Second-order cone programming

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Benjamin Lang
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1 Outline Second-order cone programming, PhD Lehigh University Department of Industrial and Systems Engineering February 10, 2009

2 Outline 1 Basic properties Spectral decomposition The cone of squares The arrowhead operator 2 Notation Optimality conditions barrier functions perturbed optimality The Newton system 3 4

3 Basic properties Spectral decomposition The cone of squares The arrowhead operator A new For u, v R n define: u v = u T v; u 1 v 2:n + v 1 u 2:n ). Theorem Properties of ) 1 Distributive law: u v + w) = u v + u w. 2 Commutative law: u v = v u. 3 The unit element is ι = 1; 0), i.e., u ι = ι u = u. 4 Using the notation u 2 = u u we have u u 2 v) = u 2 u v). 5 Power associativity: u p = u u is well-defined. 6 Associativity does not hold in general.

4 Basic properties Spectral decomposition The cone of squares The arrowhead operator Spectral decomposition Every vector u R n can be written as u = λ 1 c 1) + λ 2 c 2), where c 1) and c 2) are on the boundary of the cone, and c 1)T c 2) = 0 c 1) c 2) = 0 c 1) c 1) = c 1) c 2) c 2) = c 2) c 1) + c 2) = ι c 1), c 2) : Jordan frame λ 1, λ 2 : eigenvalues or spectral values: λ 1,2 u) = u 1 ± u 2:n 2 Naturally: u L λ 1,2 u) 0

5 Basic properties Spectral decomposition The cone of squares The arrowhead operator The cone of squares Theorem A vector x is in a second order cone i.e., x 1 x 2:n 2 ) if and only if it can be written as the square of a vector under the multiplication, i.e., x = u u. u F = λ λ2 2 = 2 u 2, u 2 = max { λ 1, λ 2 } = u 1 + u 2:n 2, u 1 = λ 1 1 c1) + λ 1 2 c2), u 1 2 = λ c1) + λ c 2), where u u 1 = u 1 u = ι and u 1 2 u 1 2 = u.

6 Basic properties Spectral decomposition The cone of squares The arrowhead operator The arrowhead operator Since the mapping v u v is linear, it can be represented with a matrix. u 1 u 2... u n u 2 u 1 Arr u) =...., u n u 1 Now we have u v = Arr u) v = Arr u) Arr v) ι. Quadratic representation: Q u = 2 Arr u) 2 Arr u 2), thus Q u v) = 2u u v) u 2 v is a quadratic function.

7 Primal-dual interior-point methods: notation Outline Notation Optimality conditions barrier functions perturbed optimality The Newton system K = L n1 L nk, A = A 1,..., A k), x = x 1 ;... ; x k), s = s 1 ;... ; s k), c = c 1 ;... ; c k). With this notation we can write k Ax = A i x i, i=1 ) A T y = A 1 T y;,... ; A k T y. Arr u) and Q u are block diagonal matrices built from the blocks Arr u i) and Q u i, respectively.

8 Notation Optimality conditions barrier functions perturbed optimality The Newton system Duality and optimality Weak duality always holds Primal dual) strict feasibility implies strong duality and dual primal) solvability Under strong duality, the optimality conditions for second order conic optimization are Ax = b, x K A T y + s = c, s K x s = 0. An equivalent form of the complementarity condition is c T x b T y = x T s = 0.

9 Notation Optimality conditions barrier functions perturbed optimality The Newton system The central path using barrier functions If x int L, consider ) φx) = ln x 2 1 x 2:n 2 2 = ln λ 1 x) ln λ 2 x), Goes to if x is getting close to the boundary of the cone. Derivatives: φx) = 2 x 1; x 2:n ) T x 2 1 x 2:n 2 2 = 2 x 1) T, where the inverse is taken in the Jordan algebra.

10 Notation Optimality conditions barrier functions perturbed optimality The Newton system The central path Perturbed optimality conditions: Ax = b, x K A T y + s = c, s K where ι i = 1; 0;... ; 0) R n i. Newton system: x i s i = 2µι i, i = 1,..., k, A x = 0 A T y + s = 0, x i s i + x i s i = 2µι i x i s i, i = 1,..., k, where x = x 1 ;... ; x k ) and s = s 1 ;... ; s k ).

11 Newton system - rewritten Outline Notation Optimality conditions barrier functions perturbed optimality The Newton system A T A Arr s) I Arr x) y x s = 0 0 2µι x s, where ι = ι 1 ;... ; ι k ). Eliminating x and s: A Arr s) 1 Arr x) A T ) y = A Arr s) 1 2µι x s). Problems: Not symmetric May be singular Solution: symmetrization!

12 Outline min Q p 1c ) T Qp x) ) AQp 1 Qp x) = b Q p x K max b T y ) T AQp 1 y + Qp 1s = Q p 1c Q p 1s K Lemma If p int K, then 1 Q p Q p 1 = I. 2 The cone K is invariant, i.e., Q p K) = K. 3 The scaled and the original problems are equivalent.

13 Scaled optimality conditions Outline ) AQp 1 Qp x) = 0 ) T AQp 1 y + Qp 1 s = 0, Q p x) Q p 1 s ) + Q p x) Q p 1s ) = 2µι Q p x) Q p 1s ). Simplifies to A x = 0 A T y + s = 0, Q p x) Q p 1 s ) + Q p x) Q p 1s ) = 2µι Q p x) Q p 1s ). The last equation cannot be simplified!

14 The choice of p AHO: p = ι: does not provide a nonsingular Newton system HKM: p = s 1/2 or p = x 1/2, in which case Q p 1s = ι or Q p x = ι. Implemented in SDPT3. NT: Most popular one. p = Q x 1/2 Q x 1/2s) 1/2) 1/2 = Q s 1/2 Q s 1/2x) 1/2) 1/2. Simplifies to Q p x = Q p 1s. Implemented in SeDuMi, MOSEK, SDPT3.

15 Centrality measures µx, s) = k i=1 x it s i n i. w = w 1 ;... ; w k ), where w i = Q 1/2 x s i. i δ F x, s) := Q x 1/2 s µι F := k λ 1 w i ) µ) 2 + λ 2 w i ) µ) 2 i=1 δ x, s) := Q x 1/2 s µι 2 := max { λ 1w i ) µ, λ 2 w i ) µ } i=1,...,k δ x, s) := Qx 1/2 s µι) := µ min {λ 1w i ), λ 2 w 2 )}, i=1,...,k Neighbourhoods δ x, s) δ x, s) δ F x, s). N γ) := {x, y, s) strictly feasible : δx, s) γµx, s)}. δx, s) = δ F x, s): narrow neighbourhood δx, s) = δ x, s) wide neighbourhood

16 IPM for SOCP Theorem Short-step IPM for SOCO) Choose γ = and ζ = Assume that we have a starting point x 0, y 0, s 0 ) N F γ). Compute the Newton step from the scaled Newton ) system. In every iteration, µ is decreased to 1 ζ k µ, i.e., θ = ζ k, and the stepsize is α = 1. This finds an ε-optimal solution for the second order conic optimization problem with k second order cones in at most ) k 1 O log ε iterations. Independent of m, n!) The cost of one iteration is ) k O m 3 + m 2 n +. i=1 n 2 i

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