Conic Optimization

Total Page:16

File Type:pdf, Size:1020Kb

Conic Optimization CSCI 1951-G – Optimization Methods in Finance Part 10: Conic Optimization April 6, 2018 1 / 34 This material is covered in the textbook, Chapters 9 and 10. Some of the materials are taken from it. Some of the figures are from S. Boyd, L. Vandenberge’s book Convex Optimization https://web.stanford.edu/~boyd/cvxbook/. 2 / 34 Outline 1. Cones and conic optimization 2. Converting quadratic constraints into cone constraints 3. Benchmark-relative portfolio optimization 4. Semidefinite programming 5. Approximating covariance matrices 3 / 34 Cones A set C is a cone if for every x C and θ 0, 2 ≥ θx C 2 2 Example: (x; x ); x R R f j j 2 g ⊂ Is this set convex? 4 / 34 Convex Cones A set C is a convex cone if, for every x1; x2 C and θ1; θ2 0, 26 2 2 Convex≥ sets θ1x1 + θ2x2 C: 2 Example: x1 x2 0 Figure 2.4 The pie slice shows all points of the form θ1x1 + θ2x2,where θ1, θ2 0. The apex of the slice (which corresponds to θ1 = θ2 = 0) is at ≥ 0; its edges (which correspond to θ1 = 0 or θ2 = 0) pass through the points x1 and x2. 5 / 34 0 0 Figure 2.5 The conic hulls (shown shaded) of the two sets of figure 2.3. Conic optimization Conic optimization problem in standard form: min cTx Ax = b x C 2 where C is a convex cone in finite-dimensional vector space X. Note: linear objective function, linear constraints. n n If X = R and C = R+, then ...we get an LP! Conic optimization is a unifying framework for linear programming, • second-order cone programming (SOCP), • semidefinite programming (SDP). • 6 / 34 Norm cones n−1 Let be any norm in R . k · k The norm cone associated to is the set k · k C = x = (x1; : : : ; xn): x1 (x2; : : : ; xn) f ≥ k kg It is a convex set. 7 / 34 3 2.2 Some important examplesSecond-order cone in R 31 The second-order cone is the norm cone for the Euclidean norm 2. k · k 1 0.5 t 0 1 1 0 0 x2 1 1 − − x1 3 2 2 1/2 Figure 2.10 Boundary of second-order cone in R , (x1,x2,t) (x1+x2) t . { | ≤ } What happens when we slice the second-order cone? It is (asI.e., the name when suggests) we take a convex the cone.intersection with a hyperplane? We obtain ellipsoidal sets. Example 2.3 The second-order cone is the norm cone for the Euclidean norm, i.e., 8 / 34 n+1 C = (x, t) R x 2 t { ∈ | ∥ ∥ ≤ } T x x I 0 x = 0,t 0 . ! t $ t 0 1 t ≤ ≥ % " # $ " # " − #" # $ $ The second-order cone is also known$ by several other names. It is calledthequadratic cone, since it is defined by a quadratic inequality. It is also called the Lorentz cone or ice-cream cone. Figure 2.10 shows the second-order cone in R3. 2.2.4 Polyhedra A polyhedron is defined as the solution set of a finite number of linear equalities and inequalities: = x aT x b ,j=1,...,m, cT x = d ,j=1,...,p . (2.5) P { | j ≤ j j j } A polyhedron is thus the intersection of a finite number of halfspaces and hyper- planes. Affine sets (e.g., subspaces, hyperplanes, lines), rays, line segments, and halfspaces are all polyhedra. It is easily shown that polyhedra are convex sets. A bounded polyhedron is sometimes called a polytope, but some authors use the opposite convention (i.e., polytope for any set of the form (2.5), and polyhedron Rewriting constraints Let’s rewrite C = x = (x1; : : : ; xn): x1 (x2; : : : ; xn) 2 f ≥ k k g as 2 2 2 x1 0; x x x 0 ≥ 1 − 2 − · · · n ≥ This is a combination of an linear and a quadratic constraints. Also: convex quadratic constraints can be expressed as second-order cone membership constraints. 9 / 34 Rewriting constraints adratic constraint: xT P x + 2qTx + γ 0 ≤ Assume P w.l.o.g. positive definite, so the constraint is ...convex. Also assume, for technical reasons, that qTP q γ 0. − ≥ Goal: rewrite the above constraint as a combination of linear and second-order cone membership constraints. 