Lecture: Convex Optimization Problems

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Lecture: Convex Optimization Problems"

Transcription

1 1/36 Lecture: Convex Optimization Problems Acknowledgement: this slides is based on Prof. Lieven Vandenberghe s lecture notes

2 Introduction 2/36 optimization problem in standard form convex optimization problems quasiconvex optimization linear optimization quadratic optimization geometric programming generalized inequality constraints semidefinite programming vector optimization

3 Optimization problem in standard form min f 0 (x) s.t. f i (x) 0, i = 1,..., m h i (x) = 0, i = 1,..., p x R n is the optimization variable f 0 : R n R is the objective or cost function f i : R n R, i = 1,..., m, are the inequality constraint functions h i : R n R are the equality constraint functions optimal value: p = inf{f 0 (x) f i (x) 0, i = 1,..., m, h i (x) = 0, i = 1,..., p} p = if problem is infeasible (no x satisfies the constraints) p = if problem is unbounded below 3/36

4 Optimal and locally optimal points x is feasible if x dom f 0 and it satisfies the constraints a feasible x is optimal if f 0 (x) = p ; X opt is the set of optimal points x is locally optimal if there is an R > 0 such that x is optimal for min(overz) f 0 (z) s.t. f i (z) 0, i = 1,..., m h i (z) = 0, i = 1,..., p z x 2 R examples (with n = 1, m = p = 0) f 0 (x) = 1/x, dom f 0 = R ++ : p = 0, no optimal point f 0 (x) = log x, dom f 0 = R ++ : p = f 0 (x) = x log x, dom f 0 = R ++ : p = 1/e, x = 1/e is optimal f 0 (x) = x 3 3x, p =, local optimum at x = 1 4/36

5 Implicit constraints 5/36 the standard form optimization problem has an implicit constraint x D = m dom f i i=0 we call D the domain of the problem p dom h i, the constraints f i (x) 0, h i (x) = 0 are the explicit constraints a problem is unconstrained if it has no explicit constraints (m = p = 0) i=1 example: min f 0 (x) = k i=1 log(b i a T i x) is an unconstrained problem with implicit constraints a T i x < b i

6 6/36 Feasibility problem find x s.t. f i (x) 0, i = 1,..., m h i (x) = 0, i = 1,..., p can be considered a special case of the general problem with f 0 (x) = 0: min 0 s.t. f i (x) 0, i = 1,..., m h i (x) = 0, i = 1,..., p p = 0 if constraints are feasible; any feasible x is optimal p = if constraints are infeasible

7 7/36 Convex optimization problem standard form convex optimization problem min f 0 (x) s.t. f i (x) 0, i = 1,..., m a T i x = b i, i = 1,..., p f 0, f 1,..., f m are convex; equality constraints are affine problem is quasiconvex if f 0 is quasiconvex (and f 1,..., f m convex) often written as min f 0 (x) s.t. f i (x) 0, i = 1,..., m Ax = b important property: feasible set of a convex optimization problem is convex

8 example min f 0 (x) = x x 2 2 s.t. f 1 (x) = x 1 /(1 + x 2 2) 0 h 1 (x) = (x 1 + x 2 ) 2 = 0 f 0 is convex; feasible set {(x 1, x 2 ) x 1 = x 2 0} is convex not a convex problem (according to our definition): f 1 is not convex, h 1 is not affine equivalent (but not identical) to the convex problem min x1 2 + x2 2 s.t. x 1 0 x 1 + x 2 = 0 8/36

9 Local and global optima 9/36 any locally optimal point of a convex problem is (globally) optimal proof : suppose x is locally optimal and y is optimal with f 0 (y) < f 0 (x) x locally optimal means there is an R > 0 such that z feasible, z x 2 R = f 0 (z) f 0 (x) consider z = θy + (1 θ)x with θ = R/(2 y x 2 ) y x 2 > R, so 0 < θ < 1/2 z is a convex combination of two feasible points, hence also feasible z x 2 = R/2 and f 0 (z) θf 0 (x) + (1 θ)f 0 (y) < f 0 (x) which contradicts our assumption that x is locally optimal

10 if nonzero, f 0 (x) defines a supporting hyperplane to feasible set X at x zero, f 0 (x) defines a supporting hyperplane to feasible set X at 10/36 Optimality criterion for differentiable f ptimal if and only if it is feasible and 0 x is optimal if and only if it is feasible and f 0 (x) T (y x) 0 for all feasible y f 0 (x) T (y x) 0 for all feasible y X x f 0 (x)

11 11/36 unconstrained problem: x is optimal if and only if x dom f 0, f 0 (x) = 0 equality constrained problem min f 0 (x) s.t. Ax = b x is optimal if and only if there exists a υ such that x dom f 0, Ax = b, f 0 (x) + A T υ = 0 minimization over nonnegative orthant min f 0 (x) s.t. x 0 x is optimal if and only if x dom f 0, x 0, { f0 (x) i 0 x i = 0 f 0 (x) i = 0 x i > 0

12 12/36 Equivalent convex problems two problems are (informally) equivalent if the solution of one is readily obtained from the solution of the other, and vice-versa some common transformations that preserve convexity: eliminating equality constraints is equivalent to min f 0 (x) s.t. f i (x) 0, i = 1,..., m Ax = b min(overz) f 0 (Fz + x 0 ) s.t. f i (Fz + x 0 ) 0, i = 1,..., m where F and x 0 are such that Ax = b x = Fz + x 0 for some z

13 13/36 Equivalent convex problems introducing equality constraints min f 0 (A 0 x + b 0 ) s.t. f i (A i x + b i ) 0, i = 1,..., m is equivalent to min(over x, y i ) f 0 (y 0 ) s.t. f i (y i ) 0, i = 1,..., m y i = A i x + b i, i = 0, 1,..., m introducing slack variables for linear inequalities min f 0 (x) s.t. a T x b i, i = 1,..., m is equivalent to min(over x, s) f 0 (x) s.t. a T x + s i = b i, i = 1,..., m s i 0, i = 1,...m

14 14/36 Equivalent convex problems epigraph form: standard form convex problem is equivalent to min(over x, t) t s.t. f 0 (x) t 0 f i (x) 0, i = 1,..., m Ax = b minimizing over some variables min f 0 (x 1, x 2 ) s.t. f i (x 1 ) 0, i = 1,..., m is equivalent to min f 0 (x 1 ) s.t. f i (x 1 ) 0, i = 1,..., m where f 0 (x 1 ) = inf x2 f 0 (x 1, x 2 )

