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The reflection principle in the control problem of the heat equationFeb 21 2019We consider the control problem for the generalized heat equation for a Schr\"odinger operator on a domain with a reflection symmetry with respect to a hyperplane. We show that if this system is null-controllable, then so is the system on its respective ... More

Integer Linear Programming Formulations for Double Roman Domination ProblemFeb 21 2019For a graph $G= (V,E)$, a double Roman dominating function (DRDF) is a function $f : V \to \{0,1,2,3\}$ having the property that if $f (v) = 0$, then vertex $v$ must have at least two neighbors assigned $2$ under $f$ or one neighbor $u$ with $f (u) = ... More

LOSSGRAD: automatic learning rate in gradient descentFeb 20 2019In this paper, we propose a simple, fast and easy to implement algorithm LOSSGRAD (locally optimal step-size in gradient descent), which automatically modifies the step-size in gradient descent during neural networks training. Given a function $f$, a ... More

Dynamic Cell Imaging in PET with Optimal Transport RegularizationFeb 20 2019We propose a novel dynamic image reconstruction method from PET listmode data that could be particularly suited to tracking single or small numbers of cells. In contrast to conventional PET reconstruction the proposed method combines the information from ... More

Optimal Decentralized Dynamic Policies for Video Streaming over Wireless ChannelsFeb 20 2019The problem addressed is that of optimally controlling, in a decentralized fashion, the download of mobile video, which is expected to comprise 75 % of total mobile data traffic by 2020. The server can dynamically choose which packets to download to clients, ... More

Gain function approximation in the Feedback Particle FilterFeb 19 2019This paper is concerned with numerical algorithms for the problem of gain function approximation in the feedback particle filter. The exact gain function is the solution of a Poisson equation involving a probability-weighted Laplacian. The numerical problem ... More

Global Convergence of Adaptive Gradient Methods for An Over-parameterized Neural NetworkFeb 19 2019Adaptive gradient methods like AdaGrad are widely used in optimizing neural networks. Yet, existing convergence guarantees for adaptive gradient methods require either convexity or smoothness, and, in the smooth setting, only guarantee convergence to ... More

A Sequential Homotopy Method for Mathematical Programming ProblemsFeb 19 2019We propose a sequential homotopy method for the solution of mathematical programming problems formulated in abstract Hilbert spaces under the Guignard constraint qualification. The method is equivalent to performing projected backward Euler timestepping ... More

Optimizing certain combinations of spectral and linear$/$distance functions over spectral setsFeb 18 2019In the settings of Euclidean Jordan algebras, normal decomposition systems (or Eaton triples), and structures induced by complete isometric hyperbolic polynomials, we consider the problem of optimizing a certain combination (such as the sum) of spectral ... More

The Kalai-Smorodinski solution for many-objective Bayesian optimizationFeb 18 2019An ongoing aim of research in multiobjective Bayesian optimization is to extend its applicability to a large number of objectives. While coping with a limited budget of evaluations, recovering the set of optimal compromise solutions generally requires ... More

Existence of solutions to principal-agent problems under general preferences and non-compact allocation spaceFeb 18 2019We give an existence result for the principal-agent problem with adverse selection under general preferences and non-compact allocation space. The result is mainly based on the fact that the principal can always improve a feasible contract by another ... More

Singular control of SPDEs with space-mean dynamicsFeb 18 2019We consider the problem of optimal singular control of a stochastic partial differential equation (SPDE) with space-mean dependence. Such systems are proposed as models for population growth in a random environment. We obtain sufficient and necessary ... More

Decentralized Static Output Feedback Controller Design for Large Scale Switched T-S SystemsFeb 18 2019This paper investigates the design of decentralized output-feedback controllers for a class of a large scale switched nonlinear systems under arbitrary switching laws. A global large scale switched system can be split into a set of smaller interconnected ... More

Limitations on the expressive power of convex cones without long chains of facesFeb 18 2019A convex optimization problem in conic form involves minimizing a linear functional over the intersection of a convex cone C and an affine subspace. In some cases it is possible to replace a conic formulation using a cone C with a 'lifted' conic formulation ... More

