Days: Sunday, March 15th Monday, March 16th Tuesday, March 17th
View this program: with abstractssession overviewtalk overview
08:30 | Advances in Scalable Open Source Software for Parallel Stochastic Programming (abstract) |
08:52 | Biased Stochastic Approximation for Conditional Stochastic Optimization (abstract) |
09:14 | Benders Cut Classification via Support Vector Machines for Solving Two-stage Stochastic Programs (abstract) |
08:30 | Dynamic Tuberculosis Screening for Healthcare Employees (abstract) |
08:52 | Equity in Genetic Newborn Screening (abstract) |
09:14 | Mean-Risk Two-Stage Vaccine Allocation Under Uncertainty (abstract) |
08:30 | Complexity of stochastic dual dynamic programming (abstract) |
08:52 | Learning to optimize with hidden constraints (abstract) |
09:14 | Approximation Algorithms for the Maximum Entropy Sampling Problem (abstract) |
08:30 | On Sufficient and Necessary Conditions for Rank-One Generated Cones (abstract) |
08:52 | Weak infeasibility in semidefinite programming: a complete characterization and generating all instances (abstract) |
09:14 | A Decomposition Interior-Point Algorithm for Stochastic Linear Optimization in a Hilbert Space (abstract) |
08:30 | Approximating L1-Norm Best-Fit Lines and Extensions (abstract) |
08:52 | Engineering fast multilevel support vector machines (abstract) |
09:14 | Partition Hypergraphs with Embeddings (abstract) |
08:30 | Optimizing Revenue while Showing Relevant Recommendations (abstract) |
08:52 | Maximizing the total pleasure from a personalized day tour (abstract) |
09:14 | Solving Multi-Objective Combinatorial Cell Formation Problem: A Two-Step Approach Using A Distance Function and A Genetic Algorithm (abstract) |
08:30 | Distributed Algorithms for Multi-Scale Capacity Expansion Problems in Power Systems (abstract) |
08:52 | Integrated Learning and Optimization for AC-OPF (abstract) |
09:14 | Path-based Valid Inequalities for DC Power Systems with Discrete Transmission Considerations (abstract) |
10:30 | Subtree Decomposition Method For Multistage Stochastic Programs (abstract) |
10:52 | An Algorithm for Parametric Quadratically Constrained Quadratic Programs (abstract) |
11:14 | Sample Complexity of Sample Average Approximation for Conditional Stochastic Optimization (abstract) |
11:36 | Comparing model-free and model-based methods for multistage stochastic programming (abstract) |
10:30 | Variance Reduction in Stochastic Programming and Applications in Logistics (abstract) |
10:52 | Robust Optimization with Decision-Dependent Information Discovery (abstract) |
11:14 | Joint chance-constrained programs and the intersection of mixing sets through a submodularity lens (abstract) |
11:36 | Sample Average Approximation for Stochastic Nonconvex Mixed Integer Nonlinear Programming via Outer Approximation (abstract) |
10:30 | Robust Assortment Optimization under Product Unavailability (abstract) |
10:52 | Precision Heparin Dosing using Mixed Integer Optimization (abstract) |
11:14 | Strong formulations for sparse regression (abstract) |
11:36 | A mixed-integer optimization approach for exhaustive cross-validated model selection (abstract) |
10:30 | On the tightness of SDP relaxations of QCQPs (abstract) |
10:52 | Sparse PCA under a conic optimization lens: MISDP reformulations and scalable SOCP bounds (abstract) |
11:14 | On Local Minima of Cubic Polynomials (abstract) |
10:30 | Bridging Convex and Nonconvex Optimization in Robust PCA: Noise, Outliers, and Missing Data (abstract) |
10:52 | Learning Dynamical Systems with Side Information (abstract) |
11:14 | Composite optimization for blind deconvolution (abstract) |
11:36 | Structured local minima in some nonconvex optimization problems (abstract) |
10:30 | Data driven stochastic optimization applied to renewable energy penetration in power systems (abstract) |
10:52 | Exploiting almost identical generators in unit commitment (abstract) |
11:14 | Spatio-temporal forecasting for wind energy and its impact on wind farm operations (abstract) |
10:30 | Understanding Limitation of Two Symmetrized Orders by Worst-case Complexity (abstract) |
10:52 | Surrogate Optimization of Deep Neural Networks for Groundwater Predictions (abstract) |
11:14 | New Bregman proximal type algorithms for solving DC optimization problems (abstract) |
11:36 | New Multi-Step Conjugate Gradient Method for Optimization (abstract) |
Abstract:
The boundaries between machine learning and optimization are continuously blurring as machine learning can be used to approximate optimization problems and optimization can expand the scope and applicability of learning systems. Moreover, the tight integration of machine learning and optimization opens new possibilities for both fields. This talk reviews some interesting developments in this space through applications in power systems and mobility.