10 / 34 Rewriting constraints Because P is positive definitive, it has a Cholesky decomposition: invertible R s.t. P = RRT : 9 Rewrite the constraint as: (RTx)T(RTx) + 2qTx + γ 0 ≤ Let T T −1 y = (y1; : : : ; yn) = R x + R q The above is a bijection between x and y. We are going to rewrite the constraint as a constraint on y. 11 / 34 Rewriting constraints The constraint: (RTx)T(RTx) + 2qTx + γ 0 ≤ It holds yTy = (RTx)T(RTx) + 2qTx + qTP −1q Since there is a bijection between y and x, the constraint can be satisfied if and only if y s.t. y = RTx + R−1q; yTy qTP q γ 9 ≤ − 12 / 34 Rewriting constraints The constraint is equivalent to: y s.t. y = RTx + R−1q; yTy qTP q γ 9 ≤ − Lets denote with y0 the square root of the r.h.s. of the right inequality: T y0 = q P q γ R+ − 2 Consider the vector (y0;y 1;p : : : ; yn). y The right inequality then is | {z } n y2 yTy = y2 0 ≥ i i=1 X Taking the square root on both sides: n 2 y0 y = y 2 ≥ v i k k ui=1 uX This is the membership constraintt for the second-order cone in n+1. R 13 / 34 Rewriting constraints We rewrite the convex quadratic constraint xT P x + 2qTx + γ 0 ≤ as T T −1 (y1; : : : ; yn) = R x + R q T y0 = q P q γ R+ − 2 (y0; y1; : : : ; yn) C 2 p which is a combination of linear and second-order cone membership constraints. 14 / 34 Outline 1. Cones and conic optimization 2. Converting quadratic constraints into cone constraints 3. Benchmark-relative portfolio optimization 4. Semidefinite programming 5. Approximating covariance matrices 6. SDP and approximation algorithms 15 / 34 Benchmark-relative portfolio optimization Given a benchmark strategy xB (e.g., an index) develop a portfolio x that tracks xB, but adds value by beating it. I.e., we want a portfolio x with positive expected excess return: T µ (x xB) 0 − ≥ and specifically want to maximize the expected excess return. Challenge: balance expected excess return with its variance. 16 / 34 Tracking error and volatility constraints The (predicted) tracking error of the portfolio x is T TE(x) = (x xB) Σ(x xB) − − q It measure the variability of excess returns. In benchmark-relative portfolio optimization, we solve mean-variance optimization w.r.t. the expected excess return and tracking error: T max µ (x xB) − T 2 (x xB) Σ(x xB) T − − ≤ Ax = b 17 / 34 Comparison with mean-variance optimization We have seen MVO as: 1 T min x Σx δ 2 max µTx Σx µTx R or − 2 ≥ Ax = b Ax = b How do they dier from T max µ (x xB) − T 2 (x xB) Σ(x xB) T − − ≤ Ax = b The laer is not a standard quadratic program: it has a nonlinear constraint. 18 / 34 T max µ (x xB) − T 2 (x xB) Σ(x xB) T − − ≤ Ax = b The nonlinear constraint is ...convex quadratic We can rewrite it as a combination of linear and second-order cone membership, and solve the resulting convex conic problem. 19 / 34 Outline 1. Cones and conic optimization 2. Converting quadratic constraints into cone constraints 3. Benchmark-relative portfolio optimization 4. Semidefinite programming 5. Approximating covariance matrices 6. SDP and approximation algorithms 20 / 34 SemiDefinite Programming (SDP) The variables are the entries of a symmetric matrix in the cone of 2.3 Operations that preserve convexity 35 positive semidefinite matrices. 1 0.5 z 0 1 1 0 0.5 y 1 0 − x Figure 2.12 Boundary of positive semidefinite cone in S2. 21 / 34 x 0, y 0, xz y2. ≥ ≥ n ≥ n n The set S+ is a convex cone: if θ1, θ2 0 and A, B S+, then θ1A+θ2B S+. This can be seen directly from the definition≥ of positive∈ semidefiniteness: for∈ any x Rn, we have ∈ xT (θ A + θ B)x = θ xT Ax + θ xT Bx 0, 1 2 1 2 ≥ if A 0, B 0 and θ , θ 0. ≽ ≽ 1 2 ≥ Example 2.6 Positive semidefinite cone in S2. We have xy X = S2 x 0,z 0,xz y2. yz ∈ + ⇐⇒ ≥ ≥ ≥ ! " The boundary of this cone is shown in figure 2.12, plotted in R3 as (x, y, z). 2.3 Operations that preserve convexity In this section we describe some operations that preserve convexity of sets, or allow us to construct convex sets from others. These operations, together with the simple examples described in 2.2, form a calculus of convex sets that is useful for determining or establishing convexity§ of sets. Application: approximating covariance matrices Portfolio Optimization almost always requires covariance matrices. These are not directly available, but are estimated. Estimation of covariance matrices is a very challenging task, mathematically and computationally, because the matrices must satisfy various properties (e.g., symmetry, positive semidefiniteness). To be eicient, many estimation methods do not impose problem-dependent constraints. Typically, one is interested in finding the smallest distortion of the original estimate that satisfies the desired constraints; 22 / 34 Application: approximating covariance matrices Let Σ^ n be an estimate of a covariance matrix • 2 S Σ^ is symmetric ( n) but not positive semidefinite.
