifies conditions validating the sustainability of a business venture and optimizes market uncertainty.
This paper addresses a novel data science problem, prescriptive price optimization, which derives the optimal price strategy to maximize future profit/revenue on the basis of massive predictive formulas produced by machine learning. The prescriptive price optimization first builds sales forecast formulas of multiple pr…
In this article, we analyze the application of options contract in special commodity supply chain such as fresh agricultural products. This problem is discussed in the point of the retailer. When spot market and future market are both available, we discuss how the retailer chooses the optimal production. Furthermore, o…
The paper proposes a method to test properties of the optimal assortment in multinomial logit models.
problem Uncertainty quantification for the optimal assortment in multinomial logit models.
method The paper proposes a novel inferential framework to test properties of the optimal assortment in multinomial logit models, reducing the problem to detecting the sign change point of marginal revenue gaps.
result The asymptotic normality of the marginal revenue gap estimator and the construction of a maximum statistic to detect the sign change point.
Paper proposes a deep learning method to forecast hydrogen consumption and optimize electrolyzer scheduling for profit maximization.
problem Optimizing electrolyzer scheduling in a dynamic power market with accurate hydrogen consumption forecasting.
method Deep learning approach for forecasting hydrogen consumption of fuel cell vehicles. Minimizing production cost by adjusting production hours based on forecasted consumption.
result Optimal electrolyzer scheduling leads to profit maximization by reducing high-cost production hours and storing sufficient hydrogen during low-cost hours.
In this paper, we initiate the study of $\p R$-warped products in para-Kähler manifolds and prove some fundamental results on such submanifolds. In particular, we establish a general optimal inequality for $\p R$-warped products in para-Kähler manifolds involving only the warping function and the second fundamental for…