Holomorphic Functions and the Wirtinger Derivative

Total Page:16

File Type:pdf, Size:1020Kb

Holomorphic Functions and the Wirtinger Derivative Ioakim I. Ioannis [email protected] Holomorphic functions and the Wirtinger derivative Definition 0.1 (Holomorphic function). If f is (complex) differentiable at every point z0 of U⊆C open, we say that f is holomorphic on U. If f is (complex) differentiable on some (open) neigh- bourhood of z0, then we say that f is holomorphic at z0. The word ’holomorphic’ was introduced by two of Cauchy’s students, Briot and Bouquet and derives from the Greek wordsoλoς ´ (holos) meaning ’entire’ or ’whole’, and µo̺ϕη´ (morph¯e) meaning ’form’ or ’shape’. We shall see later on where this terminology stems from. Remarks 1. It follows immediately that if f is differentiable at z0, then it is continuous at z0. 2. Complex differentiability (or holomorphicity) if often referred to as ’analyticity’. This is due to the fact, as we shall see later, that a holomorphic function can be expanded locally as a (convergent) series, hence the term ’analyticity’. The main tool for proving such a powerful result is Cauchy’s Integral formula. Nevertheless, there exists a proof that holomorphicity implies C∞ without the help of Cauchy’s Integral formula. It is based on a topological result by G. T. Whyburn (namely, if f is differentiable and non-constant in a region D of the complex plane, then f is an open map). The proof is presented in [9]. Since in what follows we will study holomorphicity under the scope of differentiability, it is useful that we keep this fact in mind. Definition 0.2 (Entire function). A function that is holomorphic on the entire complex plane is called an entire function. We shall denote O(U) the set of all holomorphic functions on U : O(U) = {f : U → C : f isholomorphic}. Examples 1. The function f(z) = z, z ∈ C is nowhere differentiable. Lets first proof that f ′(0) does not exist. We have that f(h) − f(0) h ∆x − i∆y lim = lim = lim (1.1) h→0 h h→0 h ∆x+i∆y→0 ∆x + i∆y Ioakim Ioannis | [email protected] | [email protected] 1 Complex analysis (where we’ve written h = ∆x + i∆y). If we restrict ourselves in the case were h → 0 along the x−axis, i.e. ∆x → 0, then ∆y = 0 and the above limit then becomes ∆x lim =1 ∆x→0 ∆x and similarly, if we restrict ourselves in the case were h → 0 along the y−axis, i.e. ∆y → 0, then ∆x = 0 and the above limit then becomes −i∆y lim = −1. ∆y→0 i∆y Thus, since we approached 0 in two different directions and found the corresponding limits different, we conclude that the limit (1.1) does not exist. For z 6=0, we could follow the same iarg(z0) steps as above, but it would perhaps be easier to write z in its polar form: z0 = |z0|e . Then, f(z + h) − f(z ) z + h − z z + h − z lim 0 0 = lim 0 0 = lim 0 0 h→0 h h→0 h h→0 h −iarg(z0) h |z0|e −2iarg(z0) = lim = lim iarg(z ) = lim e (1.2) h→0 h h→0 |z0|e 0 h→0 If arg(z0)=0or= π, i.e. h → 0 along the x−axis, then 2iarg(z0) = 1 and thus (1.2) equals π 3π to 1. Similarly, if arg(z0)= 2 or = 2 , i.e. h → 0 along the y−axis, then 2iarg(z0)= −1 and thus (1.2) equals to -1. The result follows. Note that since there cannot be an open set around (0, 0) such that f is (complex) differentiable, then f is not holomorphicat (0, 0). 2. Let 2 z z 6=0 f(z)= z (1.3) (0 z =0 2 C z2 |z| Obviously, f is continuous in \{0}. Since for z 6=0, |f(z)| = z = |z| = |z|, we have that f(z) → 0= f(0) as z → 0, thus f is continuous at the origin also. f is (complex) differentiable at the origin: f(h) − f(0) |h|2 lim = lim = lim h =0. h→0 h h→0 h h→0 Thus, f ′(0) = 0. Viewing the above limit in x, y coordinates, we have |h|2 lim = lim (x − iy)=0. h=(x,y)→(0,0) h (x,y)→(0,0) Now, for z 6=0, we have f(z + h) − f(z) |z + h|2 − |z|2 zh + zh lim = lim = lim h→0 h h→0 h h→0 h 2 Ioakim Ioannis | [email protected] | [email protected] Complex analysis h which limit does not exist, since the limh→0 h does not exist. Otherwise, using x, y coordinates: • Let z → 0 along the x− axis (y = 0): x3 f(x +0i) − f(0) 2 lim = lim x =1 (x,0)→(0,0) (x +0i) − 0 (x,0)→(0,0) x • Let z → 0 along the diagonal (y = x): the diagonal is parameterized by x = t, y = x = t and thus f(t + ti) − f(0) −t(t + i) lim = lim = −1 (t,t)→(0,0) (t + ti) − 0 t→0 t(t + i) Therefore, f is not differentiable at z 6= 0 in the complex sense. 