Quantized Frank-Wolfe: Communication-Efficient Distributed OptimizationFeb 17 2019How can we efficiently mitigate the overhead of gradient communications in distributed optimization? This problem is at the heart of training scalable machine learning models and has been mainly studied in the unconstrained setting. In this paper, we ... More

Differentially Private Smart Metering: Implementation, Analytics, and BillingFeb 17 2019Smart power grids offer to revolutionize power distribution by sharing granular power usage data, though this same data sharing can reveal a great deal about users, and there are serious privacy concerns for customers. In this paper, we address these ... More

Optimal dividends and capital injection under dividend restrictionsFeb 17 2019We study a singular stochastic control problem faced by the owner of an insurance company that dynamically pays dividends and raises capital in the presence of the restriction that the surplus process must be above a given dividend payout barrier in order ... More

Optimal Stabilization Control for Discrete-time Markov Jump Linear System with Control Input DelayFeb 17 2019This paper will investigate the infinite horizon optimal control and stabilization problems for the Markov jump linear system (MJLS) subject to control input delay. Different from previous works, for the first time, the necessary and sufficient stabilization ... More

An efficient nonmonotone adaptive trust region method for unconstrained optimizationFeb 17 2019In this paper, we propose a new and efficient nonmonotone adaptive trust region algorithm to solve unconstrained optimization problems. This algorithm incorporates two novelties: it benefits from a radius dependent shrinkage parameter for adjusting the ... More

Nonlinear Transversality of Collections of Sets: Primal Space CharacterizationsFeb 17 2019This paper continues the study of 'good arrangements' of collections of sets in normed vector or Banach spaces near a point in their intersection. Our aim is to study general nonlinear transversality properties, namely, $\varphi-$semitransversality, $\varphi-$subtransversality ... More

Optimal Stopping and Utility in a Simple Model of Unemployment InsuranceFeb 16 2019Managing unemployment is one of the key issues in social policies. Unemployment insurance schemes are designed to cushion the financial and morale blow of loss of job but also to encourage the unemployed to seek new jobs more pro-actively due to the continuous ... More

Faster Gradient-Free Proximal Stochastic Methods for Nonconvex Nonsmooth OptimizationFeb 16 2019Proximal gradient method has been playing an important role to solve many machine learning tasks, especially for the nonsmooth problems. However, in some machine learning problems such as the bandit model and the black-box learning problem, proximal gradient ... More

On Privacy-preserving Decentralized Optimization through Alternating Direction Method of MultipliersFeb 16 2019Privacy concerns with sensitive data in machine learning are receiving increasing attention. In this paper, we study privacy-preserving distributed learning under the framework of Alternating Direction Method of Multipliers (ADMM). While secure distributed ... More

Tangencies and Polynomial OptimizationFeb 16 2019Given a polynomial function $f \colon \mathbb{R}^n \rightarrow \mathbb{R}$ and a unbounded basic closed semi-algebraic set $S \subset \mathbb{R}^n,$ in this paper we show that the conditions listed below are characterized exactly in terms of the so-called ... More

Geometric Programming-Based Control for Nonlinear, DAE-Constrained Water Distribution NetworksFeb 16 2019Control of water distribution networks (WDNs) can be represented by an optimization problem with hydraulic models describing the nonlinear relationship between head loss, water flow, and demand. The problem is difficult to solve due to the non-convexity ... More

Characterizing the Nonlinearity of Power System Generator ModelsFeb 16 2019Power system dynamics are naturally nonlinear. The nonlinearity stems from power flows, generator dynamics, and electromagnetic transients. Characterizing the nonlinearity of the dynamical power system model is useful for designing superior estimation ... More

Nonparametric Compositional Stochastic OptimizationFeb 15 2019In this work, we address optimization problems where the objective function is a nonlinear function of an expected value, i.e., compositional stochastic {strongly convex programs}. We consider the case where the decision variable is not vector-valued ... More

Palais-Smale values and stability of global Hölderian error bounds for polynomial functionsFeb 15 2019Let $f$ be a polynomial function of $n$ variables. In this paper, we study stability of global H\"{o}lderian error bound for a sublevel set $[f \le t]$ under a perturbation of $t$. Namely, we investigate the following questions: 1. Suppose that $[f \le ... More

Polarizing Anisotropic Heisenberg GroupsFeb 15 2019We expand the class of polarizable Carnot groups by implementing a technique to polarize anisotropic Heisenberg groups.