15 15/36 Quasiconvex optimization minimize f 0 (x) subjectmin to f 0 i (x) 0, i = 1,..., m s.t. fax i (x) = 0, b i = 1,..., m th f 0 : R n R quasiconvex, Ax f 1 =,. b.., f m convex with f 0 : R n R quasiconvex, f 1,..., f m convex n have can have locally locally optimal optimal points points that that are are not not (globally) optimal (x, f 0 (x))

16 16/36 convex representation of sublevel sets of f 0 if f 0 is quasiconvex, there exists a family of functions φ t such that: φ t (x) is convex in x for fixed t t-sublevel set of f 0 is 0-sublevel set of φ t,i.e., f 0 (x) t φ t (x) 0 example f 0 (x) = p(x) q(x) with p convex, q concave, and p(x) 0, q(x) > 0 on dom f 0 can take φ t (x) = p(x) tq(x): for t 0, φ t convex in x p(x)/q(x) t if and only if φ t (x) 0

17 quasiconvex optimization via convex feasibility problems φ t (x) 0, f i (x) 0, i = 1,..., m, Ax = b (1) for fixed t, a convex feasibility problem in x if feasible, we can conclude that t p ; if infeasible, t p Bisection method for quasiconvex optimization given l p, u p, tolerance ɛ > 0. repeat 1 t := (l + u)/2. 2 Solve the convex feasibility problem (1). 3 if (1) is feasible, u := t; else l := t. until u l ɛ. requires exactly log 2 ((u l)/ɛ) iterations (where u, l are initial values) 17/36

18 18/36 Linear program (LP) minimize c T x + d subject minto c T Gx x + d h s.t. Gx Ax = h b Ax = b convex problem with affine objective and constraint functions convex problem with affine objective and constraint functions feasible set is a polyhedron feasible set is a polyhedron P x c

19 19/36 Examples diet problem: choose quantities x 1,..., x n of n foods one unit of food j costs c j, contains amount a ij of nutrient i healthy diet requires nutrient i in quantity at least b i to find cheapest healthy diet, min c T x s.t. Ax b, x 0 piecewise-linear minimization min max i=1,...,m (a T i x + b i ) equivalent to an LP min t s.t. a T i x + b i t, i = 1,..., m

20 20/36 Chebyshev center of a polyhedron Chebyshev center of a polyhedron Chebyshev center center of of P = P {x = {x a a T T i x b i, i 1,..., m} i x b i, i = 1,..., m} is center of largest inscribed ball is center of largest inscribed ball B = {x c + u u 2 r} B = {x c + u u 2 r} x cheb a T i x a T i xb i for b i for all xall x B ifb and if and only only if if sup{a sup{a T i (x T i c (x + c u) + u) u u 2 2 r} r} = = a T a i x T i c x c + + r a r a i i 2 2 b i b i hence, x c, r can be determined by solving the LP hence, x c, r can be determined by solving the LP max r maximize r s.t. subject to a T a T i i x x c + r a c r a i i 2 b 2 i, i = 1,..., m i, i 1,...,

21 Linear-fractional program linear-fractional program min s.t. f 0 (x) Gx h Ax = b f 0 (x) = ct x + d e T x + f, dom f 0(x) = {x e T x + f > 0} a quasiconvex optimization problem; can be solved by bisection also equivalent to the LP (variables y, z) min c T y + dz s.t. Gy hz Ay = bz e T y + fz = 1 z 0 21/36

22 Quadratic program (QP) 22/36 Quadratic program (QP) min (1/2)x T Px + q T x + r s.t. Gx h minimize (1/2)x T P x + q T x + r subject to Ax Gx= bh Ax = b P S n +, so objective is convex quadratic P S n +, so objective is convex quadratic minimize a convex quadratic function over a polyhedron minimize a convex quadratic function over a polyhedron f 0 (x ) x P

23 Examples least-squares min Ax b 2 2 analytical solution x = A b (A is pseudo-inverse) can add linear constraints, e.g., l x u linear program with random cost min c T x + γx T Σx = Ec T x + γvar(c T x) s.t. Gx h, Ax = b c is random vector with mean c and covariance Σ hence, c T x is random variable with mean c T x and variance x T Σx γ > 0 is risk aversion parameter; controls the trade-off between expected cost and variance (risk) 23/36

24 24/36 Quadratically constrained quadratic program (QCQP) min (1/2)x T P 0 x + q T 0 x + r 0 s.t. (1/2)x T P i x + q T i x + r i 0, i = 1,..., m Ax = b P i S n + ; objective and constraints are convex quadratic if P 1,..., P m S n ++, feasible region is intersection of m ellipsoids and an affine set

25 25/36 Second-order cone programming min f T x s.t. A i x + b i 2 c T i x + d i, i = 1,..., m Fx = g (A i R ni n, F R p n ) inequalities are called second-order cone (SOC) constraints: (A i x + b i, c T i x + d i ) second-order cone in R n i+1 for n i = 0, reduces to an LP; if c i = 0, reduces to a QCQP more general than QCQP and LP

26 Robust linear programming the parameters in optimization problems are often uncertain, e.g., in an LP min c T x s.t. a T i x b i, i = 1,..., m, there can be uncertainty in c, a i, b i two common approaches to handling uncertainty (in a i, for simplicity) deterministic model: constraints must hold for all a i E i min c T x s.t. a T i x b i for all a i E i, i = 1,..., m, stochastic model: a i is random variable; constraints must hold with probability η min c T x s.t. prob(a T i x b i ) η, i = 1,..., m 26/36

27 27/36 deterministic approach via SOCP choose an ellipsoid as E i : E i = {ā i + P i u u 2 1} (ā i R n, P i R n n ) center is ā i, semi-axes determined by singular values/vectors of P i robust LP min c T x s.t. a T i x b i a i E i, i = 1,..., m is equivalent to the SOCP min c T x s.t. ā T i x + P T i x 2 b i, i = 1,..., m (follows from sup u 2 1 (ā i + P i u) T x = ā T i x + PT i x 2)

28 28/36 stochastic approach via SOCP assume a i is Gaussian with mean ā i, covariance Σ i (a i N (ā i, Σ i )) a T x is Gaussian r.v. with mean ā T i x, variance xt Σ i x; hence ( ) prob(a T b i ā T i i x b i ) = Φ x Σ 1/2 i x 2 where Φ(x) = (1/ 2π) x e t2 /2 dt is CDF of N (0, 1) robust LP min c T x s.t. prob(a T i x b i ) η, i = 1,..., m, with η 1/2, is equivalent to the SOCP min c T x s.t. ā T i x + Φ 1 (η) Σ 1/2 i x 2 b i, i = 1,..., m

29 Geometric programming monomial function f (x) = cx a 1 1 xa 2 2 xan n, dom f = R n ++ with c > 0; exponent α i can be any real number posynomial function: sum of monomials f (x) = K k=1 geometric program (GP) min c k x a 1k 1 xa 2k 2 x a nk n, dom f = R n ++ f 0 (x) s.t. f i (x) 1, i = 1,..., m h i (x) = 1, i = 1,..., p with f i posynomial, h i monomial 29/36

