Ellipsoid Method
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Oracles, Ellipsoid Method and Their Uses in Convex Optimization Lecturer: Matt Weinberg Scribe:Sanjeev Arora
princeton univ. F'17 cos 521: Advanced Algorithm Design Lecture 17: Oracles, Ellipsoid method and their uses in convex optimization Lecturer: Matt Weinberg Scribe:Sanjeev Arora Oracle: A person or agency considered to give wise counsel or prophetic pre- dictions or precognition of the future, inspired by the gods. Recall that Linear Programming is the following problem: maximize cT x Ax ≤ b x ≥ 0 where A is a m × n real constraint matrix and x; c 2 Rn. Recall that if the number of bits to represent the input is L, a polynomial time solution to the problem is allowed to have a running time of poly(n; m; L). The Ellipsoid algorithm for linear programming is a specific application of the ellipsoid method developed by Soviet mathematicians Shor(1970), Yudin and Nemirovskii(1975). Khachiyan(1979) applied the ellipsoid method to derive the first polynomial time algorithm for linear programming. Although the algorithm is theoretically better than the Simplex algorithm, which has an exponential running time in the worst case, it is very slow practically and not competitive with Simplex. Nevertheless, it is a very important theoretical tool for developing polynomial time algorithms for a large class of convex optimization problems, which are much more general than linear programming. In fact we can use it to solve convex optimization problems that are even too large to write down. 1 Linear programs too big to write down Often we want to solve linear programs that are too large to even write down (or for that matter, too big to fit into all the hard drives of the world). -
Chapter 11. Three Dimensional Analytic Geometry and Vectors
Chapter 11. Three dimensional analytic geometry and vectors. Section 11.5 Quadric surfaces. Curves in R2 : x2 y2 ellipse + =1 a2 b2 x2 y2 hyperbola − =1 a2 b2 parabola y = ax2 or x = by2 A quadric surface is the graph of a second degree equation in three variables. The most general such equation is Ax2 + By2 + Cz2 + Dxy + Exz + F yz + Gx + Hy + Iz + J =0, where A, B, C, ..., J are constants. By translation and rotation the equation can be brought into one of two standard forms Ax2 + By2 + Cz2 + J =0 or Ax2 + By2 + Iz =0 In order to sketch the graph of a quadric surface, it is useful to determine the curves of intersection of the surface with planes parallel to the coordinate planes. These curves are called traces of the surface. Ellipsoids The quadric surface with equation x2 y2 z2 + + =1 a2 b2 c2 is called an ellipsoid because all of its traces are ellipses. 2 1 x y 3 2 1 z ±1 ±2 ±3 ±1 ±2 The six intercepts of the ellipsoid are (±a, 0, 0), (0, ±b, 0), and (0, 0, ±c) and the ellipsoid lies in the box |x| ≤ a, |y| ≤ b, |z| ≤ c Since the ellipsoid involves only even powers of x, y, and z, the ellipsoid is symmetric with respect to each coordinate plane. Example 1. Find the traces of the surface 4x2 +9y2 + 36z2 = 36 1 in the planes x = k, y = k, and z = k. Identify the surface and sketch it. Hyperboloids Hyperboloid of one sheet. The quadric surface with equations x2 y2 z2 1. -
Radar Back-Scattering from Non-Spherical Scatterers
REPORT OF INVESTIGATION NO. 28 STATE OF ILLINOIS WILLIAM G. STRATION, Governor DEPARTMENT OF REGISTRATION AND EDUCATION VERA M. BINKS, Director RADAR BACK-SCATTERING FROM NON-SPHERICAL SCATTERERS PART 1 CROSS-SECTIONS OF CONDUCTING PROLATES AND SPHEROIDAL FUNCTIONS PART 11 CROSS-SECTIONS FROM NON-SPHERICAL RAINDROPS BY Prem N. Mathur and Eugam A. Mueller STATE WATER SURVEY DIVISION A. M. BUSWELL, Chief URBANA. ILLINOIS (Printed by authority of State of Illinois} REPORT OF INVESTIGATION NO. 28 1955 STATE OF ILLINOIS WILLIAM G. STRATTON, Governor DEPARTMENT OF REGISTRATION AND EDUCATION VERA M. BINKS, Director RADAR BACK-SCATTERING FROM