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Thursday, November 26, 2020 | History

3 edition of Quadratic optimization in the problems of active control of sound found in the catalog.

Quadratic optimization in the problems of active control of sound

Quadratic optimization in the problems of active control of sound

  • 85 Want to read
  • 40 Currently reading

Published by ICASE, NASA Langley Research Center, For sale by the National Technical Information Service in Hampton, Va, [Springfield, Va .
Written in English

    Subjects:
  • Noise spectra.,
  • Acoustic frequencies.,
  • Noise reduction.,
  • Optimizatioin.,
  • Active control.,
  • Mathematical models.,
  • Numerical analysis.

  • Edition Notes

    StatementJ. Loncaric, S.V. Tsynkov.
    SeriesICASE report -- no. 2002-35., NASA -- CR-2002-211939., NASA contractor report -- NASA CR-211939.
    ContributionsTsynkov, S. V., Institute for Computer Applications in Science and Engineering.
    The Physical Object
    FormatMicroform
    Pagination1 v.
    ID Numbers
    Open LibraryOL16125203M


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Quadratic optimization in the problems of active control of sound Download PDF EPUB FB2

Quadratic optimization in the problems of active control of sound J. Loncariˇ c´ a,1,vb ∗ a Los Alamos National Laboratory, MS-D, P.O. BoxLos Alamos, NMUSA b Department of Mathematics and Center for Research in Scientific Computation (CRSC), North Carolina State University, BoxRaleigh, NCUSA Abstract.

Quadratic Optimization in the Problems of Active Control of Sound J. Loncaric ICASE, Hampton, Virginia S.V. Tsynkov North Carolina State University, Raleigh, North Carolina and Tel Aviv University, Tel Aviv, Israel October The NASA STI Program Office in Profile.

We analyze the problem of suppressing the unwanted component of a time-harmonic acoustic field (noise) on a predetermined region of interest.

An illustration of an open book. Books. An illustration of two cells of a film strip. Video Quadratic Optimization in the Problems of Active Control of Sound.

Get this from a library. Quadratic optimization in the problems of active control of sound. [J Lončarić; Semyon V Tsynkov; Institute for Computer Applications in Science and Engineering.]. Request PDF | Quadratic optimization in the problems of active control of sound | We analyze the problem of suppressing the unwanted component of a time-harmonic acoustic field (noise) on a.

CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We analyze the problem of suppressing the unwanted component of a time-harmonic acoustic #eld #noise# on a predetermined region of interest. The suppression is rendered by active means, i.e., by introducing the additional acoustic sources called controls that generate the appropriate anti-sound.

BibTeX @MISC{A_quadraticoptimization, author = {J. Lončarić A and S. Tsynkov B}, title = {Quadratic optimization in the problems of active control of sound }, year = {}}. The material in the rest of the paper is organized as follows. In Section 2, we introduce and discuss general solutions for controls in the continuous and discrete framework.

Section 3 is devoted to the formulation and solution of the quadratic optimization problems for the control sources (unconstrained and constrained L 2 optimization). Lončarić, J., Tsynkov, S.V.: Quadratic optimization in the problems of active control of sound.

Technical ReportNASA/CR, ICASE, Hampton, VA () Also submitted to SIAM J. Applied Math. Google Scholar. A problem of eliminating the unwanted time-harmonic noise on a predetermined region of interest is solved by active means, i.e., by introducing the additional sources of sound, called controls.

We consider a problem of eliminating the unwanted time-harmonic noise on a predetermined region of interest. The desired objective is achieved by active means, i.e., by introducing additional sources of sound called control sources, which generate the appropriate annihilating acoustic signal (antisound).

A general solution for the control sources has been obtained previously in both. Quadratic Optimization Problems Quadratic Optimization: The Positive Definite Case In this chapter, we consider two classes of quadratic opti-mization problems that appear frequently in engineering and in computer science (especially in computer vision): 1.

