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Solution of the Unconditional Extremal Problem for a Linear-Fractional Integral Functional Depending on the ParameterJun 13 2019The paper is devoted to the study of the unconditional extremal problem for a fractional linear integral functional defined on a set of probability distributions. In contrast to results proved earlier, the integrands of the integral expressions in the ... More
Strategic customer behavior in a queueing system with alternating information structureJun 13 2019Strategic customer behavior is strongly influenced by the level of information that is provided to customers. Hence, to optimize the design of queueing systems, many studies consider various versions of the same service model and compare them under different ... More
Non-convex optimization via strongly convex majoirziation-minimizationJun 13 2019In this paper, we introduce a class of nonsmooth nonconvex least square optimization problem using convex analysis tools and we propose to use the iterative minimization-majorization (MM) algorithm on a convex set with initializer away from the origin ... More
Cut Selection For Benders DecompositionJun 13 2019In this paper, we present a new perspective on cut generation in the context of Benders decomposition. The approach, which is based on the relation between the alternative polyhedron and the reverse polar set, helps us to improve established cut selection ... More
Zeroth-Order Stochastic Block Coordinate Type Methods for Nonconvex OptimizationJun 13 2019We study (constrained) nonconvex (composite) optimization problems where the decision variables vector can be split into blocks of variables. Random block projection is a popular technique to handle this kind of problem for its remarkable reduction of ... More
A Brief Introduction to Manifold OptimizationJun 13 2019Manifold optimization is ubiquitous in computational and applied mathematics, statistics, engineering, machine learning, physics, chemistry and etc. One of the main challenges usually is the non-convexity of the manifold constraints. By utilizing the ... More
Folding Bilateral Backstepping Output-Feedback Control Design For an Unstable Parabolic PDEJun 13 2019We present a novel methodology for designing output-feedback backstepping boundary controllers for an unstable 1-D diffusion-reaction partial differential equation with spatially-varying reaction. Using "folding" transforms the parabolic PDE into a 2X2 ... More
Critical Point Finding with Newton-MR by Analogy to Computing Square RootsJun 12 2019Understanding of the behavior of algorithms for resolving the optimization problem (hereafter shortened to OP) of optimizing a differentiable loss function (OP1), is enhanced by knowledge of the critical points of that loss function, i.e. the points where ... More
Model-Free Practical Cooperative Control for Diffusively Coupled SystemsJun 12 2019Jun 13 2019In this paper, we develop a data-based controller design framework for diffusively coupled systems with guaranteed convergence to an $\epsilon$-neighborhood of the desired formation. The controller is comprised of a fixed controller with an adjustable ... More
Model-Free Practical Cooperative Control for Diffusively Coupled SystemsJun 12 2019In this paper, we develop a data-based controller design framework for diffusively coupled systems with guaranteed convergence to an $\epsilon$-neighborhood of the desired formation. The controller is comprised of a fixed controller with an adjustable ... More
Global optimization using Sobol indicesJun 12 2019We propose and assess a new global (derivative-free) optimization algorithm, inspired by the LIPO algorithm, which uses variance-based sensitivity analysis (Sobol indices) to reduce the number of calls to the objective function. This method should be ... More
Model Predictive Control, Cost Controllability, and HomogeneityJun 12 2019We are concerned with the design of Model Predictive Control (MPC) schemes such that asymptotic stability of the resulting closed loop is guaranteed even if the linearization at the desired set point fails to be stabilizable. Therefore, we propose to ... More
Knowledge Gradient for Selection with Covariates: Consistency and ComputationJun 12 2019Knowledge gradient is a design principle for developing Bayesian sequential sampling policies to consider in this paper the ranking and selection problem in the presence of covariates, where the best alternative is not universal but depends on the covariates. ... More
De Finetti's control problem with Parisian ruin for spectrally negative Lévy processesJun 12 2019We consider de Finetti's stochastic control problem when the (controlled) process is allowed to spend time under the critical level. More precisely, we consider a generalized version of this control problem in a spectrally negative L\'evy model with exponential ... More