30 Geometric program in convex form 30/36 change variables to y i = log x i, and take logarithm of cost, constraints monomial f (x) = cx a 1 1 xan n transforms to log f (e y 1,..., e yn ) = a T y + b (b = log c) posynomial f (x) = K k=1 c kx a 1k 1 xa 2k 2 x a nk n transforms to ( K ) log f (e y 1,..., e yn ) = log e at k y+b k (b k = log c k ) k=1 geometric program transforms to convex problem ( K ) min log k=1 exp(at 0ky + b 0k ) ( K ) s.t. log k=1 exp(at iky + b ik ) 0, i = 1,..., m Gy + d = 0

31 Minimizing spectral radius of nonnegative matrix Perron-Frobenius eigenvalue λ pf (A) exists for (elementwise) positive A R n n a real, positive eigenvalue of A, equal to spectral radius max i λ i (A) determines asymptotic growth (decay) rate of A k : A k λ k pf as k alternative characterization: λ pf (A) = inf{λ Av λv for some v 0} minimizing spectral radius of matrix of posynomials minimize λ pf (A(x)), where the elements A(x) ij are posynomials of x equivalent geometric program: min s.t. λ n j=1 A(x) ijv j /(λv i ) 1, i = 1,..., n variables λ, v, x 31/36

32 Generalized inequality constraints convex problem with generalized inequality constraints min f 0 (x) s.t. f i (x) Ki 0, i = 1,..., m Ax = b f 0 : R n R convex; f i : R n R k i K i -convex w.r.t. proper cone K i same properties as standard convex problem (convex feasible set, local optimum is global, etc.) conic form problem: special case with affine objective and constraints min c T x s.t. Fx + g K 0 Ax = b extends linear programming (K = R m +) to nonpolyhedral cones 32/36

33 Semidefinite program (SDP) min c T x s.t. x 1 F 1 + x 2 F x n F n + G 0 Ax = b with F i, G S k inequality constraint is called linear matrix inequality (LMI) includes problems with multiple LMI constraints: for example, x 1ˆF x nˆf n + Ĝ 0, x 1 F x n F n + G 0 is equivalent to single LMI x 1 [ F F 1 ] +x 2 [ F F 2 ] + +x n [ F n 0 0 F n ] [ G G ] 0 33/36

34 LP and SOCP as SDP 34/36 LP and equivalent SDP LP: min c T x s.t. Ax b SDP: min c T x s.t. diag(ax b) 0 (note different interpretation of generalized inequality ) SOCP and equivalent SDP SOCP: min f T x s.t. A i x + b i 2 c T x + d i, i = 1,..., m SDP: min f T x [ (c T s.t. i x + d i )I A i x + b i (A i x + b i ) T c T i x + d i ] 0, i = 1,..., m

35 35/36 Eigenvalue minimization min λ max (A(x)) where A(x) = A 0 + x 1 A x n A n (with given A i S k ) equivalent SDP min s.t. t A(x) ti variables x R n, t R follows from λ max (A) t A ti

36 Matrix norm minimization 36/36 min A(x) 2 = ( λ max (A(x) T A(x)) ) 1/2 where A(x) = A 0 + x 1 A x n A n (with given A i R p q ) equivalent SDP min s.t. t [ ti A(x) T A(x) ti ] 0 variables x R n, t R constraint follows from A 2 t A T A t 2 I, t 0 [ ] ti A A T 0 ti

4. Convex optimization problems

4. Convex optimization problems Convex Optimization Boyd & Vandenberghe 4. Convex optimization problems optimization problem in standard form convex optimization problems quasiconvex optimization linear optimization quadratic optimization

More information

Convex optimization problems. Optimization problem in standard form

Convex optimization problems. Optimization problem in standard form Convex optimization problems optimization problem in standard form convex optimization problems linear optimization quadratic optimization geometric programming quasiconvex optimization generalized inequality

More information

4. Convex optimization problems

4. Convex optimization problems Convex Optimization Boyd & Vandenberghe 4. Convex optimization problems optimization problem in standard form convex optimization problems quasiconvex optimization linear optimization quadratic optimization

More information

4. Convex optimization problems (part 1: general)

4. Convex optimization problems (part 1: general) EE/AA 578, Univ of Washington, Fall 2016 4. Convex optimization problems (part 1: general) optimization problem in standard form convex optimization problems quasiconvex optimization 4 1 Optimization problem

More information

Convex Optimization. 4. Convex Optimization Problems. Prof. Ying Cui. Department of Electrical Engineering Shanghai Jiao Tong University

Convex Optimization. 4. Convex Optimization Problems. Prof. Ying Cui. Department of Electrical Engineering Shanghai Jiao Tong University Conve Optimization 4. Conve Optimization Problems Prof. Ying Cui Department of Electrical Engineering Shanghai Jiao Tong University 2017 Autumn Semester SJTU Ying Cui 1 / 58 Outline Optimization problems

More information

Convex Optimization and Modeling

Convex Optimization and Modeling Convex Optimization and Modeling Convex Optimization Fourth lecture, 05.05.2010 Jun.-Prof. Matthias Hein Reminder from last time Convex functions: first-order condition: f(y) f(x) + f x,y x, second-order

More information

CSCI : Optimization and Control of Networks. Review on Convex Optimization

CSCI : Optimization and Control of Networks. Review on Convex Optimization CSCI7000-016: Optimization and Control of Networks Review on Convex Optimization 1 Convex set S R n is convex if x,y S, λ,µ 0, λ+µ = 1 λx+µy S geometrically: x,y S line segment through x,y S examples (one

More information

1. Introduction. mathematical optimization. least-squares and linear programming. convex optimization. example. course goals and topics

1. Introduction. mathematical optimization. least-squares and linear programming. convex optimization. example. course goals and topics 1. Introduction Convex Optimization Boyd & Vandenberghe mathematical optimization least-squares and linear programming convex optimization example course goals and topics nonlinear optimization brief history

More information

1. Introduction. mathematical optimization. least-squares and linear programming. convex optimization. example. course goals and topics

1. Introduction. mathematical optimization. least-squares and linear programming. convex optimization. example. course goals and topics 1. Introduction Convex Optimization Boyd & Vandenberghe mathematical optimization least-squares and linear programming convex optimization example course goals and topics nonlinear optimization brief history

More information

A Brief Review on Convex Optimization

A Brief Review on Convex Optimization A Brief Review on Convex Optimization 1 Convex set S R n is convex if x,y S, λ,µ 0, λ+µ = 1 λx+µy S geometrically: x,y S line segment through x,y S examples (one convex, two nonconvex sets): A Brief Review