1.1. The Cauchy-Riemann equations We shall now establish the relationship between the (complex) differentiability of a function f = u+iv and the partial derivatives of u and v. Let f the function defined in ((1.3)). Viewing f as a function from R2 to R, f is written as f(x, y)= x2 + y2 and since the partial derivatives of f are continuous functions all over R2, we conclude that f is differentiable (in R2). But as we saw earlier, f is (complex) differentiable only at the origin. Thus, the differentiability of u and v does not imply the (complex) differentiability of f = u + iv. Also consider the function f(z)=z, ¯ z ∈ C. Write f(x + iy) = x − iy. Then the partial derivatives of all orders of u(x, y) = x and v(x, y) = −y exist but, as we already proved, f is nowhere differentiable. The following Theorem states that the opposite is true: differentiability of f = u + iv implies (partial) differentiability of u and v. Recall Definition 1.1 (Smooth Functions). Let U ⊆ R2 open and f : U → R a continuous function. We say that f is of class Ck (or f is k− continuously differentiable, or simply f is Ck) if the partial derivatives of f up to order k and including order k exist and are continuous (functions) on U, (where k ∈ N ∪{0}). In this case, we write f ∈ Ck(U). The space C0(U) is just the space of all continuous functions on U, the space C1(U) is the ∂f ∂f 2 space of all differentiable functions on U such that ∂x and ∂y are smooth, C (U) is the space of ∂f ∂f ∂f 2 ∂f 2 all differentiable functions on U such that ∂x , ∂y ∂x∂y and ∂y∂x are smooth and so on. The space C∞(U) is the space of all functions infinitely differentiable on U. At this point, lets recall some basic facts about multivariate calculus, restricting ourselves mainly in the bivariate case. 1. Separately continuous 6⇒ continuity. It is possible that a function f of two real variables x, y is separately continuous in x, y, at some point (x0,y0) of its domain, but not continuous at the point. Consider for example the function xy , (x, y) 6= (0, 0) f(x, y)= x2+y2 (0, (x, y)=(0, 0) We can easily verify that f is continuous as well as separately continuous on R2 \{(0, 0)}. Since limx→0 f(x, y) = limy→0 f(x, y)=0= f(x, y), we conclude that f is separately continuous at Ioakim Ioannis | [email protected] | [email protected] 3 Complex analysis the point (0, 0) also. But f is not continuous at (0, 0), for it where, the lim(x,y)→(0,0) f(x, y) would equal to f(0, 0) = 0 on every direction. This is not the case on the diagonal {y = x} : t2 1 lim f(x, y) = lim = . (t,t)→(0,0) t→0 t2 + t2 2 Here is a plot for the surface f(x, y) = 0 : ·10−2 1 0 2 −1 0 −2 0 2 −2 y x 2. Existence of partial derivatives 6⇒ differentiability. It is possible that both partial derivatives of a function f of two real variables x, y exist at some point of its domain, but it is not differentiable there. See the previous example. 3. Existence of partial derivatives, function is continuous 6⇒ differentiability. Example of a function of two real variables x, y where the partial derivatives exist at some point of its domain, is also continuous at that point, but it is not differentiable there: 1 f(x, y) = (xy) 3 . We immediately see that for (x, y) 6= (0, 0) 1 1 ∂f(x, y) y 3 ∂f(x, y) x 3 = 2 and (by symmetry) = 2 ∂x 3x 3 ∂y 3y 3 ∂f(x,0) ∂f(x,0) Since f(x, 0)=0, for x 6=0, ∂x =0, (for x 6= 0) but ∂y does not exist (verify this). By symmetry, the similar results hold if we interchange x and y. Now, since the restriction of f ∂f(0,0) ∂f(0,0) along the x− axis (or the y axis) is identically zero, the partial derivatives ∂x and ∂y exist and are both equal to 0.