Minimizing f(x)= 1 2 x￿Ax+x￿b over all x ∈ Rn,orsubjecttolinearoraffinecon. 1 Quadratic Optimization A quadratic optimization problem is an optimization problem of the form: (QP): minimize f (x):=1 xT Qx + c xT 2 s.t. x ∈ n. Problems of the form QP are natural models that arise in a variety of settings.

For example, consider the problem of approximately solving. Journal of Sound and Vibration. VolumeIssue 5, 11 MarchPages Regular article. A QUADRATIC PROGRAMMING APPROACH TO THE DESIGN OF ACTIVE–PASSIVE VIBRATION ISOLATION SYSTEMS.

Author links open overlay panel D.J. Leo D.J. Inman. Show more. CHAPTER QUADRATIC OPTIMIZATION PROBLEMS Quadratic Optimization: The General Case In this section, we complete the study initiated in Section and give necessary and sucient conditions for the quadratic function 1 2x >Ax + x b to have a global mini-mum.

We begin with the following simple fact: Proposition If A is an. The classical approach, which involves reducing (2) to a quadratic programming problem, is detailed below. Then, more recent approaches such as sub-gradient descent and coordinate descent will be discussed. Primal. Minimizing (2) can be rewritten as a constrained optimization problem with a differentiable objective function in the following way.

Active set methods are designed to make an intelligent guess of the active set of constraints, and to modify this guess at each iteration.

Herein we describe a relatively simple active-set method that can be used to solve quadratic optimization problems. Consider a quadratic optimization problem in the format: QP: minimize x xT Qx + c xT s.t. In this paper, we provide two types of sufficient conditions for ensuring the quadratic growth conditions of a class of constrained convex symmetric and nonsymmetric matrix optimization problems regularized by nonsmooth spectral functions.

solution for the QP (convex optimization problem); otherwise, the solution of the PCG solver needs to be veri ed. Special case: when Q is positive de nite and A has full rank (rank(A) = m1), the matrix Q A> A O is invertible. I small-scale quadratic optimization problems can be solved directly to obtain the solution) x = Q 1q + Q 1A> AQ 1A> 1 b.

Quadratic programming (QP) is the process of solving certain mathematical optimization problems involving quadratic ically, one seeks to optimize (minimize or maximize) a multivariate quadratic function subject to linear constraints on the variables.

Quadratic programming is a type of nonlinear programming. "Programming" in this context refers to a formal procedure for solving. The basic topic of this book is solving problems from system and control theory using convex optimization. We show that a wide variety of problems arising in system and control theory can be reduced to a handful of standard convex and quasiconvex optimization problems that involve matrix inequalities.

For a few special cases there. Keyterms—Reactive Power Optimization, Optimal Power Flow, Active Power Loss, Sequential Quadratic Programming. I INTRODUCTION. The OPF problem was defined in the early ’s [1] as an extension of conventional economic dispatch to determine the optimal control settings in a power system networks with various constraints [2].

Quadratic programming problems (QPs) that arise from dynamic optimization problems typically exhibit a very particular structure. We address the ubiquitous case where these QPs are strictly convex and propose a dual Newton strategy that exploits the block-bandedness similarly to an interior-point method.

Still, the proposed method features warmstarting capabilities of active-set methods. the optimal solution, then one can use the active constraints to reduce the number of unknowns, and then perform algorithms for unconstrained optimization problems.

Problem: how to guess the set of active constraints. Linear programming is an active set method. (UNIT 8) Numerical Optimization. Graphing Quadratic Functions Axis of Symmetry, Vertex & Standard Form, X Y Intercepts, Word Problems - Duration: The Organic Chemistry Tutorviews Asymptotic approximation to a solution of a singularly perturbed linear-quadratic optimal control problem with second-order linear ordinary differential equation of state variable Nguyen Thi Hoai doi: /naco + [Abstract] () + [HTML] (90) + [PDF] (KB).

It uses an object-oriented approach to define and solve various optimization tasks from different problem classes (e.g., linear, quadratic, non-linear programming problems).

This makes optimization transparent for the user as the corresponding workflow is abstracted from the underlying solver. Quadratic Programming Introduction Quadratic programming maximizes (or minimizes) a quadratic objective function subject to one or more constraints. The technique finds broad use in operations research and is occasionally of use in statistical work.

