SearcharxivSearch

arXiv subjects

Fengqiao Luo

Publications and source records attributed to Fengqiao Luo.

15 recordsLinked to original sources

Proton-to-Alpha branching ratio in the $^{12}$C+$^{12}$C fusion reaction at astrophysical energies

The unique resonance features in the $^{12}$C+$^{12}$C fusion reaction lead to significant fluctuations in the branching ratio $R_{p/\alpha}=\sigma_p/\sigma_\alpha$, making it difficult to determine the $R_{p/\alpha}$ at astrophysical energies. By combining Hauser--Feshbach statistical-model calculations with constraints from direct charged-particle and gamma-ray measurements, we investigate the energy dependence of the averaged $R_{p/\alpha}$ and predict its behavior within the Gamow window. Owing to the strong energy dependence of $R_{p/\alpha}$, the corresponding reaction-rate ratios, $\langle \sigma v \rangle_p / \langle \sigma v \rangle_\alpha$, during core and shell carbon burning are determined to be 0.29, 0.45, and 0.52 at $T_9 = 0.5$, 1.0, and 1.2, respectively, significantly lower than the widely adopted CF88 constant value of 0.79. The implications of the revised $\langle \sigma v \rangle_p / \langle \sigma v \rangle_\alpha$ ratio for stellar nucleosynthesis and white-dwarf evolution are also discussed.

nucl-th

A Cutting-plane and Benders' Decomposition Algorithm for Two-Stage Distributionally Robust Convex programs

We present a finitely convergent cutting-plane algorithm for solving a general mixed-integer convex program given an oracle for solving a general convex program. This method is extended to solve a family of two-stage mixed-integer convex programs using cutting planes, with applications to solving distributionally-robust two-stage stochastic mixed-integer convex programs. Analysis is also given for the case where convex programming oracle provides an $epsilon$-optimal solution. We combine the cut generation with a branch-and-union scheme to develop a more practical algorithm. Computational results on generated test problems show the practicality of our algorithm. Specifically, results show that in the tested problems our algorithm achieves < 5% optimality gap in 12 hours. This gap is >17% with a commercial solver.

math.OC

Structured Nonsmooth Optimization Using Functional Encoding and Branching Information

We develop a novel gradient-based algorithm for optimizing nonsmooth nonconvex functions where nonsmoothness arises from explicit nonsmooth operators in the objective's analytical form. Our key innovation involves encoding active smooth branches of these operators, enabling both branch function extraction at arbitrary points and transition detection through branch tracking. This approach yields a Branch-Information-Driven Gradient Descent (BIGD) method for encodable piecewise-differentiable functions, with an enhanced version achieving local linear convergence under appropriate conditions. The computationally efficient encoding mechanism is straightforward to implement. The power of using branch information has been proved via substantial numerical experiments compared to some existing nonsmooth optimization methods on standard test problems. Most importantly, for piecewise-smooth problems given analytical expressions, implementation of functional encoding can be integrated into a wide range of existing nonsmooth optimization methods to improve the bundle points management, reduce the complexity of the quadratic programming sub-problems, and improve the efficiency of line search.

math.OC

Coordinated Vehicle Platooning on Tree Networks: Efficient Time Discretization and Strengthened Formulation

We consider the coordinated vehicle platooning problem on a tree network with time constraints while the routes of vehicles are given. The problem is to coordinate the departure time of each vehicle to enable platoon formation hence maximizing the total fuel saving. For this problem setting, relative time windows can be defined for all vehicles to which an efficient time discretization can be applied. This property leads to a tight mixed-integer linear program reformulation as compared to the continuous-time formulation involving big-M coefficients proposed in our previous work. It is demonstrated by systematic numerical experiments that the reformulation outperforms the continuous-time formulation for this family of problem instances.

math.OC

An Empirical Quantile Estimation Approach to Nonlinear Optimization Problems with Chance Constraints