Recommended publications
  • Why Wirtinger Derivatives Behave So Well
    Why Wirtinger derivatives behave so well Bart Michels∗ April 14, 2019 Contents 1 Introduction 2 2 Partial derivatives revisited 2 3 Complexification 3 3.1 Complexified differentials: two variables . .4 3.2 Complexified differentials: multiple variables . .4 3.3 Conjugation . .5 4 Chain rule 7 5 Holomorphic functions 10 6 Power series 11 7 Concluding remarks 12 ∗Universit´eParis 13, LAGA, CNRS, UMR 7539, F-93430, Villetaneuse, France. Email: [email protected] 1 1 Introduction Given a smooth function f : C ! C, it makes a priori no sense to talk about @f @f the Wirtinger derivatives and . We could define them by @z @z¯ @f 1 @f @f = − i @z 2 @x @y (1) @f 1 @f @f = + i @z¯ 2 @x @y but that does not explain why we denote them like partial derivatives, where the confusing sign convention comes from, why they behave like partial derivatives, and why they act on power series in z and z in the way they should. Here follows a construction of Wirtinger derivatives that avoids the explicit formula (1). The reader may be interested in the definitions in section 3, in the sanity check in Proposition 3.2, the chain rule from Proposition 4.3 and the results from section 6. 2 Partial derivatives revisited It is worthwhile to take a second look at what a partial derivative is. Con- sider finite-dimensional real vector spaces V and W .1 They carry a canonical smooth structure. Consider a differentiable function f : V ! W . At every point p 2 V , f has a differential Dpf : V ! W , an R-linear map.
    [Show full text]
  • Wirtinger Kalman Overdrive
    Wirtinger Kalman Overdrive O. Smirnov (Rhodes & SKA SA) C. Tasse (Obs. Paris Meudon & Rhodes) The 3GC Culture Wars Two approaches to dealing with DD effects The “NRAO School”: Represent everything by the A-term Correct during imaging (convolutional gridding) Solve for pointing offsets Sky models are images The “ASTRON School”: Solve for DD gains towards (clusters of) sources Make component sky models, subtract sources in uv-plane while accounting for DD gains O. Smirnov & C. Tasse - Wirtinger Kalman Overdrive - SPARCS 2015 2 Why DD Gains Cons: non-physical, slow & expensive But, DD+MeqTrees have consistently delivered the goods with all major pathfinders Early LOFAR maps and LOFAR EoR (S. Yatawatta) Beautiful ASKAP/BETA maps (I. Heywood) JVLA 5M+ DR (M. Mitra earlier) Fair bet that we'll still be using them come MeerKAT and SKA O. Smirnov & C. Tasse - Wirtinger Kalman Overdrive - SPARCS 2015 3 DD Gains Are Like Whiskey The smoother the better Make everything look more attractive If you overindulge, you wake in in the morning wondering where your {polarized foregrounds, weak sources, science signal} have gotten to differential source gain & bandpass gain beam coherency (s) (s) (s)H (s)H H Vpq= G⏞p ∑ ⏞Δ Ep E⏞p ⏞Xpq Eq Δ Eq Gq ⏟( s ) sum over sources O. Smirnov & C. Tasse - Wirtinger Kalman Overdrive - SPARCS 2015 4 The One True Way O. Smirnov & C. Tasse - Wirtinger Kalman Overdrive - SPARCS 2015 5 The Middle Way DR limited by how well we can subtract the brighter source population thus bigger problem for small dish/WF Subtract the first two-three orders of magnitude in the uv-plane good source modelling and (deconvolution and/or Bayesian) PB models, pointing solutions, +solvable DD gains Image and deconvolve the rest really well A-term and/or faceting O.