NON-SPHERICAL SCATTERERS PART 1 CROSS-SECTIONS OF CONDUCTING PROLATES AND SPHEROIDAL FUNCTIONS PART 11 CROSS-SECTIONS FROM NON-SPHERICAL RAINDROPS BY Prem N. Mathur and Eugene A. Mueller STATE WATER SURVEY DIVISION A. M. BUSWELL, Chief URBANA, ILLINOIS (Printed by authority of State of Illinois) Definitions of Terms Part I semi minor axis of spheroid semi major axis of spheroid wavelength of incident field = a measure of size of particle prolate spheroidal coordinates = eccentricity of ellipse = angular spheroidal functions = Legendse polynomials = expansion coefficients = radial spheroidal functions = spherical Bessel functions = electric field vector = magnetic field vector = back scattering cross section = geometric back scattering cross section Part II = semi minor axis of spheroid = semi major axis of spheroid = Poynting vector = measure of size of spheroid = wavelength of the radiation = back scattering -
John Ellipsoid 5.1 John Ellipsoid
CSE 599: Interplay between Convex Optimization and Geometry Winter 2018 Lecture 5: John Ellipsoid Lecturer: Yin Tat Lee Disclaimer: Please tell me any mistake you noticed. The algorithm at the end is unpublished. Feel free to contact me for collaboration. 5.1 John Ellipsoid In the last lecture, we discussed that any convex set is very close to anp ellipsoid in probabilistic sense. More precisely, after renormalization by covariance matrix, we have kxk2 = n ± Θ(1) with high probability. In this lecture, we will talk about how convex set is close to an ellipsoid in a strict sense. If the convex set is isotropic, it is close to a sphere as follows: Theorem 5.1.1. Let K be a convex body in Rn in isotropic position. Then, rn + 1 B ⊆ K ⊆ pn(n + 1)B : n n n Roughly speaking, this says that any convex set can be approximated by an ellipsoid by a n factor. This result has a lot of applications. Although the bound is tight, making a body isotropic is pretty time- consuming. In fact, making a body isotropic is the current bottleneck for obtaining faster algorithm for sampling in convex sets. Currently, it can only be done in O∗(n4) membership oracle plus O∗(n5) total time. Problem 5.1.2. Find a faster algorithm to approximate the covariance matrix of a convex set. In this lecture, we consider another popular position of a convex set called John position and its correspond- ing ellipsoid is called John ellipsoid. Definition 5.1.3. Given a convex set K. -
Introduction to Aberrations OPTI 518 Lecture 13
Introduction to aberrations OPTI 518 Lecture 13 Prof. Jose Sasian OPTI 518 Topics • Aspheric surfaces • Stop shifting • Field curve concept Prof. Jose Sasian OPTI 518 Aspheric Surfaces • Meaning not spherical • Conic surfaces: Sphere, prolate ellipsoid, hyperboloid, paraboloid, oblate ellipsoid or spheroid • Cartesian Ovals • Polynomial surfaces • Infinite possibilities for an aspheric surface • Ray tracing for quadric surfaces uses closed formulas; for other surfaces iterative algorithms are used Prof. Jose Sasian OPTI 518 Aspheric surfaces The concept of the sag of a surface 2 cS 4 6 8 10 ZS ASASASAS4 6 8 10 ... 1 1 K ( c2 1 ) 2 S Sxy222 K 2 K is the conic constant K=0, sphere K=-1, parabola C is 1/r where r is the radius of curvature; K is the K<-1, hyperola conic constant (the eccentricity squared); -1<K<0, prolate ellipsoid A’s are aspheric coefficients K>0, oblate ellipsoid Prof. Jose Sasian OPTI 518 Conic surfaces focal properties • Focal points for Ellipsoid case mirrors Hyperboloid case Oblate ellipsoid • Focal points of lenses K n2 Hecht-Zajac Optics Prof. Jose Sasian OPTI 518 Refraction at a spherical surface Aspheric surface description 2 cS 4 6 8 10 ZS ASASASAS4 6 8 10 ... 1 1 K ( c2 1 ) 2 S 1122 2222 22 y x Aicrasphe y 1 x 4 K y Zx 28rr Prof. Jose Sasian OTI 518P Cartesian Ovals ln l'n' Cte . Prof. Jose Sasian OPTI 518 Aspheric cap Aspheric surface The aspheric surface can be thought of as comprising a base sphere and an aspheric cap Cap Spherical base surface Prof. -