More information

Tutorial on Convex Optimization for Engineers Part II

Tutorial on Convex Optimization for Engineers Part II Tutorial on Convex Optimization for Engineers Part II M.Sc. Jens Steinwandt Communications Research Laboratory Ilmenau University of Technology PO Box 100565 D-98684 Ilmenau, Germany jens.steinwandt@tu-ilmenau.de

More information

Lecture: Examples of LP, SOCP and SDP

Lecture: Examples of LP, SOCP and SDP 1/34 Lecture: Examples of LP, SOCP and SDP Zaiwen Wen Beijing International Center For Mathematical Research Peking University http://bicmr.pku.edu.cn/~wenzw/bigdata2018.html wenzw@pku.edu.cn Acknowledgement:

More information

12. Interior-point methods

12. Interior-point methods 12. Interior-point methods Convex Optimization Boyd & Vandenberghe inequality constrained minimization logarithmic barrier function and central path barrier method feasibility and phase I methods complexity

More information

Optimisation convexe: performance, complexité et applications.

Optimisation convexe: performance, complexité et applications. Optimisation convexe: performance, complexité et applications. Introduction, convexité, dualité. A. d Aspremont. M2 OJME: Optimisation convexe. 1/128 Today Convex optimization: introduction Course organization

More information

12. Interior-point methods

12. Interior-point methods 12. Interior-point methods Convex Optimization Boyd & Vandenberghe inequality constrained minimization logarithmic barrier function and central path barrier method feasibility and phase I methods complexity

More information

Convex Optimization Problems. Prof. Daniel P. Palomar

Convex Optimization Problems. Prof. Daniel P. Palomar Conve Optimization Problems Prof. Daniel P. Palomar The Hong Kong University of Science and Technology (HKUST) MAFS6010R- Portfolio Optimization with R MSc in Financial Mathematics Fall 2018-19, HKUST,

More information

8. Geometric problems

8. Geometric problems 8. Geometric problems Convex Optimization Boyd & Vandenberghe extremal volume ellipsoids centering classification placement and facility location 8 Minimum volume ellipsoid around a set Löwner-John ellipsoid

More information

CS675: Convex and Combinatorial Optimization Fall 2016 Convex Optimization Problems. Instructor: Shaddin Dughmi

CS675: Convex and Combinatorial Optimization Fall 2016 Convex Optimization Problems. Instructor: Shaddin Dughmi CS675: Convex and Combinatorial Optimization Fall 2016 Convex Optimization Problems Instructor: Shaddin Dughmi Outline 1 Convex Optimization Basics 2 Common Classes 3 Interlude: Positive Semi-Definite

More information

Lecture: Duality.

Lecture: Duality. Lecture: Duality http://bicmr.pku.edu.cn/~wenzw/opt-2016-fall.html Acknowledgement: this slides is based on Prof. Lieven Vandenberghe s lecture notes Introduction 2/35 Lagrange dual problem weak and strong

More information

1. Introduction. mathematical optimization. least-squares and linear programming. convex optimization. example. course goals and topics

1. Introduction. mathematical optimization. least-squares and linear programming. convex optimization. example. course goals and topics 1. Introduction Convex Optimization Boyd & Vandenberghe mathematical optimization least-squares and linear programming convex optimization example course goals and topics nonlinear optimization brief history

More information

1. Introduction. mathematical optimization. least-squares and linear programming. convex optimization. example. course goals and topics

1. Introduction. mathematical optimization. least-squares and linear programming. convex optimization. example. course goals and topics 1. Introduction Convex Optimization Boyd & Vandenberghe mathematical optimization least-squares and linear programming convex optimization example course goals and topics nonlinear optimization brief history

More information

1. Introduction. mathematical optimization. least-squares and linear programming. convex optimization. example. course goals and topics

1. Introduction. mathematical optimization. least-squares and linear programming. convex optimization. example. course goals and topics 1. Introduction mathematical optimization least-squares and linear programming convex optimization example course goals and topics nonlinear optimization brief history of convex optimization 1 1 Mathematical

More information

8. Geometric problems

8. Geometric problems 8. Geometric problems Convex Optimization Boyd & Vandenberghe extremal volume ellipsoids centering classification placement and facility location 8 1 Minimum volume ellipsoid around a set Löwner-John ellipsoid

More information

EE/ACM Applications of Convex Optimization in Signal Processing and Communications Lecture 17

EE/ACM Applications of Convex Optimization in Signal Processing and Communications Lecture 17 EE/ACM 150 - Applications of Convex Optimization in Signal Processing and Communications Lecture 17 Andre Tkacenko Signal Processing Research Group Jet Propulsion Laboratory May 29, 2012 Andre Tkacenko

More information

Convex Optimization. (EE227A: UC Berkeley) Lecture 6. Suvrit Sra. (Conic optimization) 07 Feb, 2013

Convex Optimization. (EE227A: UC Berkeley) Lecture 6. Suvrit Sra. (Conic optimization) 07 Feb, 2013 Convex Optimization (EE227A: UC Berkeley) Lecture 6 (Conic optimization) 07 Feb, 2013 Suvrit Sra Organizational Info Quiz coming up on 19th Feb. Project teams by 19th Feb Good if you can mix your research

More information

Convex Optimization M2

Convex Optimization M2 Convex Optimization M2 Lecture 3 A. d Aspremont. Convex Optimization M2. 1/49 Duality A. d Aspremont. Convex Optimization M2. 2/49 DMs DM par email: dm.daspremont@gmail.com A. d Aspremont. Convex Optimization

More information

Convex Optimization and l 1 -minimization

Convex Optimization and l 1 -minimization Convex Optimization and l 1 -minimization Sangwoon Yun Computational Sciences Korea Institute for Advanced Study December 11, 2009 2009 NIMS Thematic Winter School Outline I. Convex Optimization II. l

More information

Semidefinite Programming Basics and Applications

Semidefinite Programming Basics and Applications Semidefinite Programming Basics and Applications Ray Pörn, principal lecturer Åbo Akademi University Novia University of Applied Sciences Content What is semidefinite programming (SDP)? How to represent

More information

5. Duality. Lagrangian

5. Duality. Lagrangian 5. Duality Convex Optimization Boyd & Vandenberghe Lagrange dual problem weak and strong duality geometric interpretation optimality conditions perturbation and sensitivity analysis examples generalized

More information

Exercises. Exercises. Basic terminology and optimality conditions. 4.2 Consider the optimization problem

Exercises. Exercises. Basic terminology and optimality conditions. 4.2 Consider the optimization problem Exercises Basic terminology and optimality conditions 4.1 Consider the optimization problem f 0(x 1, x 2) 2x 1 + x 2 1 x 1 + 3x 2 1 x 1 0, x 2 0. Make a sketch of the feasible set. For each of the following