Finite element error estimates for elliptic optimal control by BV functionsFeb 15 2019We derive a priori error estimates for two discretizations of a PDE-constrained optimal control problem that involves univariate functions of bounded variation as controls. Using, first, variational discretization of the control problem we prove $L^2$-, ... More

On exponential stabilization of N-level quantum angular momentum systemsFeb 15 2019In this paper, we consider the feedback stabilization problem for N-level quantum angular momentum systems undergoing continuous-time measurements. By using stochastic and geometric control tools, we provide sufficient conditions on the feedback control ... More

Using GRASP Approach and Path Relinking to Minimize Total Number of Tardy Jobs on a Single Batch Processing MachineFeb 15 2019This paper considers the problem of scheduling a single batch processing machine such that the total number of tardy jobs is minimized. The machine can simultaneously process several jobs as a batch as long as the machine capacity is not violated. The ... More

Quantitative analysis of a singularly perturbed shape optimization problem in a polygonFeb 15 2019We carry on our study of the connection between two shape optimization problems with spectral cost. On the one hand, we consider the optimal design problem for the survival threshold of a population living in a heterogenous habitat $\Omega$; this problem ... More

Weak monotone rearrangement on the lineFeb 15 2019Weak optimal transport has been recently introduced by Gozlan et al. The original motivation stems from the theory of geometric inequalities; further applications concern numerics of martingale optimal transport and stability in mathematical finance. ... More

A Mean-field Approach for Controlling Singularly Perturbed Multi-population SIS EpidemicsFeb 15 2019We consider a multi-population epidemic model with one or more (almost) isolated communities and one mobile community. Each of the isolated communities has contact within itself and, in addition, contact with the outside world but only through the mobile ... More

ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex OptimizationFeb 15 2019In this paper, we propose a new stochastic algorithmic framework to solve stochastic composite nonconvex optimization problems that covers both finite-sum and expectation settings. Our algorithms rely on the SARAH estimator introduced in (Nguyen et al., ... More

Generalizing Laplacian Controllability of PathsFeb 15 2019It is well known that if a network topology is a path or line and the states of vertices or nodes evolve according to the consensus policy, then the network is Laplacian controllable by an input connected to its terminal vertex. In this work a path is ... More

Dynamic Interconnection and Damping Injection for Input-to-State Stable Bilateral TeleoperationFeb 14 2019In bilateral teleoperation, the human who operates the master and the environment which interacts with the slave are part of the force feedback loop. Yet, both have time-varying and unpredictable dynamics and are challenging to model. A conventional strategy ... More

Schauder Estimates for a Class of Potential Mean Field Games of ControlsFeb 14 2019An existence result for a class of mean field games of controls is provided. In the considered model, the cost functional to be minimized by each agent involves a price depending at a given time on the controls of all agents and a congestion term. The ... More

A Bundle Approach for SDPs with Exact Subgraph ConstraintsFeb 14 2019The 'exact subgraph' approach was recently introduced as a hierarchical scheme to get increasingly tight semidefinite programming relaxations of several NP-hard graph optimization problems. Solving these relaxations is a computational challenge because ... More

Mean-field optimal control and optimality conditions in the space of probability measuresFeb 14 2019We derive a framework to compute optimal controls for problems with states in the space of probability measures. Since many optimal control problems constrained by a system of ordinary differential equations (ODE) modelling interacting particles converge ... More

Second-order analysis for the time crisis problemFeb 14 2019In this article, we prove second-order necessary optimality conditions for the so-called time crisis problem that comes up within the context of viability theory. It consists in minimizing the time spent by solutions of a controlled dynamics outside a ... More

Generalized subdifferentials of spectral functions over Euclidean Jordan algebrasFeb 14 2019This paper is devoted to the study of generalized subdifferentials of spectral functions over Euclidean Jordan algebras. Spectral functions appear often in optimization problems field playing the role of "regularizer", "barrier", "penalty function" and ... More

A linear programming approach to approximating infinite time reachable sets of strictly stable linear control systemsFeb 14 2019We develop a new numerical method for approximating infinite time reachable sets of strictly stable linear control systems. By solving a linear program with a constraint that incorporates the system dynamics, we compute a polytope with fixed facet normals ... More