Two-stage Stochastic Lot-sizing Problem with Chance-constrained Condition in the Second StageJun 12 2019In a given production planning horizon, the demands may only be comfirmed in part of the whole periods, and the others are uncertain. In this paper, we consider a two-stage stochastic lot-sizing problem with chance-constrained condition in the second ... More
Bilateral Boundary Control Design for a Cascaded Diffusion-ODE System Coupled at an Arbitrary Interior PointJun 12 2019We present a methodology for designing bilateral boundary controllers for a class of systems consisting of a coupled diffusion equation with an unstable ODE at an arbitrary interior point. A folding transformation is applied about the coupling point, ... More
Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural NetworksJun 12 2019Tight estimation of the Lipschitz constant for deep neural networks (DNNs) is useful in many applications ranging from robustness certification of classifiers to stability analysis of closed-loop systems with reinforcement learning controllers. Existing ... More
Communication-Efficient Accurate Statistical EstimationJun 12 2019When the data are stored in a distributed manner, direct application of traditional statistical inference procedures is often prohibitive due to communication cost and privacy concerns. This paper develops and investigates two Communication-Efficient ... More
Statistical guarantees for local graph clusteringJun 11 2019Local graph clustering methods aim to find small clusters in very large graphs. These methods take as input a graph and a seed node, and they return as output a good cluster in a running time that depends on the size of the output cluster but that is ... More
Reinforcement Learning for Integer Programming: Learning to CutJun 11 2019Integer programming (IP) is a general optimization framework widely applicable to a variety of unstructured and structured problems arising in, e.g., scheduling, production planning, and graph optimization. As IP models many provably hard to solve problems, ... More
Fast Trajectory Optimization via Successive Convexification for Spacecraft Rendezvous with Integer ConstraintsJun 11 2019In this paper we present a fast method based on successive convexification for generating fuel-optimized spacecraft rendezvous trajectories in the presence of mixed-integer constraints. A recently developed paradigm of state-triggered constraints allows ... More
Active Distribution Grids offering Ancillary Services in Islanded and Grid-connected ModeJun 11 2019Future active distribution grids (ADGs) will incorporate a plethora of Distributed Generators (DGs) and other Distributed Energy Resources (DERs), allowing them to provide ancillary services in grid-connected mode and, if necessary, operate in an islanded ... More
Generating Pareto optimal dose distributions for radiation therapy treatment planningJun 11 2019Radiotherapy treatment planning currently requires many trail-and-error iterations between the planner and treatment planning system, as well as be-tween the planner and physician for discussion/consultation. The physician's preferences for a particular ... More
Deep Forward-Backward SDEs for Min-max ControlJun 11 2019This paper presents a novel approach to numerically solve stochastic differential games for nonlinear systems. The proposed approach relies on the nonlinear Feynman-Kac theorem that establishes a connection between parabolic deterministic partial differential ... More
Deep 2FBSDEs for Systems with Control Multiplicative NoiseJun 11 2019We present a deep recurrent neural network architecture to solve a class of stochastic optimal control problems described by fully nonlinear Hamilton Jacobi Bellman partial differential equations. Such PDEs arise when one considers stochastic dynamics ... More
Variance-reduced $Q$-learning is minimax optimalJun 11 2019We introduce and analyze a form of variance-reduced $Q$-learning. For $\gamma$-discounted MDPs with finite state space $\mathcal{X}$ and action space $\mathcal{U}$, we prove that it yields an $\epsilon$-accurate estimate of the optimal $Q$-function in ... More
Hybrid Nonlinear Observers for Inertial Navigation Using Landmark MeasurementsJun 11 2019This paper considers the problem of attitude, position and linear velocity estimation for rigid body systems relying on landmark measurements. We propose two hybrid nonlinear observers on the matrix Lie group $SE_2(3)$, leading to global exponential stability. ... More
An Improved Analysis of Training Over-parameterized Deep Neural NetworksJun 11 2019A recent line of research has shown that gradient-based algorithms with random initialization can converge to the global minima of the training loss for over-parameterized (i.e., sufficiently wide) deep neural networks. However, the condition on the width ... More
Analysis of Optimization Algorithms via Sum-of-SquaresJun 11 2019In this work, we introduce a new framework for unifying and systematizing the performance analysis of first-order black-box optimization algorithms for unconstrained convex minimization over finite-dimensional Euclidean spaces. The low-cost iteration ... More