    [Show full text]
  • Blind Demixing Via Wirtinger Flow with Random Initialization
    Blind Demixing via Wirtinger Flow with Random Initialization Jialin Dong Yuanming Shi [email protected] [email protected] ShanghaiTech University ShanghaiTech University Abstract i = 1; ··· ; s, i.e., s This paper concerns the problem of demixing X \ \ a series of source signals from the sum of bi- yj = bj∗hixi∗aij; 1 ≤ j ≤ m; (1) linear measurements. This problem spans di- i=1 verse areas such as communication, imaging N K processing, machine learning, etc. However, where faijg 2 C , fbjg 2 C are design vectors. semidefinite programming for blind demixing Here, the first K columns of the matrix F form the ma- m K m m is prohibitive to large-scale problems due to trix B := [b1; ··· ; bm]∗ 2 C × , where F 2 C × high computational complexity and storage is the unitary discrete Fourier transform (DFT) ma- cost. Although several efficient algorithms \ trix with FF ∗ = Im. Our goal is to recover fxig and have been developed recently that enjoy the \ fhig from the sum of bilinear measurements, which is benefits of fast convergence rates and even known as blind demixing [1, 2]. regularization free, they still call for spec- tral initialization. To find simple initial- This problem has spanned a wide scope of applications ization approach that works equally well as ranging from imaging processing [3, 4] and machine spectral initialization, we propose to solve learning [5, 6] to communication [7, 8]. Specifically, blind demixing problem via Wirtinger flow by solving the blind demixing problem, both original with random initialization, which yields a images and corresponding convolutional kernels can be natural implementation.
    [Show full text]
  • LOCAL WELL-POSEDNESS for the QUASI-LINEAR HAMILTONIAN SCHRÖDINGER EQUATION on TORI Roberto Feola, Felice Iandoli
    LOCAL WELL-POSEDNESS FOR THE QUASI-LINEAR HAMILTONIAN SCHRÖDINGER EQUATION ON TORI Roberto Feola, Felice Iandoli To cite this version: Roberto Feola, Felice Iandoli. LOCAL WELL-POSEDNESS FOR THE QUASI-LINEAR HAMIL- TONIAN SCHRÖDINGER EQUATION ON TORI. 2020. hal-02504490v2 HAL Id: hal-02504490 https://hal.archives-ouvertes.fr/hal-02504490v2 Preprint submitted on 16 Mar 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. LOCAL WELL-POSEDNESS FOR THE QUASI-LINEAR HAMILTONIAN SCHRODINGER¨ EQUATION ON TORI ROBERTO FEOLA AND FELICE IANDOLI ABSTRACT. We prove a local in time well-posedness result for quasi-linear Hamiltonian Schrodinger¨ equa- d s tions on T for any d ≥ 1. For any initial condition in the Sobolev space H , with s large, we prove the existence and unicity of classical solutions of the Cauchy problem associated to the equation. The lifespan of such a solution depends only on the size of the initial datum. Moreover we prove the continuity of the solution map. CONTENTS 1. Introduction . 1 2. Functional setting . 3 3. Paralinearization of NLS . 13 4. Basic energy estimates . 15 5. Proof of the main Theorem 1.2 .