First-order Methods with Convergence Rates for Multi-agent Systems on Semidefinite Matrix SpacesFeb 14 2019The goal in this paper is to develop first-order methods equipped with convergence rates for multi-agent optimization problems on semidefinite matrix spaces. These problems include cooperative optimization problems and non-cooperative Nash games. Accordingly, ... More

Fitting rectangles under vulnerability curves: optimal water flow through plantsFeb 13 2019We study an optimization problem for a model of steady state water transport through plants that maximizes water flow subject to the constraints on hydraulic conductance due to vulnerability to embolism (air blockage of conduits). The model has an elementary ... More

A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic ForecastingFeb 13 2019We study epidemic forecasting on real-world health data by a graph-structured recurrent neural network (GSRNN). We achieve state-of-the-art forecasting accuracy on the benchmark CDC dataset. To improve model efficiency, we sparsify the network weights ... More

From Time-Domain Data to Low-Dimensional Structured ModelsFeb 13 2019We present a framework for constructing a structured realization of a linear time-invariant dynamical system solely from a discrete sampling of an input and output trajectory of the system. We estimate the transfer function of the original model at selected ... More

Feedback Stabilization of a Class of Diagonal Infinite-Dimensional Systems with Delay Boundary ControlFeb 13 2019This paper studies the boundary feedback stabilization of a class of diagonal infinite-dimensional boundary control systems. In the studied setting, the boundary control input is subject to a constant delay while the open loop system might exhibit a finite ... More

Stable-Predictive Optimistic Counterfactual Regret MinimizationFeb 13 2019The CFR framework has been a powerful tool for solving large-scale extensive-form games in practice. However, the theoretical rate at which past CFR-based algorithms converge to the Nash equilibrium is on the order of $O(T^{-1/2})$, where $T$ is the number ... More

Do Subsampled Newton Methods Work for High-Dimensional Data?Feb 13 2019Subsampled Newton methods approximate Hessian matrices through subsampling techniques, alleviating the cost of forming Hessian matrices but using sufficient curvature information. However, previous results require $\Omega (d)$ samples to approximate Hessians, ... More

A Branch-and-Price Algorithm for the Temporal Bin Packing ProblemFeb 13 2019We study an extension of the classical Bin Packing Problem, where each item consumes the bin capacity during a given time window that depends on the item itself. The problem asks for finding the minimum number of bins to pack all the items while respecting ... More

Sparse learning of chemical reaction networks from trajectory dataFeb 13 2019In this paper, we develop a data-driven numerical method to learn chemical reaction networks from trajectory data. Modeling the reaction system as a continuous-time Markov chain, our method learns the propensity functions of the system with predetermined ... More

Decoupling of Control and Force Objective in Adjoint-Based Fluid Dynamic Shape OptimizationFeb 13 2019We discuss exterior and classical interior alternatives for evaluating fluid flow induced forces on bodies. The discussion aims at a reduction of the total shape derivative, achieved through a decoupling of control and objective in the exterior approach. ... More

Dynamic Non-Diagonal Regularization in Interior Point Methods for Linear and Convex Quadratic ProgrammingFeb 13 2019In this paper, we present a dynamic non-diagonal regularization for interior point methods. The non-diagonal aspect of this regularization is implicit, since all the off-diagonal elements of the regularization matrices are cancelled out by those elements ... More

A Suitable Conjugacy for the l0 PseudonormFeb 13 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

Inexact Convex Relaxations for AC Optimal Power Flow: Towards AC FeasibilityFeb 13 2019Convex relaxations of AC optimal power flow (AC-OPF) problems have attracted significant interest as in several instances they provably yield the global optima to the original non-convex problems. If the relaxation fails to be exact, the optimality gap ... More

Lower Bound Convex Programs for Exact Sparse OptimizationFeb 13 2019In exact sparse optimization problems on Rd (also known as sparsity constrained problems), one looks for solution that have few nonzero components. In this paper, we consider problems where sparsity is exactly measured either by the nonconvex l0 pseudonorm ... More

Stochastic Gradient Descent Escapes Saddle Points EfficientlyFeb 13 2019This paper considers the perturbed stochastic gradient descent algorithm and shows that it finds $\epsilon$-second order stationary points ($\left\|\nabla f(x)\right\|\leq \epsilon$ and $\nabla^2 f(x) \succeq -\sqrt{\epsilon} \mathbf{I}$) in $\tilde{O}(d/\epsilon^4)$ ... More

