-
作者：Eckles, Dean; Esfandiari, Hossein; Mossel, Elchanan; Rahimian, M. Amin
作者单位：Massachusetts Institute of Technology (MIT); Alphabet Inc.; Google Incorporated; Massachusetts Institute of Technology (MIT); Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh
摘要：We study the task of selecting k nodes, in a social network of size n, to seed a diffusion with maximum expected spread size, under the independent cascade model with cascade probability p. Most of the previous work on this problem (known as influence maximization) focuses on efficient algorithms to approximate the optimal seed set with provable guarantees given knowledge of the entire network; however, obtaining full knowledge of the network is often very costly in practice. Here we develop a...
-
作者：Hunter, David Scott; Zaman, Tauhid
作者单位：Massachusetts Institute of Technology (MIT); Yale University
摘要：We consider the problem of optimizing the placement of stubborn agents in a social network in order to maximally influence the population. We assume the network contains stubborn users whose opinions do not change, and nonstubborn users who can be persuaded. We further assume that the opinions in the network are in an equilibrium that is common to many opinion dynamics models, including the well-known DeGroot model. We develop a discrete optimization formulation for the problem of maximally sh...
-
作者：Liu, Hongcheng; Ye, Yinyu; Lee, Hung Yi
作者单位：State University System of Florida; University of Florida; Stanford University
摘要：High-dimensional statistical learning (HDSL) has wide applications in data analysis, operations research, and decision making. Despite the availability of multiple theoretical frameworks, most existing HDSL schemes stipulate the following two conditions: (a) the sparsity and (b) restricted strong convexity (RSC). This paper generalizes both conditions via the use of the folded concave penalty (FCP). More specifically, we consider an M-estimation problem where (i) (conventional) sparsity is rel...
-
作者：Pearce, Michael Arthur Leopold; Poloczek, Matthias; Branke, Juergen
作者单位：University of Warwick; Amazon.com; University of Warwick
摘要：Bayesian optimization is a powerful tool for expensive stochastic black-box optimization problems, such as simulation-based optimization or machine learning hyperparameter tuning. Many stochastic objective functions implicitly require a random number seed as input. By explicitly reusing a seed, a user can exploit common random numbers, comparing two or more inputs under the same randomly generated scenario, such as a common customer stream in a job shop problem or the same random partition of ...
-
作者：Zhu, Nan; Bauer, Daniel
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison
摘要：This paper presents and applies models for the valuation and management of mortality-contingent exposures. Such exposures include insurance and pension benefits, as well as novel mortality-linked securities traded in financial markets. Unlike conventional approaches to modeling mortality, we consider the stochastic evolution of mortality projections rather than realized mortality rates. Relying on a time series of age-specific mortality forecasts, we develop a set of stochastic models that-unl...
-
作者：Clausen, Jens Vinther; Lusby, Richard; Ropke, Stefan
作者单位：Technical University of Denmark
摘要：This paper introduces a family of valid inequalities, which we term consistency cuts, to be applied to a Dantzig-Wolfe reformulation (or decomposition) with linking variables. We prove that these cuts ensure an integer solution to the corresponding DantzigWolfe relaxation when certain criteria to the structure of the decomposition are met. We implement the cuts and use them to solve a commonly used test set of 200 instances of the temporal knapsack problem. We assess the performance with and w...
-
作者：Bertsimas, Dimitris; Cory-Wright, Ryan; Pauphilet, Jean
作者单位：Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); University of London; London Business School
摘要：We propose a framework for modeling and solving low-rank optimization problems to certifiable optimality. We introduce symmetric projection matrices that satisfy Y-2 = Y, the matrix analog of binary variables that satisfy z(2) = z, to model rank constraints. By leveraging regularization and strong duality, we prove that this modeling paradigm yields convex optimization problems over the nonconvex set of orthogonal projection matrices. Furthermore, we design outer-approximation algorithms to so...
-
作者：Desir, Antoine; Goyal, Vineet; Zhang, Jiawei
作者单位：INSEAD Business School; Columbia University; New York University
摘要：Assortment optimization is an important problem that arises in many practical applications such as retailing and online advertising. In this problem, the goal is to select a subset of items that maximizes the expected revenue in the presence of (1) the substitution behavior of consumers specified by a choice model, and (2) a potential capacity constraint bounding the total weight of items in the assortment. The latter is a natural constraint arising in many applications. We begin by showing ho...
-
作者：Liu, Yunan; Sun, Xu; Hovey, Kyle
作者单位：North Carolina State University; State University System of Florida; University of Florida; United States Department of Defense
摘要：Motivated by large-scale service systems, we study a multiclass queueing system having class-dependent service rates and heterogeneous abandonment distributions. Our objective is to devise proper staffing and scheduling schemes to achieve differentiated services for each class. Formally, for a class-specific delay target w(i) > 0 and threshold alpha(i) is an element of (0,1), we concurrently determine an appropriate staffing level (number of servers) and a server-assignment rule (assigning new...
-
作者：Ashlagi, Itai; Krishnaswamy, Anilesh K.; Makhijani, Rahul; Saban, Daniela; Shiragur, Kirankumar
作者单位：Stanford University; Duke University; Facebook Inc; Stanford University
摘要：Two-sided matching platforms provide users with menus of match recommendations. To maximize the number of realized matches between the two sides (referred to herein as customers and suppliers), the platform must balance the inherent tension between recommending more suppliers to customers to increase the chances that they like one of them and avoiding the conflicts that arise when customers, who are given more options, end up choosing the same suppliers. We introduce a stylized model to study ...