    [Show full text]
  • Phase Retrieval Via Wirtinger Flow: Theory and Algorithms
    Phase Retrieval via Wirtinger Flow: Theory and Algorithms Emmanuel J. Cand`es∗ Xiaodong Li† Mahdi Soltanolkotabi‡ July 3, 2014; Revised January 2015 Abstract We study the problem of recovering the phase from magnitude measurements; specifically, we wish to reconstruct a complex-valued signal x ∈ Cn about which we have phaseless samples of the 2 form yr = S⟨ar; x⟩S , r = 1; : : : ; m (knowledge of the phase of these samples would yield a linear system). This paper develops a non-convex formulation of the phase retrieval problem as well as a concrete solution algorithm. In a nutshell, this algorithm starts with a careful initialization obtained by means of a spectral method, and then refines this initial estimate by iteratively applying novel update rules, which have low computational complexity, much like in a gradient descent scheme. The main contribution is that this algorithm is shown to rigorously allow the exact retrieval of phase information from a nearly minimal number of random measurements. Indeed, the sequence of successive iterates provably converges to the solution at a geometric rate so that the proposed scheme is efficient both in terms of computational and data resources. In theory, a variation on this scheme leads to a near-linear time algorithm for a physically realizable model based on coded diffraction patterns. We illustrate the effectiveness of our methods with various experiments on image data. Underlying our analysis are insights for the analysis of non- convex optimization schemes that may have implications for computational problems beyond phase retrieval. 1 Introduction We are interested in solving quadratic equations of the form 2 yr = S⟨ar; z⟩S ; r = 1; 2; : : : ; m; (1.1) n n where z ∈ C is the decision variable, ar ∈ C are known sampling vectors, and yr ∈ R are observed measurements.
    [Show full text]
  • An Introduction to Complex Differentials and Complex Differentiability
    An Introduction to Complex Differentials and Complex Differentiability Raphael Hunger Technical Report TUM-LNS-TR-07-06 2007 Technische Universitat¨ Munchen¨ Associate Institute for Signal Processing Prof. Dr.-Ing. Wolfgang Utschick Contents 1. Introduction 3 2. Complex Differentiability and Holomorphic Functions 4 3. Differentials of Analytic and Non-Analytic Functions 8 4. Differentials of Real-Valued Functions 11 5. Derivatives of Functions of Several Complex Variables 14 6. Matrix-Valued Derivatives of Real-Valued Scalar-Fields 17 Bibliography 20 2 1. Introduction This technical report gives a brief introduction to some elements of complex function theory. First, general definitions for complex differentiability and holomorphic functions are presented. Since non-analytic functions are not complex differentiable, the concept of differentials is explained both for complex-valued and real-valued mappings. Finally, multivariate differentials and Wirtinger derivatives are investigated. 3 2. Complex Differentiability and Holomorphic Functions Complex differentiability is defined as follows, cf. [Schmieder, 1993, Palka, 1991]: Definition 2.0.1. Let A ⊂ C be an open set. The function f : A ! C is said to be (complex) differentiable at z0 2 A if the limit f(z) − f(z ) lim 0 (2.1) ! z z0 z − z0 exists independent of the manner in which z ! z . This limit is then denoted by f 0(z ) = df(z) 0 0 dz z=z0 and is called the derivative of f with respect to z at the point z . 0 A similar expression for (2.1) known from real analysis reads as df(z) f(z + ∆z) − f(z) = lim ; (2.2) dz ∆z!0 ∆z where ∆z 2 C now holds.
    [Show full text]
  • Radio Interferometric Gain Calibration As a Complex Optimization Problem 3 from Which Definitions of the Complex Jacobian and Complex Laurent Et Al
    A Mon. Not. R. Astron. Soc. 000, 1–18 (2014) Printed 26 February 2015 (MN LTEX style file v2.2) Radio interferometric gain calibration as a complex optimization problem O.M. Smirnov12⋆, C. Tasse31 1Department of Physics and Electronics, Rhodes University, PO Box 94, Grahamstown, 6140 South Africa 2SKA South Africa, 3rd Floor, The Park, Park Road, Pinelands, 7405 South Africa 3GEPI, Observatoire de Paris, CNRS, Université Paris Diderot, 5 place Jules Janssen, 92190 Meudon, France Accepted 2015 February 24. Received 2015 February 13; in original form 2014 October 30 ABSTRACT Recent developments in optimization theory have extended some traditional al- gorithms for least-squares optimization of real-valued functions (Gauss-Newton, Levenberg-Marquardt, etc.) into the domain of complex functions of a complex vari- able. This employs a formalism called the Wirtinger derivative, and derives a full- complex Jacobian counterpart to the conventional real Jacobian. We apply these de- velopments to the problem of radio interferometric gain calibration, and show how the general complex Jacobian formalism, when combined with conventional optimization approaches, yields a whole new family of calibration algorithms, including those for the polarized and direction-dependent gain regime. We further extend the Wirtinger calculus to an operator-based matrix calculus for describing the polarized calibration regime. Using approximate matrix inversion results in computationally efficient im- plementations; we show that some recently proposed calibration algorithms such as StefCal and peeling can be understood as special cases of this, and place them in the context of the general formalism. Finally, we present an implementation and some applied results of CohJones, another specialized direction-dependent calibration al- gorithm derived from the formalism.