A Proximal Point Dual Newton Algorithm for Solving Group Graphical Lasso ProblemsJun 11 2019Undirected graphical models have been especially popular for learning the conditional independence structure among a large number of variables where the observations are drawn independently and identically from the same distribution. However, many modern ... More
A refined primal-dual analysis of the implicit biasJun 11 2019Recent work shows that gradient descent on linearly separable data is implicitly biased towards the maximum margin solution. However, no convergence rate which is tight in both n (the dataset size) and t (the training time) is given. This work proves ... More
Upper envelopes of families of Feller semigroups and viscosity solutions to a class of nonlinear Cauchy problemsJun 11 2019In this paper we construct the smallest semigroup $\mathscr{S}$ that dominates a given family of linear Feller semigroups. The semigroup $\mathscr{S}$ will be referred to as the semigroup envelope or Nisio semigroup. In a second step we investigate strong ... More
Macro-action Multi-timescale Dynamic Programming for Energy Management with Phase Change MaterialsJun 11 2019This paper focuses on home energy management systems (HEMS) in buildings that have controllable HVAC systems and use phase change material (PCM) as an energy storage system. In this setting, optimally operating a HVAC system is a challenge, because of ... More
Vulnerabilities of Power System Operations to Load Forecasting Data Injection AttacksJun 11 2019We study the security threats of power system operation brought by a class of data injection attacks upon load forecasting algorithms. In particular, with minimal assumptions on the knowledge and ability of the attacker, we design attack data on input ... More
Approximate Gradient Descent Convergence Dynamics for Adaptive Control on Heterogeneous NetworksJun 11 2019Adaptive control is a classical control method for complex cyber-physical systems, including transportation networks. In this work, we analyze the convergence properties of such methods on exemplar graphs, both theoretically and numerically. We first ... More
Efficiently escaping saddle points on manifoldsJun 10 2019Smooth, non-convex optimization problems on Riemannian manifolds occur in machine learning as a result of orthonormality, rank or positivity constraints. First- and second-order necessary optimality conditions state that the Riemannian gradient must be ... More
Multi-Level Taxonomy and Critical Review of Eco-Routing MethodsJun 10 2019Routing decisions are initially based on minimizing travel time. Nevertheless, eco-routing considers the environmental aspect (e.g. emissions, fuel, and exposure) and was introduced to replace the initial routing concept to mitigate the undesirable impact ... More
Sequential Source Coding for Stochastic Systems Subject to Finite Rate ConstraintsJun 10 2019In this paper, we apply a sequential source coding framework to analyze fundamental performance limitations of stochastic control systems subject to feedback data-rate constraints. We first show that the characterization of the rate-distortion region ... More
Inference and Uncertainty Quantification for Noisy Matrix CompletionJun 10 2019Noisy matrix completion aims at estimating a low-rank matrix given only partial and corrupted entries. Despite substantial progress in designing efficient estimation algorithms, it remains largely unclear how to assess the uncertainty of the obtained ... More
Convergence analysis of a Crank-Nicolson Galerkin method for an inverse source problem for parabolic systems with boundary observationsJun 10 2019This work is devoted to an inverse problem of identifying a source term depending on both spatial and time variables in a parabolic equation from single Cauchy data on a part of the boundary. A Crank-Nicolson Galerkin method is applied to the least squares ... More
Distributionally Robust Optimization for a Resilient Transmission Grid During Geomagnetic DisturbancesJun 10 2019Jun 11 2019In recent years, there have been increasing concerns about the impacts of geomagnetic disturbances (GMDs) on electrical power systems. Geomagnetically-induced currents (GICs) can saturate transformers, induce hot-spot heating and increase reactive power ... More
Distributionally Robust Optimization for a Resilient Transmission Grid During Geomagnetic DisturbancesJun 10 2019In recent years, there have been increasing concerns about the impacts of geomagnetic disturbances (GMDs) on electrical power systems. Geomagnetically-induced currents (GICs) can saturate transformers, induce hot-spot heating and increase reactive power ... More
Flexible Demand Resource Pricing Scheme: A Stochastic Benefit-Sharing ApproachJun 10 2019With the rapidly increased penetration of renewable generations, incentive-based demand side management (DSM) shows great value on alleviating the uncertainty and providing flexibility for microgrid. However, how to price those demand resources becomes ... More