We investigate an empirical quantile estimation approach to solve chance-constrained nonlinear optimization problems. Our approach is based on the reformulation of the chance constraint as an equivalent quantile constraint to provide stronger signals on the gradient. In this approach, the value of the quantile function is estimated empirically from samples drawn from the random parameters, and the gradient of the quantile function is estimated via a finite-difference approximation on top of the quantile-function-value estimation. We establish a convergence theory of this approach within the framework of an augmented Lagrangian method for solving general nonlinear constrained optimization problems. The foundation of the convergence analysis is a concentration property of the empirical quantile process, and the analysis is divided based on whether or not the quantile function is differentiable. In contrast to the sampling-and-smoothing approach used in the literature, the method developed in this paper does not involve any smoothing function and hence the quantile-function gradient approximation is easier to implement and there are less accuracy-control parameters to tune. We demonstrate the effectiveness of this approach and compare it with a smoothing method for the quantile-gradient estimation. Numerical investigation shows that the two approaches are competitive for certain problem instances.

math.OC

Service Center Location with Decision Dependent Utilities with an Application to Early Stage Testing and Vaccination in Epidemic Planning

We study a service center location problem with ambiguous utility gains upon receiving service. The model is motivated by the problem of deciding medical clinic/service centers, possibly in rural communities, where residents need to visit the clinics to receive health services. A resident gains his utility based on travel distance, waiting time, and service features of the facility that depend on the clinic location. The elicited location-dependent utilities are assumed to be ambiguously described by an expected value and variance constraint. We show that despite a non-convex nonlinearity, given by a constraint specified by a maximum of two second-order conic functions, the model admits a mixed 0-1 second-order cone (MISOCP) formulation. We study the non-convex substructure of the problem, and present methods for developing its strengthened formulations by using valid tangent inequalities. Computational study shows the effectiveness of solving the strengthened formulations. Examples are used to illustrate the importance of including decision dependent ambiguity. An illustrative example to identify locations for Covid-19 testing and vaccination is used to further illustrate the model and its properties.

math.OC

Polyhedral Analysis of a Polytope from a Service Center Location Problem with a Type of Decision-Dependent Customer Demand

The polytope from a service center location problem with a special location-dependent demand has been investigated in this paper. The location-dependent demand is defined based on a maximum-attraction-principle assumption. Along this direction, the intersection polytope of the capacity constraints and demand constraints yields a novel polyhedral structure. The structure is investigated in this paper via a family of valid and facet-defining inequalities that can reflect the change of demand fulfillment in a neighborhood.

math.OC

A Distributionally-Robust Service Center Location Problem with Decision Dependent Demand Induced from a Maximum Attraction Principle

We establish and analyze a service center location model with a simple but novel decision-dependent demand induced from a maximum attraction principle. The model formulations are investigated in the distributionally-robust optimization framework. A statistical model that is based on the maximum attraction principle for estimating customer demand and utility gain from service is established and analyzed. The numerical experiments show that the model admits high computational efficiency in solving mid-size instances.

math.OC

A Repeated Route-then-Schedule Approach to Coordinated Vehicle Platooning: Algorithms, Valid Inequalities and Computation

Platooning of vehicles is a promising approach for reducing fuel consumption, increasing vehicle safety, and using road space more efficiently. We consider the important but difficult problem of assigning optimal routes and departure schedules to a collection of vehicles. We propose an iterative route-then-schedule heuristic for centralized planning that quickly converges to high-quality solutions. We also propose and analyze a collection of valid inequalities for the individual problems of assigning vehicles to routes and scheduling the times that vehicles traverse their routes. These inequalities are shown to reduce the computational time or optimality gap of solving the routing and scheduling problem instances. Our approach uses the valid inequalities in both the routing and scheduling portions of each iteration; numerical experiments highlight the speed of the approach for routing vehicles on a real-world road network.

math.OC

A Model of Supply-Chain Decisions for Resource Sharing with an Application to Ventilator Allocation to Combat COVID-19

This paper presents a stochastic optimization model for allocating and sharing a critical resource in the case of a pandemic. The demand for different entities peaks at different times, and an initial inventory from a central agency is to be allocated. The entities (states) may share the critical resource with a different state under a risk-averse condition. The model is applied to study the allocation of ventilator inventory in the COVID-19 pandemic by the Federal Emergency Management Agency of the US Department of Homeland Security (FEMA) to different states in the US. Findings suggest that if less than 60% of the ventilator inventory is available for non-COVID-19 patients, FEMA's stockpile of 20,000 ventilators (as of 03/23/2020) would be nearly adequate to meet the projected needs. However, when more than 75% of the available ventilator inventory must be reserved for non-COVID-19 patients, various degrees of shortfall are expected. In an extreme case, where the demand is assumed to be concentrated in the top-most quartile of the forecast confidence interval, the total shortfall over the planning horizon (till 05/31/20) is about 28,500 ventilator days, with a peak shortfall of 2,700 ventilators on 04/12/20. The results also suggest that in the worse-than-average to severe demand scenario cases, NY requires between 7,600-9,200 additional ventilators for COVID-19 patients during its peak demand. However, between 400 to 2,000 of these ventilators can be given to a different state after the peak demand in NY has subsided.