Distributed Online Linear RegressionFeb 13 2019We study online linear regression problems in a distributed setting, where the data is spread over a network. In each round, each network node proposes a linear predictor, with the objective of fitting the \emph{network-wide} data. It then updates its ... More

Search and Rescue in the Face of Uncertain ThreatsFeb 13 2019We consider a search problem in which one or more targets must be rescued by a search party, or Searcher. The targets may be survivors of some natural disaster, or prisoners held by an adversary. The targets are hidden among a finite set of locations, ... More

The Complexity of Making the Gradient Small in Stochastic Convex OptimizationFeb 13 2019We give nearly matching upper and lower bounds on the oracle complexity of finding $\epsilon$-stationary points ($\| \nabla F(x) \| \leq\epsilon$) in stochastic convex optimization. We jointly analyze the oracle complexity in both the local stochastic ... More

The Complexity of Making the Gradient Small in Stochastic Convex OptimizationFeb 13 2019Feb 14 2019We give nearly matching upper and lower bounds on the oracle complexity of finding $\epsilon$-stationary points ($\| \nabla F(x) \| \leq\epsilon$) in stochastic convex optimization. We jointly analyze the oracle complexity in both the local stochastic ... More

Towards moderate overparameterization: global convergence guarantees for training shallow neural networksFeb 12 2019Many modern neural network architectures are trained in an overparameterized regime where the parameters of the model exceed the size of the training dataset. Sufficiently overparameterized neural network architectures in principle have the capacity to ... More

Measure of quality of finite-dimensional linear systems: A frame-theoretic viewFeb 12 2019A measure of quality of a control system is a quantitative extension of the classical binary notion of controllability. In this article we study the quality of linear control systems from a frame-theoretic perspective. We demonstrate that all LTI systems ... More

Non-local Linearization of Nonlinear Differential Equations via PolyflowsFeb 12 2019Motivated by the mathematics literature on the algebraic properties of so-called polynomial vector flows, we propose a technique for approximating nonlinear differential equations by linear differential equations. Although the idea of approximating nonlinear ... More

Output-Feedback Boundary Control of a Heat PDE Sandwiched Between Two ODEsFeb 12 2019Feb 14 2019We present designs for exponential stabilization of an ODE-heat PDE-ODE coupled system where the control actuation only acts in one ODE. The combination of PDE backstepping and ODE backstepping is employed in a state-feedback control law and in an observer ... More

Output-Feedback Boundary Control of a Heat PDE Sandwiched Between Two ODEsFeb 12 2019We present designs for exponential stabilization of an ODE-heat PDE-ODE coupled system where the control actuation only acts in one ODE. The combination of PDE backstepping and ODE backstepping is employed in a state-feedback control law and in an observer ... More

Boundary null-controllability of two coupled parabolic equations : simultaneous condensation of eigenvalues and eigenfunctionsFeb 12 2019Let the matrix operator L = D$\partial$xx + q(x)A0, with D = diag(1, $\nu$), $\nu$ = 1, q $\in$ L $\infty$ (0, $\pi$), and A0 is a Jordan block of order 1. We analyze the boundary null controllability for system yt -- Ly = 0. When v \notin Q * + and q(x) ... More

Existence of solution for an optimal control problem associated to the Ginzburg-Landau system in superconductivityFeb 12 2019This article develops a global existence result for the solution of an optimal control problem associated to the Ginzburg-Landau system. This main result is based on standard tools of analysis and functional analysis, such as the Friedrichs Curl Inequality ... More

A Problem-Adaptive Algorithm for Resource AllocationFeb 12 2019We consider a sequential stochastic resource allocation problem under the gradient feedback, where the reward of each resource is concave. We construct a generic algorithm that is adaptive to the complexity of the problem, which is measured using the ... More

Benders Decomposition for a Class of Mathematical Programs with Constraints on Dual VariablesFeb 12 2019Interdependent systems with mutual feedback are best represented as a multi-level mathematical programming, in which the leader decisions determine the follower operations, which subsequently affect the leader operations. For instance, a recent paper ... More