Flexible Demand Resource Pricing Scheme: A Stochastic Benefit-Sharing ApproachJun 10 2019Jun 12 2019With the rapidly increased penetration of renewable generations, incentive-based demand side management (DSM) shows great value on alleviating the uncertainty and providing flexibility for microgrid. However, how to price those demand resources becomes ... More
Optimal multi-period dispatch of distributed energy resources in unbalanced distribution feedersJun 10 2019This paper develops an efficient algorithm for the optimal dispatch of deterministic inverter-interfaced energy storage in an unbalanced distribution feeder with significant solar PV penetration.The three-phase non-convex loss-minimization problem is ... More
An active-set algorithm for norm constrained quadratic problemsJun 10 2019We present an algorithm for the minimization of a nonconvex quadratic function subject to linear inequality constraints and a two-sided bound on the 2-norm of its solution. The algorithm minimizes the objective using an active-set method by solving a ... More
MPC-Based Precision Cooling Strategy (PCS) for Efficient Thermal Management of Automotive Air Conditioning SystemJun 10 2019In this paper, we propose an MPC-based precision cooling strategy (PCS) for energy efficient thermal management of automotive air conditioning (A/C) system. The proposed PCS is able to provide precise tracking of the time-varying cooling power trajectory, ... More
Tuning-Free, Low Memory Robust Estimator to Mitigate GPS Spoofing AttacksJun 10 2019The operation of critical infrastructures such as the electrical power grid, cellphone towers, and financial institutions relies on precise timing provided by stationary GPS receivers. These GPS devices are vulnerable to a type of spoofing called Time ... More
Time-Optimal Control Problem With State Constraints In A Time-Periodic Flow FieldJun 10 2019This article contributes to a framework for a computational indirect method based on the Pontryagin maximum principle to efficiently solve a class of state constrained time-optimal control problems in the presence of a time-dependent flow field. Path-planning ... More
A Lyapunov Approach to Robust Regulation of Distributed Port-Hamiltonian SystemsJun 10 2019This paper studies robust output tracking and disturbance rejection for boundary controlled infinite-dimensional port-Hamiltonian systems including second order models such as the Euler-Bernoulli beam. The control design is achieved using the internal ... More
Stochastic Mirror Descent on Overparameterized Nonlinear Models: Convergence, Implicit Regularization, and GeneralizationJun 10 2019Most modern learning problems are highly overparameterized, meaning that there are many more parameters than the number of training data points, and as a result, the training loss may have infinitely many global minima (parameter vectors that perfectly ... More
Gossip-based Actor-Learner Architectures for Deep Reinforcement LearningJun 09 2019Multi-simulator training has contributed to the recent success of Deep Reinforcement Learning by stabilizing learning and allowing for higher training throughputs. We propose Gossip-based Actor-Learner Architectures (GALA) where several actor-learners ... More
Distributed sub-optimal resource allocation via a projected form of singular perturbationJun 09 2019Distributed optimization for resource allocation problems is investigated and a sub-optimal continuous-time algorithm is proposed. Our algorithm has lower order dynamics than others to reduce burdens of computation and communication, and is applicable ... More
Accelerated Alternating MinimizationJun 09 2019Alternating minimization (AM) optimization algorithms have been known for a long time and are of importance in machine learning problems, among which we are mostly motivated by approximating optimal transport distances. AM algorithms assume that the decision ... More
Optimal Control for Controllable Stochastic Linear SystemsJun 09 2019This paper is concerned with a constrained stochastic linear-quadratic optimal control problem, in which the terminal state is fixed and the initial state is constrained to lie in a stochastic linear manifold. The controllability of stochastic linear ... More
Verifying fundamental solution groups for lossless wave equations via stationary action and optimal controlJun 09 2019A representation of a fundamental solution group for a class of wave equations is constructed by exploiting connections between stationary action and optimal control. By using a Yosida approximation of the associated generator, an approximation of the ... More
Toward Solving 2-TBSG EfficientlyJun 09 20192-TBSG is a two-player game model which aims to find Nash equilibriums and is widely utilized in reinforced learning and AI. Inspired by the fact that the simplex method for solving the deterministic discounted Markov decision processes (MDPs) is strongly ... More