Geodetic Position Computations
GEODETIC POSITION COMPUTATIONS E. J. KRAKIWSKY D. B. THOMSON February 1974 TECHNICALLECTURE NOTES REPORT NO.NO. 21739 PREFACE In order to make our extensive series of lecture notes more readily available, we have scanned the old master copies and produced electronic versions in Portable Document Format. The quality of the images varies depending on the quality of the originals. The images have not been converted to searchable text. GEODETIC POSITION COMPUTATIONS E.J. Krakiwsky D.B. Thomson Department of Geodesy and Geomatics Engineering University of New Brunswick P.O. Box 4400 Fredericton. N .B. Canada E3B5A3 February 197 4 Latest Reprinting December 1995 PREFACE The purpose of these notes is to give the theory and use of some methods of computing the geodetic positions of points on a reference ellipsoid and on the terrain. Justification for the first three sections o{ these lecture notes, which are concerned with the classical problem of "cCDputation of geodetic positions on the surface of an ellipsoid" is not easy to come by. It can onl.y be stated that the attempt has been to produce a self contained package , cont8.i.ning the complete development of same representative methods that exist in the literature. The last section is an introduction to three dimensional computation methods , and is offered as an alternative to the classical approach. Several problems, and their respective solutions, are presented. The approach t~en herein is to perform complete derivations, thus stqing awrq f'rcm the practice of giving a list of for11111lae to use in the solution of' a problem. -
Models for Earth and Maps
Earth Models and Maps James R. Clynch, Naval Postgraduate School, 2002 I. Earth Models Maps are just a model of the world, or a small part of it. This is true if the model is a globe of the entire world, a paper chart of a harbor or a digital database of streets in San Francisco. A model of the earth is needed to convert measurements made on the curved earth to maps or databases. Each model has advantages and disadvantages. Each is usually in error at some level of accuracy. Some of these error are due to the nature of the model, not the measurements used to make the model. Three are three common models of the earth, the spherical (or globe) model, the ellipsoidal model, and the real earth model. The spherical model is the form encountered in elementary discussions. It is quite good for some approximations. The world is approximately a sphere. The sphere is the shape that minimizes the potential energy of the gravitational attraction of all the little mass elements for each other. The direction of gravity is toward the center of the earth. This is how we define down. It is the direction that a string takes when a weight is at one end - that is a plumb bob. A spirit level will define the horizontal which is perpendicular to up-down. The ellipsoidal model is a better representation of the earth because the earth rotates. This generates other forces on the mass elements and distorts the shape. The minimum energy form is now an ellipse rotated about the polar axis. -
World Geodetic System 1984
World Geodetic System 1984 Responsible Organization: National Geospatial-Intelligence Agency Abbreviated Frame Name: WGS 84 Associated TRS: WGS 84 Coverage of Frame: Global Type of Frame: 3-Dimensional Last Version: WGS 84 (G1674) Reference Epoch: 2005.0 Brief Description: WGS 84 is an Earth-centered, Earth-fixed terrestrial reference system and geodetic datum. WGS 84 is based on a consistent set of constants and model parameters that describe the Earth's size, shape, and gravity and geomagnetic fields. WGS 84 is the standard U.S. Department of Defense definition of a global reference system for geospatial information and is the reference system for the Global Positioning System (GPS). It is compatible with the International Terrestrial Reference System (ITRS). Definition of Frame • Origin: Earth’s center of mass being defined for the whole Earth including oceans and atmosphere • Axes: o Z-Axis = The direction of the IERS Reference Pole (IRP). This direction corresponds to the direction of the BIH Conventional Terrestrial Pole (CTP) (epoch 1984.0) with an uncertainty of 0.005″ o X-Axis = Intersection of the IERS Reference Meridian (IRM) and the plane passing through the origin and normal to the Z-axis. The IRM is coincident with the BIH Zero Meridian (epoch 1984.0) with an uncertainty of 0.005″ o Y-Axis = Completes a right-handed, Earth-Centered Earth-Fixed (ECEF) orthogonal coordinate system • Scale: Its scale is that of the local Earth frame, in the meaning of a relativistic theory of gravitation. Aligns with ITRS • Orientation: Given by the Bureau International de l’Heure (BIH) orientation of 1984.0 • Time Evolution: Its time evolution in orientation will create no residual global rotation with regards to the crust Coordinate System: Cartesian Coordinates (X, Y, Z). -
Ductile Deformation - Concepts of Finite Strain
327 Ductile deformation - Concepts of finite strain Deformation includes any process that results in a change in shape, size or location of a body. A solid body subjected to external forces tends to move or change its displacement. These displacements can involve four distinct component patterns: - 1) A body is forced to change its position; it undergoes translation. - 2) A body is forced to change its orientation; it undergoes rotation. - 3) A body is forced to change size; it undergoes dilation. - 4) A body is forced to change shape; it undergoes distortion. These movement components are often described in terms of slip or flow. The distinction is scale- dependent, slip describing movement on a discrete plane, whereas flow is a penetrative movement that involves the whole of the rock. The four basic movements may be combined. - During rigid body deformation, rocks are translated and/or rotated but the original size and shape are preserved. - If instead of moving, the body absorbs some or all the forces, it becomes stressed. The forces then cause particle displacement within the body so that the body changes its shape and/or size; it becomes deformed. Deformation describes the complete transformation from the initial to the final geometry and location of a body. Deformation produces discontinuities in brittle rocks. In ductile rocks, deformation is macroscopically continuous, distributed within the mass of the rock. Instead, brittle deformation essentially involves relative movements between undeformed (but displaced) blocks. Finite strain jpb, 2019 328 Strain describes the non-rigid body deformation, i.e. the amount of movement caused by stresses between parts of a body. -
Linear Programming Notes
Linear Programming Notes Lecturer: David Williamson, Cornell ORIE Scribe: Kevin Kircher, Cornell MAE These notes summarize the central definitions and results of the theory of linear program- ming, as taught by David Williamson in ORIE 6300 at Cornell University in the fall of 2014. Proofs and discussion are mostly omitted. These notes also draw on Convex Optimization by Stephen Boyd and Lieven Vandenberghe, and on Stephen Boyd's notes on ellipsoid methods. Prof. Williamson's full lecture notes can be found here. Contents 1 The linear programming problem3 2 Duailty 5 3 Geometry6 4 Optimality conditions9 4.1 Answer 1: cT x = bT y for a y 2 F(D)......................9 4.2 Answer 2: complementary slackness holds for a y 2 F(D)........... 10 4.3 Answer 3: the verifying y is in F(D)...................... 10 5 The simplex method 12 5.1 Reformulation . 12 5.2 Algorithm . 12 5.3 Convergence . 14 5.4 Finding an initial basic feasible solution . 15 5.5 Complexity of a pivot . 16 5.6 Pivot rules . 16 5.7 Degeneracy and cycling . 17 5.8 Number of pivots . 17 5.9 Varieties of the simplex method . 18 5.10 Sensitivity analysis . 18 5.11 Solving large-scale linear programs . 20 1 6 Good algorithms 23 7 Ellipsoid methods 24 7.1 Ellipsoid geometry . 24 7.2 The basic ellipsoid method . 25 7.3 The ellipsoid method with objective function cuts . 28 7.4 Separation oracles . 29 8 Interior-point methods 30 8.1 Finding a descent direction that preserves feasibility . 30 8.2 The affine-scaling direction . -