More information

Lecture 4: Linear and quadratic problems

Lecture 4: Linear and quadratic problems Lecture 4: Linear and quadratic problems linear programming examples and applications linear fractional programming quadratic optimization problems (quadratically constrained) quadratic programming second-order

More information

EE364 Review Session 4

EE364 Review Session 4 EE364 Review Session 4 EE364 Review Outline: convex optimization examples solving quasiconvex problems by bisection exercise 4.47 1 Convex optimization problems we have seen linear programming (LP) quadratic

More information

Convex Optimization in Communications and Signal Processing

Convex Optimization in Communications and Signal Processing Convex Optimization in Communications and Signal Processing Prof. Dr.-Ing. Wolfgang Gerstacker 1 University of Erlangen-Nürnberg Institute for Digital Communications National Technical University of Ukraine,

More information

Convex Functions. Daniel P. Palomar. Hong Kong University of Science and Technology (HKUST)

Convex Functions. Daniel P. Palomar. Hong Kong University of Science and Technology (HKUST) Convex Functions Daniel P. Palomar Hong Kong University of Science and Technology (HKUST) ELEC5470 - Convex Optimization Fall 2017-18, HKUST, Hong Kong Outline of Lecture Definition convex function Examples

More information

Lecture: Duality of LP, SOCP and SDP

Lecture: Duality of LP, SOCP and SDP 1/33 Lecture: Duality of LP, SOCP and SDP Zaiwen Wen Beijing International Center For Mathematical Research Peking University http://bicmr.pku.edu.cn/~wenzw/bigdata2017.html wenzw@pku.edu.cn Acknowledgement:

More information

Chapter 2: Linear Programming Basics. (Bertsimas & Tsitsiklis, Chapter 1)

Chapter 2: Linear Programming Basics. (Bertsimas & Tsitsiklis, Chapter 1) Chapter 2: Linear Programming Basics (Bertsimas & Tsitsiklis, Chapter 1) 33 Example of a Linear Program Remarks. minimize 2x 1 x 2 + 4x 3 subject to x 1 + x 2 + x 4 2 3x 2 x 3 = 5 x 3 + x 4 3 x 1 0 x 3

More information

9. Geometric problems

9. Geometric problems 9. Geometric problems EE/AA 578, Univ of Washington, Fall 2016 projection on a set extremal volume ellipsoids centering classification 9 1 Projection on convex set projection of point x on set C defined

More information

Constrained Optimization and Lagrangian Duality

Constrained Optimization and Lagrangian Duality CIS 520: Machine Learning Oct 02, 2017 Constrained Optimization and Lagrangian Duality Lecturer: Shivani Agarwal Disclaimer: These notes are designed to be a supplement to the lecture. They may or may

More information

Geometric problems. Chapter Projection on a set. The distance of a point x 0 R n to a closed set C R n, in the norm, is defined as

Geometric problems. Chapter Projection on a set. The distance of a point x 0 R n to a closed set C R n, in the norm, is defined as Chapter 8 Geometric problems 8.1 Projection on a set The distance of a point x 0 R n to a closed set C R n, in the norm, is defined as dist(x 0,C) = inf{ x 0 x x C}. The infimum here is always achieved.

More information

Lecture Note 5: Semidefinite Programming for Stability Analysis

Lecture Note 5: Semidefinite Programming for Stability Analysis ECE7850: Hybrid Systems:Theory and Applications Lecture Note 5: Semidefinite Programming for Stability Analysis Wei Zhang Assistant Professor Department of Electrical and Computer Engineering Ohio State

More information

HW1 solutions. 1. α Ef(x) β, where Ef(x) is the expected value of f(x), i.e., Ef(x) = n. i=1 p if(a i ). (The function f : R R is given.

HW1 solutions. 1. α Ef(x) β, where Ef(x) is the expected value of f(x), i.e., Ef(x) = n. i=1 p if(a i ). (The function f : R R is given. HW1 solutions Exercise 1 (Some sets of probability distributions.) Let x be a real-valued random variable with Prob(x = a i ) = p i, i = 1,..., n, where a 1 < a 2 < < a n. Of course p R n lies in the standard

More information

Convex Optimization Boyd & Vandenberghe. 5. Duality

Convex Optimization Boyd & Vandenberghe. 5. Duality 5. Duality Convex Optimization Boyd & Vandenberghe Lagrange dual problem weak and strong duality geometric interpretation optimality conditions perturbation and sensitivity analysis examples generalized

More information

Convex Optimization & Lagrange Duality

Convex Optimization & Lagrange Duality Convex Optimization & Lagrange Duality Chee Wei Tan CS 8292 : Advanced Topics in Convex Optimization and its Applications Fall 2010 Outline Convex optimization Optimality condition Lagrange duality KKT

More information

ELE539A: Optimization of Communication Systems Lecture 6: Quadratic Programming, Geometric Programming, and Applications

ELE539A: Optimization of Communication Systems Lecture 6: Quadratic Programming, Geometric Programming, and Applications ELE539A: Optimization of Communication Systems Lecture 6: Quadratic Programming, Geometric Programming, and Applications Professor M. Chiang Electrical Engineering Department, Princeton University February

More information

ELE539A: Optimization of Communication Systems Lecture 15: Semidefinite Programming, Detection and Estimation Applications

ELE539A: Optimization of Communication Systems Lecture 15: Semidefinite Programming, Detection and Estimation Applications ELE539A: Optimization of Communication Systems Lecture 15: Semidefinite Programming, Detection and Estimation Applications Professor M. Chiang Electrical Engineering Department, Princeton University March

More information

3. Convex functions. basic properties and examples. operations that preserve convexity. the conjugate function. quasiconvex functions

3. Convex functions. basic properties and examples. operations that preserve convexity. the conjugate function. quasiconvex functions 3. Convex functions Convex Optimization Boyd & Vandenberghe basic properties and examples operations that preserve convexity the conjugate function quasiconvex functions log-concave and log-convex functions

More information

A : k n. Usually k > n otherwise easily the minimum is zero. Analytical solution:

A : k n. Usually k > n otherwise easily the minimum is zero. Analytical solution: 1-5: Least-squares I A : k n. Usually k > n otherwise easily the minimum is zero. Analytical solution: f (x) =(Ax b) T (Ax b) =x T A T Ax 2b T Ax + b T b f (x) = 2A T Ax 2A T b = 0 Chih-Jen Lin (National

More information

3. Convex functions. basic properties and examples. operations that preserve convexity. the conjugate function. quasiconvex functions