Reducing the variance in online optimization by transporting past gradientsJun 08 2019Most stochastic optimization methods use gradients once before discarding them. While variance reduction methods have shown that reusing past gradients can be beneficial when there is a finite number of datapoints, they do not easily extend to the online ... More
Linear Optimization of Polynomials and Rational Functions over BoxesJun 08 2019In this paper, we investigate the problem of finding tight linear lower bounding functions for multivariate polynomials over boxes. These functions are obtained by the expansion of polynomials into Bernstein form and using the linear least squares function. ... More
Optimal Convergence for Stochastic Optimization with Multiple Expectation ConstraintsJun 08 2019In this paper, we focus on the problem of stochastic optimization where the objective function can be written as an expectation function over a closed convex set. We also consider multiple expectation constraints which restrict the domain of the problem. ... More
The regulator problem for the one-dimensional Schrodinger equation via the backstepping approachJun 08 2019We investigate the regulator problem (tracking and disturbance rejection) for a system (plant) described by a boundary controlled anti-stable linear one-dimensional Schrodinger equation, using the backstepping approach. The output to be controlled is ... More
Nonlinear Pose Filters on the Special Euclidean Group SE(3) with Guaranteed Transient and Steady-state PerformanceJun 07 2019Two novel nonlinear pose (i.e, attitude and position) filters developed directly on the Special Euclidean Group SE(3)able to guarantee prescribed characteristics of transient and steady-state performance are proposed. The position error and normalized ... More
Mean Field Games for Multi-agent Systems with Multiplicative NoisesJun 07 2019This paper studies mean field games for multi-agent systems with control-dependent multiplicative noises. For the general systems with nonuniform agents, we obtain a set of decentralized strategies by solving an auxiliary limiting optimal control problem ... More
Robust subgaussian estimation of a mean vector in nearly linear timeJun 07 2019Jun 11 2019We construct an algorithm, running in nearly-linear time, which is robust to outliers and heavy-tailed data and which achieves the subgaussian rate from [Lugosi, Mendelson] \begin{equation}\label{eq:intro_subgaus_rate} \sqrt{\frac{{\rm Tr}(\Sigma)}{N}}+\sqrt{\frac{||\Sigma||_{op}K}{N}} ... More
Polyak Steps for Adaptive Fast Gradient MethodJun 07 2019Accelerated algorithms for minimizing smooth strongly convex functions usually require knowledge of the strong convexity parameter $\mu$. In the case of an unknown $\mu$, current adaptive techniques are based on restart schemes. When the optimal value ... More
Closed-loop adaptive control of extreme events in a turbulent flowJun 07 2019Extreme events that arise spontaneously in chaotic dynamical systems often have an adverse impact on the system or the surrounding environment. As such, their mitigation is highly desirable. Here, we introduce a novel control strategy for mitigating extreme ... More
Matheuristic algorithms for the parallel drone scheduling traveling salesman problemJun 07 2019In a near future drones are likely to become a viable way of distributing parcels in a urban environment. In this paper we consider the parallel drone scheduling traveling salesman problem, where a set of customers requiring a delivery is split between ... More
Optimal and Sub-optimal Feedback Controls for Biogas ProductionJun 07 2019We revisit the optimal control problem of maximizing biogas production in continuous bio-processes in two directions: 1. over an infinite horizon, 2. with sub-optimal controllers independent of the time horizon. For the first point, we identify a set ... More
Hidden Convexity in the l0 PseudonormJun 07 2019The so-called l0 pseudonorm on R d counts the number of nonzero components of a vector. It is well-known that the l0 pseudonorm is not convex, as its Fenchel biconjugate is zero. In this paper, we introduce a suitable conjugacy, induced by a novel coupling, ... More
Optimal Resource Procurement and the Price of CausalityJun 07 2019This paper studies the problem of procuring diverse resources in a forward market to cover a set $\bf{E}$ of uncertain demand signals $\bf{e}$. We consider two scenarios: (a) $\bf{e}$ is revealed all at once by an oracle (b) $\bf{e}$ reveals itself causally. ... More
Adaptive Step Size Strategy for Orthogonality Constrained Line Search MethodsJun 07 2019In this paper, we propose an adaptive step size strategy for a class of line search methods for orthogonality constrained minimization problems, which avoids the classic backtracking procedure. We prove the convergence of the line search methods equipped ... More