math.OC

A Decomposition Method for Distributionally-Robust Two-stage Stochastic Mixed-integer Cone Programs

We develop a decomposition algorithm for distributionally-robust two-stage stochastic mixed-integer convex cone programs, and its important special case of distributionally-robust two-stage stochastic mixed-integer second order cone programs. This generalizes the algorithm proposed by Sen and Sherali~[Mathematical Programming 106(2): 203-223, 2006]. We show that the proposed algorithm is finitely convergent if the second-stage problems are solved to optimality at incumbent first stage solutions, and solution to an optimization problem to identify worst-case probability distribution is available. The second stage problems can be solved using a branch-and-cut algorithm. The decomposition algorithm is illustrated with an example. Computational results on a stochastic programming generalization of a facility location problem show significant solution time improvements from the proposed approach. Solutions for many models that are intractable for an extensive form formulation become possible. Computational results suggest that solution time requirement does not increase significantly when considering distributional robust counterparts to the stochastic programming models.

math.OC

A Geometric Branch and Bound Method for a Class of Robust Maximization Problems of Convex Functions

We investigate robust optimization problems defined for maximizing convex functions. For finite uncertainty set, we develop a geometric branch-and-bound algorithmic approach to solve this problem. The geometric branch-and-bound algorithm performs sequential piecewise-linear approximations of the convex objective, and solves linear programs to determine lower and upper bounds of nodes specified by the active linear pieces. Finite convergence of the algorithm to an $ε-$optimal solution is proved. Numerical results are used to discuss the performance of the developed algorithm. The algorithm developed in this paper can be used as an oracle in the cutting surface method for solving robust optimization problems with compact ambiguity sets.

math.OC

Distributionally Robust Optimization with Decision Dependent Ambiguity Sets

We study decision dependent distributionally robust optimization models, where the ambiguity sets of probability distributions can depend on the decision variables. These models arise in situations with endogenous uncertainty. The developed framework includes two-stage decision dependent distributionally robust stochastic programming as a special case. Decision dependent generalizations of five types of ambiguity sets are considered. These sets are based on bounds on moments, Wasserstein metric, $ϕ$-divergence and Kolmogorov-Smirnov test. For the finite support case, we use linear, conic or Lagrangian duality to give reformulations of the models with a finite number of constraints. These reformulations allow solutions of such problems using global optimization techniques. Certain reformulations give rise to non-convex semi-infinite programs. Techniques from global optimization and semi-infinite programming can be used to solve these reformulations.

math.OC

A Concentration Result of Estimating Phi-Divergence using Data Dependent Partition

Estimation of the $ϕ$-divergence between two unknown probability distributions using empirical data is a fundamental problem in information theory and statistical learning. We consider a multi-variate generalization of the data dependent partitioning method for estimating divergence between the two unknown distributions. Under the assumption that the distribution satisfies a power law of decay, we provide a convergence rate result for this method on the number of samples and hyper-rectangles required to ensure the estimation error is bounded by a given level with a given probability.

math.PR

Decomposition Algorithm for Distributionally Robust Optimization using Wasserstein Metric

We study distributionally robust optimization (DRO) problems where the ambiguity set is defined using the Wasserstein metric. We show that this class of DRO problems can be reformulated as semi-infinite programs. We give an exchange method to solve the reformulated problem for the general nonlinear model, and a central cutting-surface method for the convex case, assuming that we have a separation oracle. We used a distributionally robust generalization of the logistic regression model to test our algorithm. Numerical experiments on the distributionally robust logistic regression models show that the number of oracle calls are typically 20 ? 50 to achieve 5-digit precision. The solution found by the model is generally better in its ability to predict with a smaller standard error.

math.OC