3. Convex functions. basic properties and examples. operations that preserve convexity. the conjugate function. quasiconvex functions 3. Convex functions Convex Optimization Boyd & Vandenberghe basic properties and examples operations that preserve convexity the conjugate function quasiconvex functions log-concave and log-convex functions

More information

A : k n. Usually k > n otherwise easily the minimum is zero. Analytical solution:

A : k n. Usually k > n otherwise easily the minimum is zero. Analytical solution: 1-5: Least-squares I A : k n. Usually k > n otherwise easily the minimum is zero. Analytical solution: f (x) =(Ax b) T (Ax b) =x T A T Ax 2b T Ax + b T b f (x) = 2A T Ax 2A T b = 0 Chih-Jen Lin (National

More information

EE5138R: Problem Set 5 Assigned: 16/02/15 Due: 06/03/15

EE5138R: Problem Set 5 Assigned: 16/02/15 Due: 06/03/15 EE538R: Problem Set 5 Assigned: 6/0/5 Due: 06/03/5. BV Problem 4. The feasible set is the convex hull of (0, ), (0, ), (/5, /5), (, 0) and (, 0). (a) x = (/5, /5) (b) Unbounded below (c) X opt = {(0, x

More information

Advances in Convex Optimization: Theory, Algorithms, and Applications

Advances in Convex Optimization: Theory, Algorithms, and Applications Advances in Convex Optimization: Theory, Algorithms, and Applications Stephen Boyd Electrical Engineering Department Stanford University (joint work with Lieven Vandenberghe, UCLA) ISIT 02 ISIT 02 Lausanne

More information

The proximal mapping

The proximal mapping The proximal mapping http://bicmr.pku.edu.cn/~wenzw/opt-2016-fall.html Acknowledgement: this slides is based on Prof. Lieven Vandenberghes lecture notes Outline 2/37 1 closed function 2 Conjugate function

More information

Semidefinite and Second Order Cone Programming Seminar Fall 2012 Project: Robust Optimization and its Application of Robust Portfolio Optimization

Semidefinite and Second Order Cone Programming Seminar Fall 2012 Project: Robust Optimization and its Application of Robust Portfolio Optimization Semidefinite and Second Order Cone Programming Seminar Fall 2012 Project: Robust Optimization and its Application of Robust Portfolio Optimization Instructor: Farid Alizadeh Author: Ai Kagawa 12/12/2012

More information

I.3. LMI DUALITY. Didier HENRION EECI Graduate School on Control Supélec - Spring 2010

I.3. LMI DUALITY. Didier HENRION EECI Graduate School on Control Supélec - Spring 2010 I.3. LMI DUALITY Didier HENRION henrion@laas.fr EECI Graduate School on Control Supélec - Spring 2010 Primal and dual For primal problem p = inf x g 0 (x) s.t. g i (x) 0 define Lagrangian L(x, z) = g 0

More information

Linear and non-linear programming

Linear and non-linear programming Linear and non-linear programming Benjamin Recht March 11, 2005 The Gameplan Constrained Optimization Convexity Duality Applications/Taxonomy 1 Constrained Optimization minimize f(x) subject to g j (x)

More information

Nonlinear Programming Models

Nonlinear Programming Models Nonlinear Programming Models Fabio Schoen 2008 http://gol.dsi.unifi.it/users/schoen Nonlinear Programming Models p. Introduction Nonlinear Programming Models p. NLP problems minf(x) x S R n Standard form:

More information

EE Applications of Convex Optimization in Signal Processing and Communications Dr. Andre Tkacenko, JPL Third Term

EE Applications of Convex Optimization in Signal Processing and Communications Dr. Andre Tkacenko, JPL Third Term EE 150 - Applications of Convex Optimization in Signal Processing and Communications Dr. Andre Tkacenko JPL Third Term 2011-2012 Due on Thursday May 3 in class. Homework Set #4 1. (10 points) (Adapted

More information

The Q-parametrization (Youla) Lecture 13: Synthesis by Convex Optimization. Lecture 13: Synthesis by Convex Optimization. Example: Spring-mass System

The Q-parametrization (Youla) Lecture 13: Synthesis by Convex Optimization. Lecture 13: Synthesis by Convex Optimization. Example: Spring-mass System The Q-parametrization (Youla) Lecture 3: Synthesis by Convex Optimization controlled variables z Plant distubances w Example: Spring-mass system measurements y Controller control inputs u Idea for lecture

More information

Lecture 10. ( x domf. Remark 1 The domain of the conjugate function is given by

Lecture 10. ( x domf. Remark 1 The domain of the conjugate function is given by 10-1 Multi-User Information Theory Jan 17, 2012 Lecture 10 Lecturer:Dr. Haim Permuter Scribe: Wasim Huleihel I. CONJUGATE FUNCTION In the previous lectures, we discussed about conve set, conve functions

More information

EE/ACM Applications of Convex Optimization in Signal Processing and Communications Lecture 18

EE/ACM Applications of Convex Optimization in Signal Processing and Communications Lecture 18 EE/ACM 150 - Applications of Convex Optimization in Signal Processing and Communications Lecture 18 Andre Tkacenko Signal Processing Research Group Jet Propulsion Laboratory May 31, 2012 Andre Tkacenko

More information

Convex Optimization in Classification Problems

Convex Optimization in Classification Problems New Trends in Optimization and Computational Algorithms December 9 13, 2001 Convex Optimization in Classification Problems Laurent El Ghaoui Department of EECS, UC Berkeley elghaoui@eecs.berkeley.edu 1

More information

Agenda. 1 Cone programming. 2 Convex cones. 3 Generalized inequalities. 4 Linear programming (LP) 5 Second-order cone programming (SOCP)

Agenda. 1 Cone programming. 2 Convex cones. 3 Generalized inequalities. 4 Linear programming (LP) 5 Second-order cone programming (SOCP) Agenda 1 Cone programming 2 Convex cones 3 Generalized inequalities 4 Linear programming (LP) 5 Second-order cone programming (SOCP) 6 Semidefinite programming (SDP) 7 Examples Optimization problem in

More information

Analytic Center Cutting-Plane Method

Analytic Center Cutting-Plane Method Analytic Center Cutting-Plane Method S. Boyd, L. Vandenberghe, and J. Skaf April 14, 2011 Contents 1 Analytic center cutting-plane method 2 2 Computing the analytic center 3 3 Pruning constraints 5 4 Lower

More information

FRTN10 Multivariable Control, Lecture 13. Course outline. The Q-parametrization (Youla) Example: Spring-mass System

FRTN10 Multivariable Control, Lecture 13. Course outline. The Q-parametrization (Youla) Example: Spring-mass System FRTN Multivariable Control, Lecture 3 Anders Robertsson Automatic Control LTH, Lund University Course outline The Q-parametrization (Youla) L-L5 Purpose, models and loop-shaping by hand L6-L8 Limitations