PDE Traffic Observer Validated on Freeway DataJun 06 2019This paper develops boundary observer for estimation of congested freeway traffic states based on Aw-Rascle-Zhang (ARZ) partial differential equations (PDE) model. Traffic state estimation refers to acquisition of traffic state information from partially ... More
Synthesis of control Lyapunov functions and stabilizing feedback strategies using exit-time optimal controlJun 06 2019This paper studies the problem of constructing control Lyapunov functions (CLFs) and feedback stabilization strategies for deterministic nonlinear control systems described by ordinary differential equations. Many numerical methods for solving the Hamilton-Jacobi-Bellman ... More
A Non-Asymptotic Analysis of Network Independence for Distributed Stochastic Gradient DescentJun 06 2019This paper is concerned with minimizing the average of $n$ cost functions over a network, in which agents may communicate and exchange information with their peers in the network. Specifically, we consider the setting where only noisy gradient information ... More
An Inverse Optimization Approach to Measuring Clinical Pathway ConcordanceJun 06 2019Clinical pathways outline standardized processes in the delivery of care for a specific disease. Patient journeys through the healthcare system, though, can deviate substantially from recommended or reference pathways. Given the positive benefits of clinical ... More
Hamiltonian descent for composite objectivesJun 06 2019In optimization the duality gap between the primal and the dual problems is a measure of the suboptimality of any primal-dual point. In classical mechanics the equations of motion of a system can be derived from the Hamiltonian function, which is a quantity ... More
Model predictive control with stage cost shaping inspired by reinforcement learningJun 06 2019This work presents a suboptimality study of a particular model predictive control with a stage cost shaping based on the ideas of reinforcement learning. The focus of the suboptimality study is to derive quantities relating the infinite-horizon cost function ... More
Primal-Dual Block Frank-WolfeJun 06 2019We propose a variant of the Frank-Wolfe algorithm for solving a class of sparse/low-rank optimization problems. Our formulation includes Elastic Net, regularized SVMs and phase retrieval as special cases. The proposed Primal-Dual Block Frank-Wolfe algorithm ... More
Deep Reinforcement Learning for Multi-objective OptimizationJun 06 2019This study proposes an end-to-end framework for solving multi-objective optimization problems (MOPs) using Deep Reinforcement Learning (DRL), termed DRL-MOA. The idea of decomposition is adopted to decompose a MOP into a set of scalar optimization subproblems. ... More
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local ComputationsJun 06 2019Communication bottleneck has been identified as a significant issue in distributed optimization of large-scale learning models. Recently, several approaches to mitigate this problem have been proposed, including different forms of gradient compression ... More
On the Convergence of SARAH and BeyondJun 05 2019The main theme of this work is a unifying algorithm, abbreviated as L2S, that can deal with (strongly) convex and nonconvex empirical risk minimization (ERM) problems. It broadens a recently developed variance reduction method known as SARAH. L2S enjoys ... More
On the graphical stability of hybrid solutions with non-matching jump times: Extended PaperJun 05 2019We investigate stability of a solution of a hybrid system in the sense that the graphs of solutions from nearby initial conditions remain close and tend towards the graph of the given solution. In this manner, a small continuous-time mismatch is allowed ... More
A neural network based policy iteration algorithm with global $H^2$-superlinear convergence for stochastic games on domainsJun 05 2019In this work, we propose a class of numerical schemes for solving semilinear Hamilton-Jacobi-Bellman-Isaacs (HJBI) boundary value problems which arise naturally from exit time problems of diffusion processes with controlled drift. We exploit policy iteration ... More
Optimal control of infinite-dimensional Piecewise Deterministic Markov Processes: a BSDE approach. Application to the control of an excitable cell membraneJun 05 2019In this paper we consider the optimal control of Hilbert space-valued infinite-dimensional Piecewise Deterministic Markov Processes (PDMP) and we prove that the corresponding value function can be represented via a Feynman-Kac type formula through the ... More
Weighted Irrigation PlansJun 05 2019We model an irrigation network where lower branches must be thicker in order to support the weight of the higher ones. This leads to a countable family of ODEs, one for each branch, that must be solved by backward induction. Having introduced conditions ... More