Computational Complexity and the Nature of Quantum MechanicsFeb 12 2019Quantum theory (QT) has been confirmed by numerous experiments, yet we still cannot fully grasp the meaning of the theory. As a consequence, the quantum world appears to us paradoxical. Here we shed new light on QT by having it follow from two main postulates ... More

Graver Bases via Quantum Annealing with Application to Non-Linear Integer ProgramsFeb 12 2019We propose a novel hybrid quantum-classical approach to calculate Graver bases, which have the potential to solve a variety of hard linear and non-linear integer programs, as they form a test set (optimality certificate) with very appealing properties. ... More

Network Design for Controllability MetricsFeb 12 2019In this paper, we consider the problem of tuning the edge weights of a networked system described by linear time-invariant dynamics. We assume that the topology of the underlying network is fixed and that the set of feasible edge weights is a given polytope. ... More

Topology Optimization under Uncertainty using a Stochastic Gradient-based ApproachFeb 11 2019Topology optimization under uncertainty (TOuU) often defines objectives and constraints by statistical moments of geometric and physical quantities of interest. Most traditional TOuU methods use gradient-based optimization algorithms and rely on accurate ... More

A central path following method using the normalized gradientsFeb 11 2019Motivated by the applications of the node-based shape optimization problem, where various response evaluations are often considered in constrained optimization, we propose a central path following method using the normalized gradients. There exist numerous ... More

A central path following method using the normalized gradientsFeb 11 2019Feb 12 2019Motivated by the applications of the node-based shape optimization problem, where various response evaluations are often considered in constrained optimization, we propose a central path following method using the normalized gradients. There exist numerous ... More

Dwell-time control sets and applications to the stability analysis of linear switched systemsFeb 11 2019We propose an extension of the theory of control sets to the case of inputs satisfying a dwell-time constraint. Although the class of such inputs is not closed under concatenation, we propose a suitably modified definition of control sets that allows ... More

Efficient Primal-Dual Algorithms for Large-Scale Multiclass ClassificationFeb 11 2019We develop efficient algorithms to train $\ell_1$-regularized linear classifiers with large dimensionality $d$ of the feature space, number of classes $k$, and sample size $n$. Our focus is on a special class of losses that includes, in particular, the ... More

Fast Approximation of Optimal Perturbed Many-Revolution Multiple-Impulse Transfers via Deep Neural NetworksFeb 11 2019The design of perturbed multiple-impulse-based multitarget rendezvous missions requires a method to quickly and accurately approximate the optimal perturbed many-revolution multiple-impulse transfer between any two rendezvous targets. The approximation ... More

Acceleration via Symplectic Discretization of High-Resolution Differential EquationsFeb 11 2019We study first-order optimization methods obtained by discretizing ordinary differential equations (ODEs) corresponding to Nesterov's accelerated gradient methods (NAGs) and Polyak's heavy-ball method. We consider three discretization schemes: an explicit ... More

Hierarchical Feedback Control for Complex Hybrid Models of Multiagent Legged Robotic SystemsFeb 11 2019This paper presents a hierarchical feedback control strategy for complex hybrid systems that represent collaborative multiagent legged robotic systems with arms for manipulating an object. We develop high-dimensional hybrid models, including continuous- ... More

Deducing Kurdyka-Łojasiewicz exponent via inf-projectionFeb 10 2019Kurdyka-{\L}ojasiewicz (KL) exponent plays an important role in estimating the convergence rate of many contemporary first-order methods. In particular, a KL exponent of $\frac12$ is related to local linear convergence. Nevertheless, KL exponent is in ... More

Stochastic Three Points Method for Unconstrained Smooth MinimizationFeb 10 2019Feb 16 2019In this paper we consider the unconstrained minimization problem of a smooth function in ${\mathbb{R}}^n$ in a setting where only function evaluations are possible. We design a novel randomized derivative-free algorithm --- the stochastic three points ... More

Stochastic Three Points Method for Unconstrained Smooth MinimizationFeb 10 2019In this paper we consider the unconstrained minimization problem of a smooth function in ${\mathbb{R}}^n$ in a setting where only function evaluations are possible. We design a novel randomized direct search method based on stochastic three points (STP) ... More