More information

Convex Optimization. Newton s method. ENSAE: Optimisation 1/44

Convex Optimization. Newton s method. ENSAE: Optimisation 1/44 Convex Optimization Newton s method ENSAE: Optimisation 1/44 Unconstrained minimization minimize f(x) f convex, twice continuously differentiable (hence dom f open) we assume optimal value p = inf x f(x)

More information

Lecture 2: Convex functions

Lecture 2: Convex functions Lecture 2: Convex functions f : R n R is convex if dom f is convex and for all x, y dom f, θ [0, 1] f is concave if f is convex f(θx + (1 θ)y) θf(x) + (1 θ)f(y) x x convex concave neither x examples (on

More information

subject to (x 2)(x 4) u,

subject to (x 2)(x 4) u, Exercises Basic definitions 5.1 A simple example. Consider the optimization problem with variable x R. minimize x 2 + 1 subject to (x 2)(x 4) 0, (a) Analysis of primal problem. Give the feasible set, the

More information

ICS-E4030 Kernel Methods in Machine Learning

ICS-E4030 Kernel Methods in Machine Learning ICS-E4030 Kernel Methods in Machine Learning Lecture 3: Convex optimization and duality Juho Rousu 28. September, 2016 Juho Rousu 28. September, 2016 1 / 38 Convex optimization Convex optimisation This

More information

Lagrange Duality. Daniel P. Palomar. Hong Kong University of Science and Technology (HKUST)

Lagrange Duality. Daniel P. Palomar. Hong Kong University of Science and Technology (HKUST) Lagrange Duality Daniel P. Palomar Hong Kong University of Science and Technology (HKUST) ELEC5470 - Convex Optimization Fall 2017-18, HKUST, Hong Kong Outline of Lecture Lagrangian Dual function Dual

More information

CS-E4830 Kernel Methods in Machine Learning

CS-E4830 Kernel Methods in Machine Learning CS-E4830 Kernel Methods in Machine Learning Lecture 3: Convex optimization and duality Juho Rousu 27. September, 2017 Juho Rousu 27. September, 2017 1 / 45 Convex optimization Convex optimisation This

More information

15. Conic optimization

15. Conic optimization L. Vandenberghe EE236C (Spring 216) 15. Conic optimization conic linear program examples modeling duality 15-1 Generalized (conic) inequalities Conic inequality: a constraint x K where K is a convex cone

More information

Interior Point Methods: Second-Order Cone Programming and Semidefinite Programming

Interior Point Methods: Second-Order Cone Programming and Semidefinite Programming School of Mathematics T H E U N I V E R S I T Y O H F E D I N B U R G Interior Point Methods: Second-Order Cone Programming and Semidefinite Programming Jacek Gondzio Email: J.Gondzio@ed.ac.uk URL: http://www.maths.ed.ac.uk/~gondzio

More information

LMI MODELLING 4. CONVEX LMI MODELLING. Didier HENRION. LAAS-CNRS Toulouse, FR Czech Tech Univ Prague, CZ. Universidad de Valladolid, SP March 2009

LMI MODELLING 4. CONVEX LMI MODELLING. Didier HENRION. LAAS-CNRS Toulouse, FR Czech Tech Univ Prague, CZ. Universidad de Valladolid, SP March 2009 LMI MODELLING 4. CONVEX LMI MODELLING Didier HENRION LAAS-CNRS Toulouse, FR Czech Tech Univ Prague, CZ Universidad de Valladolid, SP March 2009 Minors A minor of a matrix F is the determinant of a submatrix

More information

Lecture 6: Conic Optimization September 8

Lecture 6: Conic Optimization September 8 IE 598: Big Data Optimization Fall 2016 Lecture 6: Conic Optimization September 8 Lecturer: Niao He Scriber: Juan Xu Overview In this lecture, we finish up our previous discussion on optimality conditions

More information

Convex Optimization: Applications

Convex Optimization: Applications Convex Optimization: Applications Lecturer: Pradeep Ravikumar Co-instructor: Aarti Singh Convex Optimization 1-75/36-75 Based on material from Boyd, Vandenberghe Norm Approximation minimize Ax b (A R m

More information

minimize x x2 2 x 1x 2 x 1 subject to x 1 +2x 2 u 1 x 1 4x 2 u 2, 5x 1 +76x 2 1,

minimize x x2 2 x 1x 2 x 1 subject to x 1 +2x 2 u 1 x 1 4x 2 u 2, 5x 1 +76x 2 1, 4 Duality 4.1 Numerical perturbation analysis example. Consider the quadratic program with variables x 1, x 2, and parameters u 1, u 2. minimize x 2 1 +2x2 2 x 1x 2 x 1 subject to x 1 +2x 2 u 1 x 1 4x

More information

Convex Optimization and Modeling

Convex Optimization and Modeling Convex Optimization and Modeling Duality Theory and Optimality Conditions 5th lecture, 12.05.2010 Jun.-Prof. Matthias Hein Program of today/next lecture Lagrangian and duality: the Lagrangian the dual

More information

Lecture: Introduction to LP, SDP and SOCP

Lecture: Introduction to LP, SDP and SOCP Lecture: Introduction to LP, SDP and SOCP Zaiwen Wen Beijing International Center For Mathematical Research Peking University http://bicmr.pku.edu.cn/~wenzw/bigdata2015.html wenzw@pku.edu.cn Acknowledgement:

More information

Convex Optimization. Lieven Vandenberghe Electrical Engineering Department, UCLA. Joint work with Stephen Boyd, Stanford University

Convex Optimization. Lieven Vandenberghe Electrical Engineering Department, UCLA. Joint work with Stephen Boyd, Stanford University Convex Optimization Lieven Vandenberghe Electrical Engineering Department, UCLA Joint work with Stephen Boyd, Stanford University Ph.D. School in Optimization in Computer Vision DTU, May 19, 2008 Introduction

More information

Conic Linear Programming. Yinyu Ye

Conic Linear Programming. Yinyu Ye Conic Linear Programming Yinyu Ye December 2004, revised October 2017 i ii Preface This monograph is developed for MS&E 314, Conic Linear Programming, which I am teaching at Stanford. Information, lecture

More information

Part I Formulation of convex optimization problems

Part I Formulation of convex optimization problems Nonlinear Optimization Part I Formulation of convex optimization problems Instituto Superior Técnico Carnegie Mellon University jxavier@isr.ist.utl.pt 1 Convex sets 2 Definition (Convex set) Let V be a

More information

CS295: Convex Optimization. Xiaohui Xie Department of Computer Science University of California, Irvine

CS295: Convex Optimization. Xiaohui Xie Department of Computer Science University of California, Irvine CS295: Convex Optimization Xiaohui Xie Department of Computer Science University of California, Irvine Course information Prerequisites: multivariate calculus and linear algebra Textbook: Convex Optimization

More information

Lecture 9 Sequential unconstrained minimization

Lecture 9 Sequential unconstrained minimization S. Boyd EE364 Lecture 9 Sequential unconstrained minimization brief history of SUMT & IP methods logarithmic barrier function central path UMT & SUMT complexity analysis feasibility phase generalized inequalities

More information

What can be expressed via Conic Quadratic and Semidefinite Programming?