The mathematical representation of the quadratic programming (QP) problem is Maximize. Solving optimization problems in complex systems often requires the implementation of advanced mathematical techniques.

Quadratic programming (QP) is one technique that allows for the optimization of a quadratic function in several variables in the presence of linear constraints. QP problems arise. Optimization I; Chapter 3 56 Chapter 3 Quadratic Programming Constrained quadratic programming problems A special case of the NLP arises when the objective functional f is quadratic and the constraints h;g are linear in x 2 lRn.

Such an NLP is called a Quadratic Programming (QP) problem. Its general form is minimize f(x):= 1 2 xTBx ¡ xTb.

Active Control with the Method of Receptances: Recent Progresses and Its Application in Active Aeroelastic Control A multi-step method for partial quadratic pole assignment problem with time delay.

Applied Mathematics and Computation, Vol. Optimization methods for partial quadratic eigenvalue assignment in vibrations. "A book about turning high-degree optimization problems into quadratic optimization problems that maintain the same global minimum (ground state).

This book explores quadratizations for pseudo-Boolean optimization, perturbative gadgets used in QMA completeness theorems, and also non-perturbative k-local to 2-local transformations used for.

part 2, lesson 2 (Quadratic Functions and Revenue Optimization Problems) - Duration: Quadratic Optimization - Duration: One SIMPLE Trick to Sound.

min: x'Sx s.t.: x'a >= g x'1 = 0 x >= -Wb x active weights of assets (active weight = portfolio weight - benchmark weight) S: covariance matrix of asset returns a: expected stock excess returns g: target gain Wb: weights of assets in the benchmark c:. control[ The current work extends these studies to include vibration due to sudden mass imbalance plus the application of a constrained quadratic approach to active balancing[The in~uence balancing theory for uncoupled\ coupled\ least squares and constrained quadratic problems is described in section 2[ The experimental set!up used for.

Optimal ODE control problems with control and state constraints are solved as NLPs with NPSOL or SNOPT: PAREST: A direct multiple shooting method for the numerical solution of optimal control and parameter estimation problems: Biomimikry: Matlab code for Optimization, Control, and Automation: OCS: SciLab/Maple based to solve unconstrained ODE.

technique for the OPF problem is studied in the recent work (Q. Jiang and Cao, ). In an effort to convexify the OPF problem, it is shown in (Jabr, ) that the load flow problem of a radial distribution system can be modeled as a convex optimization problem in the form of a conic program.

Nonetheless, the results fail to hold. Quadratic programming (QP) is minimizing or maximizing an objective function subject to bounds, linear equality, and inequality constraints.

Example problems include portfolio optimization in finance, power generation optimization for electrical utilities, and design optimization in engineering. Quadratic programming is the mathematical problem of finding a vector x that minimizes a quadratic.

Constrained Optimization of Quadratic Forms One of the most important applications of mathematics is optimization, and you have some experience with this from calculus. In these notes we’re going to use some of our knowledge of quadratic forms to give linear-algebraic solutions to some optimization problems.

A multiobjective optimization procedure is proposed to deal with the optimal number and locations of collocated/noncollocated sensors and actuators and determination of LQR controller gain simultaneously using hybrid multiobjective genetic algorithm-artificial neural network (GA-ANN).

Multiobjective optimization problem has been formulated using trade-off objective functions ensuring good. Mathematical optimization (alternatively spelled optimisation) or mathematical programming is the selection of a best element (with regard to some criterion) from some set of available alternatives.

Optimization problems of sorts arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has .The optimal control signal is derived in a feedback form by using Krotov's method.

To this end, a novel sequence of Krotov functions which suits the multi-input constrained bilinear-quadratic regulator problem is formulated by means of quadratic form and differential Lyapunov equations.

An algorithm is proposed for the optimal control computation.The answer strongly depends on the size of your problem, and the convexity of the quadratic functions. If your problems are convex and you like using python, you can use cvxmod for free. For mathematical material, there's also the convex optimization book, freely available.