On modeling hard combinatorial optimization problems as linear programs: Refutations of the "unconditional impossibility" claimsFeb 10 2019There has been a series of developments in the recent literature (by essentially a same "circle" of authors) with the absolute/unconditioned (implicit or explicit) claim that there exists no abstraction of an NP-Complete combinatorial optimization problem ... More

Computational Complexity and the Nature of Quantum Mechanics (Extended version)Feb 09 2019Quantum theory (QT) has been confirmed by numerous experiments, yet we still cannot fully grasp the meaning of the theory. As a consequence, the quantum world appears to us paradoxical. Here we shed new light on QT by having it follow from two main postulates ... More

Accelerated Sampling Kaczmarz Motzkin Algorithm for Linear Feasibility ProblemFeb 09 2019The Sampling Kaczmarz-Motzkin (SKM) algorithm is a generalized method for solving large-scale linear system of inequalities. Having its root in the relaxation method of Agmon, Motzkin and the randomized Kaczmarz method, SKM outperforms the state-of-the-art ... More

Synthesis for controllability and observability of logical control networksFeb 09 2019Finite-state systems have applications in systems biology, formal verification and synthesis problems of infinite-state (hybrid) systems, etc. As deterministic finite-state systems, logical control networks (LCNs) consist of a finite number of nodes which ... More

Symmetry-Induced Clustering in Multi-Agent Systems using Network Optimization and PassivityFeb 09 2019This work studies the effects of a weak notion of symmetry on diffusively-coupled multi-agent systems. We focus on networks comprised of agents and controllers which are maximally equilibrium independent passive, and show that these converge to a clustered ... More

Proactive rebalancing and speed-up techniques for on-demand high capacity vehicle poolingFeb 09 2019By proposing speed-up techniques and a proactive rebalance algorithm, we improve the Mobility-on-Demand fleet management approach in [1] on both computational performance and system performance. The speed-up techniques comprise search space pruning and ... More

An Optimal-Storage Approach to Semidefinite Programming using Approximate ComplementarityFeb 09 2019This paper develops a new storage-optimal algorithm that provably solves generic semidefinite programs (SDPs) in standard form. This method is particularly effective for weakly constrained SDPs. The key idea is to formulate an approximate complementarity ... More

Unnormalized Optimal TransportFeb 09 2019We propose an extension of the computational fluid mechanics approach to the Monge-Kantorovich mass transfer problem, which was developed by Benamou-Brenier. Our extension allows optimal transfer of unnormalized and unequal masses. We obtain a one-parameter ... More

Forward-backward-forward methods with variance reduction for stochastic variational inequalitiesFeb 09 2019We develop a new stochastic algorithm with variance reduction for solving pseudo-monotone stochastic variational inequalities. Our method builds on Tseng's forward-backward-forward (FBF) algorithm, which is known in the deterministic literature to be ... More

H2 model reduction of linear network systems by moment matching and optimizationFeb 09 2019In this paper we study the problem of model reduction of linear network systems. We aim at computing a reduced order stable approximation of the network with the same topology and optimal w.r.t. H2 norm error approximation. Our approach is based on time-domain ... More

Worst-case Guarantees for Remote Estimation of an Uncertain SourceFeb 09 2019Consider a remote estimation problem where a sensor wants to communicate the state of an uncertain source to a remote estimator over a finite time horizon. The uncertain source is modeled as an autoregressive process with bounded noise. Given that the ... More

A Differentiable Augmented Lagrangian Method for Bilevel Nonlinear OptimizationFeb 08 2019Many problems in modern robotics can be addressed by modeling them as bilevel optimization problems. In this work, we leverage augmented Lagrangian methods and recent advances in automatic differentiation to develop a general-purpose nonlinear optimization ... More

Linear Dynamics & Control of Brain NetworksFeb 08 2019The brain is an intricately structured organ responsible for the rich emergent dynamics that support the complex cognitive functions we enjoy as humans. With around $10^{11}$ neurons and $10^{15}$ synapses, understanding how the human brain works has ... More

Large deviations and entropy production in viscous fluid flowsFeb 08 2019We study the motion of a particle in a random time-dependent vector field defined by the 2D Navier-Stokes system with a noise. Under suitable non-degeneracy hypotheses we prove that the empirical measures of the trajectories of the pair (velocity field, ... More