    [Show full text]
  • Research Article Complex-Valued Adaptive Signal Processing Using Nonlinear Functions
    Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008, Article ID 765615, 9 pages doi:10.1155/2008/765615 Research Article Complex-Valued Adaptive Signal Processing Using Nonlinear Functions Hualiang Li and Tulay¨ Adalı Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA Correspondence should be addressed to Tulay¨ Adalı, [email protected] Received 16 October 2007; Accepted 14 February 2008 Recommended by An´ıbal Figueiras-Vidal We describe a framework based on Wirtinger calculus for adaptive signal processing that enables efficient derivation of algorithms by directly working in the complex domain and taking full advantage of the power of complex-domain nonlinear processing. We establish the basic relationships for optimization in the complex domain and the real-domain equivalences for first- and second- order derivatives by extending the work of Brandwood and van den Bos. Examples in the derivation of first- and second-order update rules are given to demonstrate the versatility of the approach. Copyright © 2008 H. Li and T. Adalı. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. INTRODUCTION back to the complex domain. The approach facilitates the computations but increases the dimensionality of the prob- Most of today’s challenging signal processing applications re- lem and might not be practical for functions that are nonlin- quire techniques that are nonlinear, adaptive, and with on- ear since in this case, the functional form might not be easily line processing capability.
    [Show full text]
  • Ising Model and Field Theory: Lattice Local Fields and Massive Scaling Limit
    Ising Model and Field Theory: Lattice Local Fields and Massive Scaling Limit Thèse N° 7741 Présentée le 9 septembre 2019 à la Faculté des sciences de base Chaire de théorie des champs statistiques Programme doctoral en mathématiques pour l’obtention du grade de Docteur ès Sciences par Sung Chul PARK Acceptée sur proposition du jury Prof. M. Troyanov, président du jury Prof. C. Hongler, directeur de thèse Prof. F. Viklund, rapporteur Prof. D. Chelkak, rapporteur Prof. R. Dalang, rapporteur 2019 Abstract This thesis is devoted to the study of the local fields in the Ising model. The scaling limit of the critical Ising model is conjecturally described by Conformal Field Theory. The explicit predictions for the building blocks of the continuum theory (spin and energy density) have been rigorously established [HoSm13, CHI15]. We study how the field-theoretic description of these random fields extends beyond the critical regime of the model. Concretely, the thesis consists of two parts: The first part studies the behaviour of lattice local fields in the critical Ising model. A lattice local field is a function of a finite number of spins at microscopic distances from a given point. We study one-point functions of these fields (in particular, their asymptotics under scaling limit and conformal invariance). Our analysis, based on discrete complex analysis methods, results in explicit computations which are of interest in applications (e.g. [HKV17]). The second part considers the behaviour of the massive spin field. In the subcritical massive scaling limit regime first considered by Wu, McCoy, Tracy, and Barouch [WMTB76], we show that the correlations of the massive spin field in a bounded domain have a scaling limit.
