We propose a fast algorithm for solving the Basis Pursuit problem, minu$\{|u|_1\: \Au=f\}$, which has application to compressed sensing.We design an efficient method related unconstrained problem minu $E(u) = |u|_1 + \lambda \||Au-f\||^2_2$ based on greedy coordinate descentmethod. claim that in combination with Bregman iterative method, ouralgorithm will achieve solution speed and accuracy competitive someof leading methods basis pursuit problem.

Orthogonal frequency division multiplexing (OFDM) is a technique that will prevail in the next-generation wireless communication. Channel estimation one of key challenges OFDM, since high-resolution channel can significantly improve equalization at receiver and consequently enhance communication performances. In this paper, we propose system with an asymmetric digital-to-analog converter/analog-to-digital converter (DAC/ADC) pair formulate OFDM as compressive sensing problem. By skillfully...

This article considers a distributed reinforcement learning problem for decentralized linear quadratic (LQ) control with partial state observations and local costs. We propose zero-order policy optimization algorithm (ZODPO) that learns controllers in fashion, leveraging the ideas of gradient, optimization, consensus algorithms. In ZODPO, each agent estimates global cost by consensus, then conducts gradient parallel based on estimation. ZODPO only requires limited communication storage even...

This paper studies an online optimization problem with switching costs and a finite prediction window. We propose computationally efficient algorithm, Receding Horizon Gradient Descent (RHGD), which only requires number of gradient evaluations at each time. show that both the dynamic regret competitive ratio algorithm decay exponentially fast length Moreover, we provide fundamental lower bound on for general algorithms window, any even more computation, decays most when increasing window...

This article considers online optimization with a finite prediction window of cost functions and additional switching costs on the decisions. We study fundamental limits dynamic regret any algorithm for both with-prediction no-prediction cases. Besides, we propose two gradient-based algorithms: receding horizon gradient descent (RHGD) accelerated (RHAG); provide their upper bounds. RHAG's bound is close to lower bound, indicating tightness our that RHAG near-optimal. Finally, conduct...

Motivated by the reality that humans tend to convey their views using natural language, which is always indeterminate, imprecise, incomplete, and inconsistent, this paper introduces concept of linguistic neutrosophic sets (LNSs), in truth-membership, falsity-membership, indeterminacy-membership are represented as terms. In order compare any two numbers (LNNs), defines expected function, accuracy certainty function. Subsequently, operations for LNNs provided based on scale functions. Then,...

This paper studies the online optimal control problem with time-varying convex stage costs for a time-invariant linear dynamical system, where finite lookahead window of accurate predictions are available at each time. We design algorithms, Receding Horizon Gradient-based Control (RHGC), that utilize through steps gradient computations. study algorithm performance measured by dynamic regret: minus in hindsight. It is shown regret RHGC decays exponentially size window. In addition, we provide...

We consider the inverse problem of finding sparse initial data from thesparsely sampled solutions heat equation. The are assumedto be a sum an unknown but finite number Dirac delta functions at locations.Point-wise values solution only few locations used in an$l_1$ constrained optimization to find data. A concept ofdomain effective sensing is introduced speed up already fast Bregmaniterative algorithm for $l_1$ optimization. Furthermore, whichsuccessively adds new measurements specially...

This paper studies an online optimization problem with a finite prediction window of cost functions and additional switching costs on decisions. We propose two gradient-based algorithms: Receding Horizon Gradient Descent (RHGD), Accelerated (RHAG). Both algorithms only require number projected gradient evaluations at each stage. provide upper bounds the dynamic regrets proposed show that regret decay exponentially length window. Moreover, we study fundamental lower bound for broad class...

This paper studies the automated control method for regulating air conditioner (AC) loads in incentive-based residential demand response (DR). The critical challenge is that customer responses to load adjustment are uncertain and unknown practice. In this paper, we formulate AC problem a DR event as multi-period stochastic optimization integrates indoor thermal dynamics opt-out status transition. Specifically, machine learning techniques including Gaussian process logistic regression...

In this paper, we study the dynamic regret of online linear quadratic regulator (LQR) control with time-varying cost functions and disturbances. We consider case where a finite look-ahead window disturbances are available at each stage. The algorithm studied in paper falls into category model predictive (MPC) particular choice terminal costs to ensure exponential stability. It is proved that, when predictions accurate, such an decays exponentially fast length predictions. impact inaccurate...

In this paper, we consider residential demand response (DR) programs where an aggregator calls upon some customers to change their so that the total load adjustment is as close a target value possible. Major challenges lie in uncertainty and randomness of customer behaviors DR signals, limited knowledge available customers. To learn select right customers, formulate problem combinatorial multi-armed bandit (CMAB) with reliability goal. We propose learning algorithm: CUCB-Avg (Combinatorial...

A new exact solution of the Helmholtz equation is obtained in closed form for case a point source layered medium with refractive index variation n=(1+Az)1/2. The by starting onefold integral representation that was derived Holford [J. Acoust. Soc. Am. 70, 1427–1436 (1981)] and Jeng Liu 81, 1732–1740 (1987)]. particularly interesting because classical ray picture exhibits caustic therefore shadow zone formed unbounded medium. power flux due to such computed flow energy into region beyond...

This paper considers online optimal control with affine constraints on the states and actions under linear dynamics bounded random disturbances. The system are assumed to be known time invariant but convex stage cost functions change adversarially. To solve this problem, we propose Online Gradient Descent Buffer Zones (OGD-BZ). Theoretically, show that OGD-BZ proper parameters can guarantee satisfy all despite any admissible Further, investigate policy regret of OGD-BZ, which compares...

In this paper, a very simple method is introduced for the efficient computation of sound field in near region above an impedance ground or extended reaction ground. The procedure based on complex image theory. Using Prony’s method, slowly varying factor integrand Sommerfeld integral approximated by short series terms exponential functions. result can be exactly evaluated identity. A new closed-form approximate expression obtained, which represented as original source, its quasistatic image,...

Two new fast field programs (FFP) have been developed for numerical computation of anisotropic sound propagation through an atmosphere with a wind velocity profile. The first FFP can be used to compute the near-field and far-field pressure. implementation is based on integration algorithm using two-dimensional Fourier transform iterative refinement. For studying problems long-range propagation, novel expression pressure derived. second this approximation. If magnitude speed not very large;...

In an online Markov decision process (MDP) with time-varying reward functions, a maker has to take action before knowing the current function at each time step. This problem received many research interests because of its wide range applications. The literature usually focuses on static regret analysis by comparing total optimal offline stationary policy and that policies. paper studies different measure, dynamic regret, which is difference between (possibly nonstationary) policies measure...

Orthogonal frequency division multiplexing (OFDM) is a technique that will prevail in the next generation wireless communication. Channel estimation one of key challenges an OFDM system. In this paper, we formulate channel as compressive sensing problem, which takes advantage sparsity impulse response and reduces number probing measurements, turn ADC speed needed for estimation. Specifically, propose sending out pilots with random phases order to ``spread out" sparse taps over uniformly...