Bio:
Pascal Van Hentenryck is the A. Russell Chandler III Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology, the Associate Chair for Innovation and Entrepreneurship, and the director of the Socially Aware Mobility lab. Van Hentenryck is an INFORMS Fellow and a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), and the recipient of two honorary doctoral degrees. Several of his optimization systems, including the CHIP and OPL systems, have been in commercial use for more than 20 years. His current research focuses on machine learning, optimization, and privacy with applications in mobility, energy, and resilience.
15:15 | Self-guided Approximate Linear Programs (abstract) |
15:37 | A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints (abstract) |
15:59 | Adaptive Approximate Linear Programs based on Index Networks for Solving Weakly-coupled Dynamic Programs (abstract) |
16:21 | Lookahead-bounded Q-learning (abstract) |
15:15 | Dualization and Bilevel optimization in JuMP (abstract) |
15:37 | Disjunctive cuts for Mixed-Integer Conic Optimization (abstract) |
15:59 | Hypatia: Generic Nonsymmetric Conic Optimization in Julia (abstract) |
16:21 | Hypatia: formulations, linear algebra, and preliminary computational results (abstract) |
15:15 | In memory of Dave Shanno: Revisiting Quasi-Newton Methods (abstract) |
15:37 | In Memory of Dave Shanno: Objective Bounds from ADMM Algorithms (abstract) |
15:59 | Solving the Problem of Portfolio VAR Minimization as a Nonlinear Program (abstract) |
16:21 | In memory of Dave Shanno: from optimization to machine learning, it’s all the same data (abstract) |
15:15 | First-order methods for large-scale linear programming (abstract) |
15:37 | Strong mixed-integer programming formulations for trained neural networks (abstract) |
15:59 | Risk Guarantees For End-to-end Prediction and Optimization Processes (abstract) |
15:15 | Exploring Algorithmic Fairness in Robust Graph Covering Problems (abstract) |
15:37 | Optimization Under Uncertainty in Kidney Exchange (abstract) |
15:59 | Robust two echelon vehicle and drone routing for humanitarian relief operations (abstract) |
15:15 | Optimization Algorithms for the Bin Packing Problem with Minimum Color Fragmentation (abstract) |
15:37 | Data-Driven Optimization for Team Formation (abstract) |
15:59 | On parametric decision diagrams (abstract) |
16:21 | Last mile scheduling under uncertainty (abstract) |
15:15 | Scenario grouping and decomposition algorithms for chance-constrained programs (abstract) |
15:37 | Exploiting Multiple Optimal Solutions in Independent Set (abstract) |
15:59 | Learning-Enhanced Integer Programming: A Hybrid Approach for Routinely Solved Discrete Optimization (abstract) |
16:21 | Cardinality Constrained Multilinear Sets (abstract) |
17:00 | Stochastic Dynamic Linear Programming and its Application (abstract) |
17:22 | Adaptive Partition-based Stochastic Dual Dynamic Programming Algorithms For Multistage Stochastic Linear Programs (abstract) |
17:44 | A Data-Driven Approach for a Class of Stochastic Dynamic Optimization Problems (abstract) |
18:06 | Distributionally Robust Stochastic Dual Dynamic Programming (abstract) |
17:00 | Spectral relaxations and branching for MIQPs (abstract) |
17:22 | Aspects of maximum-entropy sampling (abstract) |
17:44 | Volumetric Analysis of Perspective Reformulations (abstract) |
18:06 | Decomposing Optimization-Based Bounds Tightening Problems Via Graph Partitioning (abstract) |
17:00 | Remembering Dave Shanno: Twenty Years of Collaboration (abstract) |
17:22 | In Memory of Dave Shanno: the History of OB1 (abstract) |
17:44 | In Memory of Dave Shanno: making an interior-point method for NLP converge (abstract) |
18:06 | Reflections on Dave Shanno (abstract) |
17:00 | Radial Duality (abstract) |
17:22 | Coordinate Descent Without Coordinates: Tangent Subspace Descent on Riemannian Manifolds (abstract) |