Recommended publications
  • A Ten Page Introduction to Conic Optimization, 2015
    CHAPTER 1 A ten page introduction to conic optimization This background chapter gives an introduction to conic optimization. We do not give proofs, but focus on important (for this thesis) tools and concepts. 1.1. Optimization and computational hardness Optimization is about maximizing or minimizing a function over a set. The set is typically described more implicitly than just an enumeration of its elements, and the structure in this description is essential in developing good optimization tech- niques. The set is known as the feasible set and its elements the feasible solutions. The function is called the objective function and its values the objective values. We write a minimization problem asp = inf x S f(x), and we often usep to refer to the optimization problem as a whole instead∈ of just its optimal value. We are not only interested infinding the optimal value, but also infinding optimal solutions, and if this is too difficult (or if they do not exist) we seek close to optimal feasible solutions. An important topic isfinding certificates asserting the solution’s opti- mality or quality of approximation. In fact, when solving an optimization problem we often intend tofind an optimal or near optimal feasible solution together with a certificate. Linear programming is foundational in conic optimization. Consider the prob- lem offinding a vectorx satisfying a linear system Ax=b. We canfind such anx by Gaussian elimination, but when we also require the entries ofx to be nonnegative, then we need algorithms which go further then just solving linear systems. Linear programming is the optimization analogue of this feasibility problem: In a linear program we optimize a linear functional over all nonnegative vectors satisfying some linear system.
    [Show full text]
  • Conic/Semidefinite Optimization
    Mathematical Optimization IMA SUMMER COURSE: CONIC/SEMIDEFINITE OPTIMIZATION Shuzhong Zhang Department of Industrial & Systems Engineering University of Minnesota [email protected] August 3, 2016 Shuzhong Zhang (ISyE@UMN) Mathematical Optimization August 3, 2016 1 / 43 Let us start by considering the rationalized preferences choices. In the traditional economics theory, the rationality of the preferences lies in the fact that they pass the following consistency test: 1. Any commodity is always considered to be preferred over itself. 2. If commodity A is preferred over commodity B, while B is preferred over A at the same time, then the decision-maker is clearly indifferent between A and B, i.e., they are identical to the decision-maker. 3. If commodity A is preferred over commodity B, then any positive multiple of A should also be preferred over the same amount of multiple of B. 4. If commodity A is preferred over commodity B, while commodity B is preferred over commodity C, then the decision-maker would prefer A over C. Shuzhong Zhang (ISyE@UMN) Mathematical Optimization August 3, 2016 2 / 43 In mathematical terms, the analog is an object known as the pointed convex cone. We shall confine ourself to a finite dimensional Euclidean space here, to be denoted by Rn. A subset K of Rn is called a pointed convex cone in Rn if the following conditions are satisfied: 1. The origin of the space – vector 0 – belongs to K. 2. If x P K and ´x P K then x “ 0. 3. If x P K then tx P K for all t ¡ 0.