What can be expressed via Conic Quadratic and Semidefinite Programming? What can be expressed via Conic Quadratic and Semidefinite Programming? A. Nemirovski Faculty of Industrial Engineering and Management Technion Israel Institute of Technology Abstract Tremendous recent

More information

EE/AA 578, Univ of Washington, Fall Duality

EE/AA 578, Univ of Washington, Fall Duality 7. Duality EE/AA 578, Univ of Washington, Fall 2016 Lagrange dual problem weak and strong duality geometric interpretation optimality conditions perturbation and sensitivity analysis examples generalized

More information

Conic Linear Programming. Yinyu Ye

Conic Linear Programming. Yinyu Ye Conic Linear Programming Yinyu Ye December 2004, revised January 2015 i ii Preface This monograph is developed for MS&E 314, Conic Linear Programming, which I am teaching at Stanford. Information, lecture

More information

CSCI 1951-G Optimization Methods in Finance Part 10: Conic Optimization

CSCI 1951-G Optimization Methods in Finance Part 10: 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

More information

N. L. P. NONLINEAR PROGRAMMING (NLP) deals with optimization models with at least one nonlinear function. NLP. Optimization. Models of following form:

N. L. P. NONLINEAR PROGRAMMING (NLP) deals with optimization models with at least one nonlinear function. NLP. Optimization. Models of following form: 0.1 N. L. P. Katta G. Murty, IOE 611 Lecture slides Introductory Lecture NONLINEAR PROGRAMMING (NLP) deals with optimization models with at least one nonlinear function. NLP does not include everything

More information

COURSE ON LMI PART I.2 GEOMETRY OF LMI SETS. Didier HENRION henrion

COURSE ON LMI PART I.2 GEOMETRY OF LMI SETS. Didier HENRION   henrion COURSE ON LMI PART I.2 GEOMETRY OF LMI SETS Didier HENRION www.laas.fr/ henrion October 2006 Geometry of LMI sets Given symmetric matrices F i we want to characterize the shape in R n of the LMI set F

More information

Extreme Abridgment of Boyd and Vandenberghe s Convex Optimization

Extreme Abridgment of Boyd and Vandenberghe s Convex Optimization Extreme Abridgment of Boyd and Vandenberghe s Convex Optimization Compiled by David Rosenberg Abstract Boyd and Vandenberghe s Convex Optimization book is very well-written and a pleasure to read. The

More information

13. Convex programming

13. Convex programming CS/ISyE/ECE 524 Introduction to Optimization Spring 2015 16 13. Convex programming Convex sets and functions Convex programs Hierarchy of complexity Example: geometric programming Laurent Lessard (www.laurentlessard.com)

More information

Convex Optimization for Signal Processing and Communications: From Fundamentals to Applications

Convex Optimization for Signal Processing and Communications: From Fundamentals to Applications Convex Optimization for Signal Processing and Communications: From Fundamentals to Applications Chong-Yung Chi Institute of Communications Engineering & Department of Electrical Engineering National Tsing

More information

Lecture: Smoothing.

Lecture: Smoothing. Lecture: Smoothing http://bicmr.pku.edu.cn/~wenzw/opt-2018-fall.html Acknowledgement: this slides is based on Prof. Lieven Vandenberghe s lecture notes Smoothing 2/26 introduction smoothing via conjugate

More information

Solution to EE 617 Mid-Term Exam, Fall November 2, 2017

Solution to EE 617 Mid-Term Exam, Fall November 2, 2017 Solution to EE 67 Mid-erm Exam, Fall 207 November 2, 207 EE 67 Solution to Mid-erm Exam - Page 2 of 2 November 2, 207 (4 points) Convex sets (a) (2 points) Consider the set { } a R k p(0) =, p(t) for t

More information

Course Outline. FRTN10 Multivariable Control, Lecture 13. General idea for Lectures Lecture 13 Outline. Example 1 (Doyle Stein, 1979)

Course Outline. FRTN10 Multivariable Control, Lecture 13. General idea for Lectures Lecture 13 Outline. Example 1 (Doyle Stein, 1979) Course Outline FRTN Multivariable Control, Lecture Automatic Control LTH, 6 L-L Specifications, models and loop-shaping by hand L6-L8 Limitations on achievable performance L9-L Controller optimization:

More information

Convex Functions. Pontus Giselsson

Convex Functions. Pontus Giselsson Convex Functions Pontus Giselsson 1 Today s lecture lower semicontinuity, closure, convex hull convexity preserving operations precomposition with affine mapping infimal convolution image function supremum

More information

Lecture 4: Convex Functions, Part I February 1

Lecture 4: Convex Functions, Part I February 1 IE 521: Convex Optimization Instructor: Niao He Lecture 4: Convex Functions, Part I February 1 Spring 2017, UIUC Scribe: Shuanglong Wang Courtesy warning: These notes do not necessarily cover everything

More information

MOSEK Modeling Cookbook Release 3.0. MOSEK ApS

MOSEK Modeling Cookbook Release 3.0. MOSEK ApS MOSEK Modeling Cookbook Release 3.0 MOSEK ApS 13 August 2018 Contents 1 Preface 1 2 Linear optimization 3 2.1 Introduction................................... 3 2.2 Linear modeling.................................

More information

Lecture: Cone programming. Approximating the Lorentz cone.

Lecture: Cone programming. Approximating the Lorentz cone. Strong relaxations for discrete optimization problems 10/05/16 Lecture: Cone programming. Approximating the Lorentz cone. Lecturer: Yuri Faenza Scribes: Igor Malinović 1 Introduction Cone programming is

More information

Convex Optimization. (EE227A: UC Berkeley) Lecture 28. Suvrit Sra. (Algebra + Optimization) 02 May, 2013

Convex Optimization. (EE227A: UC Berkeley) Lecture 28. Suvrit Sra. (Algebra + Optimization) 02 May, 2013 Convex Optimization (EE227A: UC Berkeley) Lecture 28 (Algebra + Optimization) 02 May, 2013 Suvrit Sra Admin Poster presentation on 10th May mandatory HW, Midterm, Quiz to be reweighted Project final report

More information