17:44 | Complexity of proximal augmented Lagrangian for nonconvex optimization with nonlinear equality constraints (abstract) |
18:06 | A Dual Approach for Optimal Algorithms in Distributed Optimization over Networks (abstract) |
17:00 | Integer Programming Methods to Alleviate Human Trafficking in Maritime Settings (abstract) |
17:22 | Impact of Bias on Hiring Decisions (abstract) |
17:44 | Optimal Active Preference Elicitation via Adjustable Robust Optimization (abstract) |
17:00 | On Polyhedra of Two Influence Maximization Problems in Social Networks (abstract) |
17:22 | A Branch-and-Cut Approach For Simple Graph Partitioning on Sparse Graphs (abstract) |
17:44 | Centralities for Networks with Consumable Resources (abstract) |
17:00 | Achieving Consistency with Cutting Planes (abstract) |
17:22 | Network Models for Multiobjective Discrete Optimization (abstract) |
17:44 | A stochastic mixed integer linear program for agricultural land use optimization (abstract) |
View this program: with abstractssession overviewtalk overview
08:30 | Stochastic DC Optimal Power Flow With Reserve Saturation (abstract) |
08:52 | A Reserve Scheduling Framework for Uncertainty Management in Power Systems and Gas Pipelines (abstract) |
09:14 | Distributionally Robust Transmission Grid under Geomagnetic Disturbances (abstract) |
09:36 | Optimal Power Flow in Distribution Networks under Stochastic N-1 Disruptions (abstract) |
08:30 | A Sequential Sampling Method for Distributionally Robust Linear Programs (abstract) |
08:52 | (Partial) Convex Hull for Deterministic and Stochastic Structured Conic Mixed Integer Sets using Conic Mixed Integer Rounding (abstract) |
09:14 | Using Effective Scenarios to Accelerate Decomposition Algorithms for Two-stage Distributionally Robust Optimization with Total Variation Distance (abstract) |
08:30 | Exact Algorithms for Lot-Sizing Problems with Multiple Capacities, Piecewise Concave Production Costs, and Subcontracting (abstract) |
08:52 | A Heuristic and Exact Solution Approach for the 0-1 Cubic Knapsack Problem (abstract) |
09:14 | A Unified Approach to Mixed-Integer Optimization: Nonlinear Formulations and Scalable Algorithms (abstract) |
09:36 | The Competitive Ratio of Threshold Policies for Online Unit-density Knapsack Problems (abstract) |
08:30 | Template-based Minor Embedding for Adiabatic Quantum Optimization (abstract) |
08:52 | Integer programming techniques for minor-embedding in quantum annealers (abstract) |
09:14 | The Potential of Quantum Annealing for Rapid Solution Structure Identification (abstract) |
09:36 | Multistart Methods for Quantum Approximate Optimization (abstract) |
08:30 | Data-driven optimization for wind farm siting (abstract) |
08:52 | Reinforcement Learning under Drift: The Blessing of (More) Optimism (abstract) |
09:14 | JANOS : An Integrated Predictive and Prescriptive Modeling Framework (abstract) |
09:36 | Data-driven sample average approximation with covariate information (abstract) |
08:30 | Online Primal-Dual Mirror Descent under Stochastic Constraints (abstract) |
08:52 | Zeroth-order Nonconvex Stochastic Optimization: Handling Constraints, High-Dimensionality, and Saddle-Points (abstract) |
09:14 | Level-Set Method for Convex Constrained Optimization with Error Bound Conditions (abstract) |
09:36 | Powerful Operator Splitting with Projective Splitting (abstract) |
08:30 | Improving Operating Room Scheduling: Integer and Constraint Programming Approaches (abstract) |
08:52 | Multilevel algorithms for geographical districting problems (abstract) |
09:14 | Bi-Objective Firefly Optimization Algorithm for n-Stage Parallel Machine Scheduling (abstract) |
Abstract:
During November 2018 and October 2019, the Advanced Research Projects Agency-Energy (ARPA-E) conducted a high-profile competition for the robust optimization of large-scale electrical power grids. 26 teams from academia and industry developed tailored optimization algorithms and high-performance software to compete for a total of $4,000,000 in prize money. The competition provided a unique opportunity for researchers to explore a challenging and important industrial application with realistic data sets and high-performance hardware. The Security-Constrained Optimal Power Flow (SCOPF) problem formulated by the organizers optimizes the power production of generators in large-scale electricity transmission networks. Several characteristics make the solution of this problem very challenging: (i) The power generation must be allocated in a way so that a simple recourse rule is sufficient to maintain feasibility under thousands of N-1 contingencies in which one element of the grid fails; (ii) in contrast to linear models currently used in industry, the formulation involves the nonlinear nonconvex alternating current (AC) power flow equations; (iii) discrete switches formulated as complementarity constraints further add to the nonconvexity of the model; and (iv) with up to 30,000 buses, the test instances are very large. In this talk, we describe the solution approach developed by the GO-SNIP team which scored second in the overall competition.
Bio:
Andreas Wächter is a Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University. He obtained his master's degree in Mathematics at the University of Cologne, Germany, in 1997, and his Ph.D. in Chemical Engineering at Carnegie Mellon University in 2002. Before joining Northwestern University in 2011, he was a Research Staff Member in the Department of Mathematical Sciences at IBM Research in Yorktown Heights, NY. His research interests include the design, analysis, implementation and application of numerical algorithms for nonlinear continuous and mixed-integer optimization. He is a recipient of the 2011 Wilkinson Prize for Numerical Software and the 2009 Informs Computing Society Prize for his work on the open-source optimization package Ipopt.
13:15 | Online Learning in Service Systems: Optimal Pricing And Capacity Sizing For A G/G/1 Queue With Demand Learning (abstract) |
13:37 | Tractable Reformulations of Distributionally Robust Two-stage Stochastic Programs with $\infty-$Wasserstein Distance (abstract) |
13:59 | Learning Interpretable Policies for Markov Decision Process by Hybrid Linear-Black-Box Model (abstract) |
14:21 | Data-Driven Distributionally Robust Appointment Scheduling (abstract) |
13:15 | Nonlinear Regularization for Neural Networks (abstract) |
13:37 | The Role of Optimization Algorithms in Generalization of Deep Learning (abstract) |
13:59 | Convergent variants of gradient descent for neural networks (abstract) |
14:21 | On the Benefit of Width for Neural Networks: Disappearance of Basins (abstract) |
13:15 | A Combinatorial Cut-and-Lift Procedure with an Application to 0-1 Chance Constraints (abstract) |
13:37 | Ranking-based Facet Defining Inequalities for the Weak Order Polytope (abstract) |
13:59 | On the convexification of constrained quadratic optimization problems with indicator variables (abstract) |
14:21 | Subadditive Duality for Conic Mixed-Integer Programs (abstract) |
13:15 | Lossless Compression of Deep Neural Networks (abstract) |
13:37 | Boolean Decision Rules via Column Generation (abstract) |
13:59 | Outlier detection in time series via mixed-integer conic quadratic optimization (abstract) |
13:15 | Exploiting symmetry in linear programming (abstract) |
13:37 | The Star Degree Centrality Problem: A Benders Decomposition Approach (abstract) |
13:59 | An Integer Column Generation Procedure for Coordinating Product Transitions in Semiconductor Manufacturing using Combinatorial Auctions (abstract) |
14:21 | The RIP Technique for Box Configuration for Birchbox (abstract) |