    [Show full text]
  • Tesis Universidad Tecnologica De Pereira
    Faculty of Engineering Master thesis Tertiary control in microgrids: an optimal power flow approach based on convex optimization and Wirtinger calculus by Diego Alejandro Ramirez Loaiza August 19, 2020 www.utp.edu.co TERTIARY CONTROL IN MICROGRIDS: AN OPTIMAL POWER FLOW APPROACH BASED ON CONVEX OPTIMIZATION AND WIRTINGER CALCULUS. by: Diego Alejandro Ramirez Loaiza (email: [email protected]) Advisor: Professor Alejandro Garcés Ruiz (email: [email protected]). Master in electric Power Engineering Universidad Tecnológica de Pereira August 19, 2020 Abstract This work presents a tertiary control for microgrids considering renewable energy sources and energy storage devices. The proposed model considers the operation in 24h includ- ing capacity and grid-code limitations. The optimization problem under consideration is non-convex, therefore, the model is approximated to a convex representation, by using a linear formulation of the optimal power flow equations via Wirtinger’s calculus. As for the mathematical methodology, the use of convex optimization and Wirtinger calculus guarantee uniqueness of the solution, global optimality and convergence of the algorithms. These characteristics are key for applications in automation processes such as the tertiary control which requires real-time operation. The model is implemented in a low cost small single-board computer (Raspberry-pi) programmed in Python. Nu- merical experiments on the CIGRE low voltage test system demonstrate the usage of the model. 2 Contents 1 Introduction 6 1.1 Motivation . .6 1.2 State of the art . .7 1.3 Contribution . .9 1.4 Outline of the thesis . 10 2 Operation of microgrids 11 2.1 Hierarchical control .
    [Show full text]
  • Holomorphic Functions and Power Series
    Holomorphic Functions and Power Series Having studied the basic properties of the complex eld, we now study functions of a complex variable. We dene what it means for a complex-valued function dened on a domain in the com- plex plane to be complex-dierentiable. We then study the relationship between this new notion of dierentiability and the already familiar notion of real-dierentiability. Functions that are complex- dierentiable at every point in a domain are called holomorphic functions. For various reasons, which we shall study in great detail, a function being holomorphic is far more restrictive than the function being real-dierentiable. The focal point of this chapter will be power series and functions that have local power series expansions, i.e., analytic functions. The reason for this is two-fold: 1. The most important functions that arise in practice are analytic. 2. All holomorphic functions are analytic! The second point above will be one of the central theorems we will prove in this course. We end this chapter with a brief exposition of the exponential and trigonometric functions. Notation: Throughout this chapter, U will be a domain in C with a 2 U and f : U ! C. will be a function. 1 Holomorphic functions 1.1 Definitions and examples Denition 1. Let U ⊂ C be open and f : U ! C be a map and a 2 U . We say f is complex- dierentiable (C-dierentiable at a) if f (z) − f (a) lim 0,z!a z − a exists and in which case we denote the limit by f 0(a) and call it the complex derivative of f at a.
    [Show full text]
  • 20 Feb 2020 on the Nonlinear Cauchy-Riemann Equations of Structural Transformation and Nonlinear
    On the nonlinear Cauchy-Riemann equations of structural transformation and nonlinear Laplace equation Gen Wang ∗ Department of Mathematics, Zhejiang Normal University, ZheJiang, Jinhua 321004, P.R.China. Abstract This paper aims at studying a functional K-transformation w (z) w (z) = → w (z) K (z) that is made to reconsider the complex differentiability for a given complex function w and subsequently we obtain structural holomorphic to judge a complexe function to be complex structural differentiable. Since K (z) can be chosen arbitrarily, thus it has greatly generalized the applied practicability. And we particularly consider K (z)=1+ κ (z), then we found an unique Carleman-Bers-Vekua equations which is more simpler that all coefficients are dependent to the structural function κ (z). The generalized exterior differential operator and the generalized Wirtinger derivatives are simultaneously obtained as well. As a discussion, second-order nonlinear Laplace equation is studied. Keywords: Cauchy-Riemann equation, K-transformation, structural holomorphic condition, Carleman-Bers-Vekua equations, nonlinear Laplace equation 2010 Mathematics Subject Classification: 32V05, 32W05 Contents 1 Introduction 2 1.1 Cauchy-Riemannequations. 2 1.2 Carleman-Bers-Vekua (CBV) equation. ... 3 arXiv:1806.00171v2 [math.CV] 20 Feb 2020 1.3 the models of generalized Cauchy-Riemann system . .... 4 1.4 Nonlinear Cauchy-Riemann equations . 6 2 Nonlinear Structural Cauchy-Riemann Equations 7 2.1 K-transformation................................. 7 2.2 Structural holomorphic
    [Show full text]