1979 Khachiyan's Ellipsoid Method
Convex Optimization CMU-10725 Ellipsoid Methods Barnabás Póczos & Ryan Tibshirani Outline Linear programs Simplex algorithm Running time: Polynomial or Exponential? Cutting planes & Ellipsoid methods for LP Cutting planes & Ellipsoid methods for unconstrained minimization 2 Books to Read David G. Luenberger, Yinyu Ye : Linear and Nonlinear Programming Boyd and Vandenberghe: Convex Optimization 3 Back to Linear Programs Inequality form of LPs using matrix notation: Standard form of LPs: We already know: Any LP can be rewritten to an equivalent standard LP 4 Motivation Linear programs can be viewed in two somewhat complementary ways: continuous optimization : continuous variables, convex feasible region continuous objective function combinatorial problems : solutions can be found among the vertices of the convex polyhedron defined by the constraints Issues with combinatorial search methods : number of vertices may be exponentially large, making direct search impossible for even modest size problems 5 History 6 Simplex Method Simplex method: Jumping from one vertex to another, it improves values of the objective as the process reaches an optimal point. It performs well in practice, visiting only a small fraction of the total number of vertices. Running time? Polynomial? or Exponential? 7 The Simplex method is not polynomial time Dantzig observed that for problems with m ≤ 50 and n ≤ 200 the number of iterations is ordinarily less than 1.5m. That time many researchers believed (and tried to prove) that the simplex algorithm is polynomial in the size of the problem (n,m) In 1972, Klee and Minty showed by examples that for certain linear programs the simplex method will examine every vertex. These examples proved that in the worst case, the simplex method requires a number of steps that is exponential in the size of the problem. -
Convex Optimization: Algorithms and Complexity
Foundations and Trends R in Machine Learning Vol. 8, No. 3-4 (2015) 231–357 c 2015 S. Bubeck DOI: 10.1561/2200000050 Convex Optimization: Algorithms and Complexity Sébastien Bubeck Theory Group, Microsoft Research [email protected] Contents 1 Introduction 232 1.1 Some convex optimization problems in machine learning . 233 1.2 Basic properties of convexity . 234 1.3 Why convexity? . 237 1.4 Black-box model . 238 1.5 Structured optimization . 240 1.6 Overview of the results and disclaimer . 240 2 Convex optimization in finite dimension 244 2.1 The center of gravity method . 245 2.2 The ellipsoid method . 247 2.3 Vaidya’s cutting plane method . 250 2.4 Conjugate gradient . 258 3 Dimension-free convex optimization 262 3.1 Projected subgradient descent for Lipschitz functions . 263 3.2 Gradient descent for smooth functions . 266 3.3 Conditional gradient descent, aka Frank-Wolfe . 271 3.4 Strong convexity . 276 3.5 Lower bounds . 279 3.6 Geometric descent . 284 ii iii 3.7 Nesterov’s accelerated gradient descent . 289 4 Almost dimension-free convex optimization in non-Euclidean spaces 296 4.1 Mirror maps . 298 4.2 Mirror descent . 299 4.3 Standard setups for mirror descent . 301 4.4 Lazy mirror descent, aka Nesterov’s dual averaging . 303 4.5 Mirror prox . 305 4.6 The vector field point of view on MD, DA, and MP . 307 5 Beyond the black-box model 309 5.1 Sum of a smooth and a simple non-smooth term . 310 5.2 Smooth saddle-point representation of a non-smooth function312 5.3 Interior point methods .