13:15 | Extreme-point Tabu Search Heuristic for Fixed-Charge Generalized Transshipment Problems (abstract) |
13:37 | Minimum blockers for the set covering problem (abstract) |
13:59 | A Stochastic Programming Model with Endogenous and Exogenous Uncertainty for Reliable Network Design Under Random Disruption (abstract) |
13:15 | Solving risk-averse mixed-integer stochastic programs using convex approximations (abstract) |
13:37 | Stochastic Team Formation Problem (abstract) |
13:59 | Conic Formulations for a Mean-Risk Stochastic Mixed Integer Nonlinear Program with Applications to Network Protection (abstract) |
Abstract:
While the trend in machine learning has tended towards more complex hypothesis spaces, it is not clear that this extra complexity is always necessary or helpful for many domains. In particular, models and their predictions are often made easier to understand by adding interpretability constraints. These constraints shrink the hypothesis space; that is, they make the model simpler. Statistical learning theory suggests that generalization may be improved as a result as well. However, adding extra constraints can make optimization (exponentially) harder. For instance it is much easier in practice to create an accurate neural network than an accurate and sparse decision tree. We address the following question: Can we show that a simple-but-accurate machine learning model might exist for our problem, before actually finding it? If the answer is promising, it would then be worthwhile to solve the harder constrained optimization problem to find such a model. In this talk, I present an easy calculation to check for the possibility of a simpler model. This calculation indicates that simpler-but-accurate models do exist in practice more often than you might think. Time permitting, I will briefly overview several new methods for interpretable machine learning. These methods are for (i) sparse optimal decision trees, (ii) sparse linear integer models (also called medical scoring systems), and (iii) interpretable case-based reasoning in deep neural networks for computer vision.
This is joint work with Lesia Semenova, Ron Parr, Xiyang Hu, Chudi Zhong, Jimmy Lin, Margo Seltzer, Chaofan Chen, Oscar Li, Alina Barnett, Daniel Tao, Jonathan Su, and Berk Ustun.
Bio:
Cynthia Rudin is a professor of computer science, electrical and computer engineering, and statistical science at Duke University, and directs the Prediction Analysis Lab, whose main focus is in interpretable machine learning. She is also an associate director of the Statistical and Applied Mathematical Sciences Institute (SAMSI). Previously, Prof. Rudin held positions at MIT, Columbia, and NYU. She holds an undergraduate degree from the University at Buffalo, and a PhD from Princeton University. She is a three time winner of the INFORMS Innovative Applications in Analytics Award, was named as one of the “Top 40 Under 40” by Poets and Quants in 2015, and was named by Businessinsider.com as one of the 12 most impressive professors at MIT in 2015. She is past chair of both the INFORMS Data Mining Section and the Statistical Learning and Data Science section of the American Statistical Association. She has also served on committees for DARPA, the National Institute of Justice, and AAAI. She has served on three committees for the National Academies of Sciences, Engineering and Medicine, including the Committee on Applied and Theoretical Statistics, the Committee on Law and Justice, and the Committee on Analytic Research Foundations for the Next-Generation Electric Grid. She is a fellow of the American Statistical Association and a fellow of the Institute of Mathematical Statistics. She gave a Thomas Langford Lecturer at Duke University during the 2019-2020 academic year, and will be the Terng Lecturer at the Institute for Advanced Study in 2020.