    [Show full text]
  • Solving Conic Optimization Problems Using MOSEK
    Solving conic optimization problems using MOSEK December 16th 2017 [email protected] www.mosek.com Section 1 Background The company • MOSEK is a Danish company founded 15th of October 1997. • Vision: Create and sell software for mathematical optimization problems. • Linear and conic problems. • Convex quadratically constrained problems. • Also mixed-integer versions of the above. • Located in Copenhagen Denmark at Symbion Science Park. • Daily management: Erling D. Andersen. 2 / 126 Section 2 Outline Todays topics • Conic optimization • What is it? • And why? • What can be modelled? • MOSEK • What is it? • Examples of usage. • Technical details. • Computational results. 4 / 126 Section 3 Conic optimization A special case Linear optimization The classical linear optimization problem: minimize cT x subject to Ax = b; x ≥ 0: Pro: • Structure is explicit and simple. • Data is simple: c; A; b. • Structure implies convexity i.e. data independent. • Powerful duality theory including Farkas lemma. • Smoothness, gradients, Hessians are not an issue. Therefore, we have powerful algorithms and software. 6 / 126 linear optimization cont. Con: • It is linear only. 7 / 126 The classical nonlinear optimization problem The classical nonlinear optimization problem: minimize f(x) subject to g(x) ≤ 0: Pro • It is very general. Con: • Structure is hidden. • How to specify the problem at all in software? • How to compute gradients and Hessians if needed? • How to do presolve? • How to exploit structure e.g. linearity? • Convexity checking! • Verifying convexity is NP hard. • Solution: Disciplined modelling by Grant, Boyd and Ye [6] to assure convexity. 8 / 126 The question Is there a class of nonlinear optimization problems that preserve almost all of the good properties of the linear optimization problem? 9 / 126 Generic conic optimization problem Primal form X minimize (ck)T xk k X subject to Akxk = b; k xk 2 Kk; 8k; where k nk • c 2 R , k m×nk • A 2 R , m • b 2 R , • Kk are convex cones.
    [Show full text]
  • MOSEK Modeling Cookbook Release 3.2.3
    MOSEK Modeling Cookbook Release 3.2.3 MOSEK ApS 13 September 2021 Contents 1 Preface1 2 Linear optimization3 2.1 Introduction.................................3 2.2 Linear modeling...............................7 2.3 Infeasibility in linear optimization..................... 11 2.4 Duality in linear optimization........................ 13 3 Conic quadratic optimization 20 3.1 Cones..................................... 20 3.2 Conic quadratic modeling.......................... 22 3.3 Conic quadratic case studies......................... 26 4 The power cone 30 4.1 The power cone(s).............................. 30 4.2 Sets representable using the power cone.................. 32 4.3 Power cone case studies........................... 33 5 Exponential cone optimization 37 5.1 Exponential cone............................... 37 5.2 Modeling with the exponential cone.................... 38 5.3 Geometric programming........................... 41 5.4 Exponential cone case studies........................ 45 6 Semidefinite optimization 50 6.1 Introduction to semidefinite matrices.................... 50 6.2 Semidefinite modeling............................ 54 6.3 Semidefinite optimization case studies................... 62 7 Practical optimization 71 7.1 Conic reformulations............................. 71 7.2 Avoiding ill-posed problems......................... 74 7.3 Scaling.................................... 76 7.4 The huge and the tiny............................ 78 7.5 Semidefinite variables............................ 79 7.6 The quality of a solution..........................
    [Show full text]
  • Engineering and Business Applications of Sum of Squares Polynomials
    Proceedings of Symposia in Applied Mathematics Engineering and Business Applications of Sum of Squares Polynomials Georgina Hall Abstract. Optimizing over the cone of nonnegative polynomials, and its dual counterpart, optimizing over the space of moments that admit a representing measure, are fundamental problems that appear in many different applications from engineering and computational mathematics to business. In this paper, we review a number of these applications. These include, but are not limited to, problems in control (e.g., formal safety verification), finance (e.g., option pricing), statistics and machine learning (e.g., shape-constrained regression and optimal design), and game theory (e.g., Nash equilibria computation in polynomial games). We then show how sum of squares techniques can be used to tackle these problems, which are hard to solve in general. We conclude by highlighting some directions that could be pursued to further disseminate sum of squares techniques within more applied fields. Among other things, we briefly address the current challenge that scalability represents for optimization problems that involve sum of squares polynomials and discuss recent trends in software development. A variety of applications in engineering, computational mathematics, and business can be cast as optimization problems over the cone of nonnegative polynomials or the cone of moments admitting a representing measure. For a long while, these problems were thought to be intractable until the advent, in the 2000s, of techniques based on sum of squares (sos) optimization. The goal of this paper is to provide concrete examples|from a wide variety of fields—of settings where these techniques can be used and detailed explanations as to how to use them.
    [Show full text]