Quantum Algorithms for Solving Dynamic Programming ProblemsJun 05 2019We present quantum algorithms for solving finite-horizon and infinite-horizon dynamic programming problems. The infinite-horizon problems are studied using the framework of Markov decision processes. We prove query complexity lower bounds for classical ... More
Intrinsic Stability: Global Stability of Dynamical Networks and Switched Systems Resilient to any Type of Time-DelaysJun 05 2019In real-world networks the interactions between network elements are inherently time-delayed. These time-delays can not only slow the network but can have a destabilizing effect on the network's dynamics leading to poor performance. The same is true in ... More
Last-iterate convergence rates for min-max optimizationJun 05 2019We study the problem of finding min-max solutions for smooth two-input objective functions. While classic results show average-iterate convergence rates for various algorithms, nonconvex applications such as training Generative Adversarial Networks require ... More
Dynamic monopolistic competition with sluggish adjustment of entry and exitJun 05 2019We study a steady state of a free entry oligopoly with differentiated goods, that is, a monopolistic competition, with sluggish adjustment of entry and exit of firms under general demand and cost functions by a differential game approach. Mainly we show ... More
A semi-implicit relaxed Douglas-Rachford algorithm (sir-DR) for PtychograhpyJun 05 2019Alternating projection based methods, such as ePIE and rPIE, have been used widely in ptychography. However, they only work well if there are adequate measurements (diffraction patterns); in the case of sparse data (i.e. fewer measurements) alternating ... More
Non-Wire Alternatives: an Additional Value Stream for Distributed Energy ResourcesJun 05 2019Distributed energy resources (DERs) can serve as non-wire alternatives (NWAs) to capacity expansion by managing peak load to avoid or delay traditional expansion projects. However, the value stream derived from using DERs as NWAs is usually not explicitly ... More
How Many Impulses ReduxJun 05 2019A central problem in orbit transfer optimization is to determine the number, time, direction and magnitude of velocity impulses that minimize the total impulse. This problem was posed in 1967 by T. N. Edelbaum, and while notable advances have been made, ... More
Generalized Linear Rule ModelsJun 05 2019This paper considers generalized linear models using rule-based features, also referred to as rule ensembles, for regression and probabilistic classification. Rules facilitate model interpretation while also capturing nonlinear dependences and interactions. ... More
Distributed Training with Heterogeneous Data: Bridging Median and Mean Based AlgorithmsJun 04 2019Recently, there is a growing interest in the study of median-based algorithms for distributed non-convex optimization. Two prominent such algorithms include signSGD with majority vote, an effective approach for communication reduction via 1-bit compression ... More
Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based AlgorithmsJun 04 2019Jun 06 2019Recently, there is a growing interest in the study of median-based algorithms for distributed non-convex optimization. Two prominent such algorithms include signSGD with majority vote, an effective approach for communication reduction via 1-bit compression ... More
Optimal auction duration: A price formation viewpointJun 04 2019We consider an auction market in which market makers fill the order book during a given time period while some other investors send market orders. We define the clearing price of the auction as the price maximizing the exchanged volume at the clearing ... More
Natural Gas Flow Solvers using Convex RelaxationJun 04 2019In addition to their lower emissions and fast ramping capabilities, gas-fired electric power generation is increasing due to newly discovered supplies and declining prices. The vast infrastructure development, gas flow dynamics, and complex interdependence ... More
Legislative effectiveness hangs in the balance: Studying balance and polarization through partitioning signed networksJun 04 2019Over the past several decades in the US Congress, there has been a decline in the fraction of bills introduced that eventually become law. This decline in legislative effectiveness has occurred in parallel with rising levels of political polarization, ... More
Learning dynamic polynomial proofsJun 04 2019Polynomial inequalities lie at the heart of many mathematical disciplines. In this paper, we consider the fundamental computational task of automatically searching for proofs of polynomial inequalities. We adopt the framework of semi-algebraic proof systems ... More
Bayesian Active Learning With Abstention FeedbacksJun 04 2019We study pool-based active learning with abstention feedbacks where a labeler can abstain from labeling a queried example with some unknown abstention rate. Using the Bayesian approach, we develop two new greedy algorithms that learn both the classification ... More