16:30 | Machine Learning to Address Hospital Capacity: Predicting Discharges and Identifying Barriers to Discharge (abstract) |
16:52 | Adaptive Pharmaceutical Supply Chain Design under Disruption and its Effects on Drug Shortages (abstract) |
17:14 | Data-driven optimization of supplemental screening usage in breast cancer detection (abstract) |
17:36 | A distributionally robust optimization approach for outpatient colonoscopy scheduling (abstract) |
16:30 | Tutorial: A Unified Framework for Sequential Decisions under Uncertainty (abstract) |
16:30 | Inverse Mixed-Integer Optimization with Trust-Region Methods (abstract) |
16:52 | Dynamic Resource Allocation in the Cloud with Near-Optimal Efficiency (abstract) |
17:14 | Neural network approximations in quadratic optimization (abstract) |
17:36 | Characterizing Linearizable QAPs by the Level-1 RLT (abstract) |
16:30 | An MINLP approach to bounding probability with applications to Network routing (abstract) |
16:52 | On SDP formulations for quadratic optimization with indicator variables (abstract) |
17:14 | Computing the Projection onto the Degenerate Doubly Nonnegative Cone (abstract) |
16:30 | Electrical Flows over Spanning Trees (abstract) |
16:52 | Categorical Feature Compression via Submodular Optimization (abstract) |
17:14 | Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence (abstract) |
17:36 | Sparse Regression with Interactions: A Scalable Integer Programming Approach (abstract) |
16:30 | A Progressive approximation approach for the exact solution of sparse large-scale binary interdiction games (abstract) |
16:52 | Estimating Wireless Mesh Network Vulnerability: A Search for the “Best” Interference Model (abstract) |
17:14 | Network Interdiction with Asymmetric Cost Uncertainty (abstract) |
16:30 | Interval Linear Programming with an Additional Function of Interest: Computational Complexity, Reformulations, and Heuristics (abstract) |
16:52 | The Outcome Range Problem in Interval Linear Programming (abstract) |
17:14 | Control and tolerance two-sided interval linear programming problems (abstract) |
View this program: with abstractssession overviewtalk overview
08:00 | Partially observable multistage stochastic programming (abstract) |
08:22 | Inverse Markov decision process with unknown transition probabilities (abstract) |
08:44 | Personalized Dose Finding Trials (abstract) |
08:00 | Optimal Crashing of an Activity Network with Multiple Disruptions (abstract) |
08:22 | Bi-objective multistage stochastic linear programming (abstract) |
08:44 | Coupled Learning Enabled Stochastic Programming with Endogenous Uncertainty (abstract) |
08:00 | Developing an Efficient Exact Solution Approach for Scheduling Problems (abstract) |
08:22 | A Study on the Block Relocation Problem: Lower Bound Derivations and Strong Formulations (abstract) |
08:44 | A Model of the Online Independent Set Problem (abstract) |
08:00 | Causal Inference with Selectively-Deconfounded Data (abstract) |
08:22 | Optimal Decision Tree with Noisy Outcomes (abstract) |
08:44 | Meta Dynamic Pricing: Learning Across Experiments (abstract) |
09:06 | Safely Learning to Personalize from Sequential Observational Data (abstract) |
08:00 | Nonsmooth Optimization over Stiefel Manifold: Riemannian Subgradient Methods (abstract) |
08:30 | Near-optimal bounds for phase synchronization (abstract) |
08:52 | Provable Generalizability of Deep Neural Networks under Sparsity-Inducing Regularization (abstract) |
09:14 | Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution (abstract) |
08:00 | A multi-vehicle covering tour problem with speed optimization (abstract) |
08:22 | Network Alignment by Propagating Reliable Similarities (abstract) |
08:44 | A Team Orienteering Problem for Fixed-Wing Drones (abstract) |
08:00 | A bilevel programming model for coordinating product transitions (abstract) |
08:22 | Incentivizing Homeowners for Tree Management using a Principal-Agent Game-Theoretical Approach (abstract) |
08:44 | Global Supply Chain Networks and Tariff Rate Quotas: Equilibrium Analysis with Application to Agricultural Products (abstract) |
Abstract:
While the machine learning world is dominated by unconstrained optimization models and methods, the operation research world often (or even mostly) considers constrained optimization models and methods. In this talk we consider the potential impact of computational methods and software tools for constrained optimization in machine learning through two specific applications. The first application considers the use of mixed integer programming to verify robustness of trained deep neural network. The second application considers the use of advanced interior point solvers for shape constrained regression.
Bio:
Juan Pablo Vielma is the Richard S. Leghorn (1939) Career Development Associate Professor at MIT Sloan School of Management. Dr. Vielma has a B.S. in Mathematical Engineering from University of Chile and a Ph.D. in Industrial Engineering from the Georgia Institute of Technology. His current research interests include the theory and practice of mixed-integer programming and applications in energy, natural resource management, marketing and statistics. He has received the Presidential Early Career Award for Scientists and Engineers (PECASE), the NSF CAREER Award, the INFORMS Computing Society Prize and the INFORMS Optimization Society Student Paper Prize. Dr. Vielma has served as chair of the INFORMS Section on Energy, Natural Resources, and the Environment and as vice-chair for Integer and Discrete Optimization of the INFORMS Optimization Society. He is currently an associate editor for Operations Research and Operations Research Letters, a member of the board of directors of the INFORMS Computing Society, and a member of the NumFocus steering committee for JuMP.
11:00 | Data-Driven Two-Stage Conic Optimization with Rare High-Impact Zero-One Uncertainties (abstract) |
11:22 | Data-Driven Robust MDPs Using Distance-Based Ambiguity Sets (abstract) |
11:44 | Robust Optimization with Order Statistic Uncertainty Set (abstract) |
11:00 | Nurse Staffing under Absenteeism: A Distributionally Robust Optimization Approach (abstract) |
11:22 | A Distributionally Robust Optimization Approach for Stochastic Elective Surgery Scheduling with Limited ICU Capacity (abstract) |
11:44 | On the Values of Vehicle-to-Grid Electricity Selling in Electric Vehicle Sharing (abstract) |
11:00 | Optimal location of last-mile consolidation areas to reduce environmental impact of urban deliveries of goods: a heuristic approach (abstract) |
11:22 | University timetabling problem with requested instructor workload using constraint program (abstract) |
11:44 | On Modeling Local Search with Special-Purpose Combinatorial Optimization Hardware (abstract) |
11:00 | Scaling Up Quasi-Newton Algorithms: Communication Efficient Distributed SR1 (abstract) |
11:22 | A Line-Search Descent Algorithm for Strict Saddle Functions with Complexity Guarantees (abstract) |
11:44 | Acceleration of Primal-Dual Methods by Preconditioning and Simple Subproblem Procedures (abstract) |
12:06 | Decentralized Computation of Effective Resistances for Distributed Learning (abstract) |
11:00 | An $O(s^r)$-Resolution ODE Framework for Discrete-Time Optimization Algorithms and Applications to Convex-Concave Saddle-Point Problems (abstract) |
11:22 | Learning Positive-Valued Functions with Pseudo Mirror Descent (abstract) |
11:44 | Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives (abstract) |
11:00 | Wasserstein Distributionally Robust Inverse Multiobjective Optimization (abstract) |
11:22 | Katyusha Acceleration for Convex Finite-Sum Compositional Optimization (abstract) |
11:44 | Near-Optimal Algorithms for Smooth Convex-Concave Minimax Optimization and Beyond (abstract) |
12:06 | Sliding Linearized ADMM For Convex Smooth Optimization With Affine Constraints (abstract) |
11:00 | M-natural-convexity and its applications in operations (abstract) |
11:22 | Earned Value Measurement in Project Buffers Management (abstract) |
11:44 | Developing a Mixed Integer Linear Program (MILP) to maximize material utilization and switch the setup from internal to external (abstract) |
12:06 | Strong contraction mapping and topological non-convex optimization (abstract) |