v2010.10.26 - Convex Optimization
v2010.10.26 - Convex Optimization v2010.10.26 - Convex Optimization
718 APPENDIX E. PROJECTIONof relative dual cone K ∗ ∩aff K=cone(A †T ) (2.13.9.4) correspond to thelinearly independent (B.1.1.1) rows of A † . Directions of nonorthogonalprojection are determined by the pseudoinverse; id est, direction ofprojection a i a ∗Ti x−x on R(a i ) is orthogonal to a ∗ i . E.9Because the extreme directions of this cone K are linearly independent,the component projections are unique in the sense:there is only one linear combination of extreme directions of K thatyields a particular point x∈ R(A) wheneverR(A) = aff K = R(a 1 ) ⊕ R(a 2 ) ⊕ ... ⊕ R(a n ) (1940)E.5.0.0.4 Example. Nonorthogonal projection on elementary matrix.Suppose P Y is a linear nonorthogonal projector projecting on subspace Y ,and suppose the range of a vector u is linearly independent of Y ; id est,for some other subspace M containing Y supposeM = R(u) ⊕ Y (1941)Assuming P M x = P u x + P Y x holds, then it follows for vector x∈MP u x = x − P Y x , P Y x = x − P u x (1942)nonorthogonal projection of x on R(u) can be determined fromnonorthogonal projection of x on Y , and vice versa.Such a scenario is realizable were there some arbitrary basis for Ypopulating a full-rank skinny-or-square matrix AA [ basis Y u ] ∈ R N×n+1 (1943)Then P M =AA † fulfills the requirements, with P u =A(:,n + 1)A † (n + 1,:)and P Y =A(:,1:n)A † (1:n,:). Observe, P M is an orthogonal projectorwhereas P Y and P u are nonorthogonal projectors.Now suppose, for example, P Y is an elementary matrix (B.3); inparticular,P Y = I − e 1 1 T =[0 √ ]2V N ∈ R N×N (1944)E.9 This remains true in high dimension although only a little more difficult to visualizein R 3 ; confer , Figure 64.
E.5. PROJECTION EXAMPLES 719where Y = N(1 T ). We have M = R N , A = [ √ 2V N e 1 ] , and u = e 1 .Thus P u = e 1 1 T is a nonorthogonal projector projecting on R(u) in adirection parallel to a vector in Y (E.3.5), and P Y x = x − e 1 1 T x is anonorthogonal projection of x on Y in a direction parallel to u . E.5.0.0.5 Example. Projecting origin on a hyperplane.(confer2.4.2.0.2) Given the hyperplane representation having b∈R andnonzero normal a∈ R m ∂H = {y | a T y = b} ⊂ R m (114)orthogonal projection of the origin P0 on that hyperplane is the uniqueoptimal solution to a minimization problem: (1908)‖P0 − 0‖ 2 = infy∈∂H ‖y − 0‖ 2= infξ∈R m−1 ‖Zξ + x‖ 2(1945)where x is any solution to a T y=b , and where the columns of Z ∈ R m×m−1constitute a basis for N(a T ) so that y = Zξ + x ∈ ∂H for all ξ ∈ R m−1 .The infimum can be found by setting the gradient (with respect to ξ) ofthe strictly convex norm-square to 0. We find the minimizing argumentξ ⋆ = −(Z T Z) −1 Z T x (1946)soy ⋆ = ( I − Z(Z T Z) −1 Z T) x (1947)and from (1910)a TP0 = y ⋆ = a(a T a) −1 a T x = a‖a‖ ‖a‖ x =a‖a‖ 2 aT x A † Ax = a b‖a‖ 2 (1948)In words, any point x in the hyperplane ∂H projected on its normal a(confer (1972)) yields that point y ⋆ in the hyperplane closest to the origin.
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E.5. PROJECTION EXAMPLES 719where Y = N(1 T ). We have M = R N , A = [ √ 2V N e 1 ] , and u = e 1 .Thus P u = e 1 1 T is a nonorthogonal projector projecting on R(u) in adirection parallel to a vector in Y (E.3.5), and P Y x = x − e 1 1 T x is anonorthogonal projection of x on Y in a direction parallel to u . E.5.0.0.5 Example. Projecting origin on a hyperplane.(confer2.4.2.0.2) Given the hyperplane representation having b∈R andnonzero normal a∈ R m ∂H = {y | a T y = b} ⊂ R m (114)orthogonal projection of the origin P0 on that hyperplane is the uniqueoptimal solution to a minimization problem: (1908)‖P0 − 0‖ 2 = infy∈∂H ‖y − 0‖ 2= infξ∈R m−1 ‖Zξ + x‖ 2(1945)where x is any solution to a T y=b , and where the columns of Z ∈ R m×m−1constitute a basis for N(a T ) so that y = Zξ + x ∈ ∂H for all ξ ∈ R m−1 .The infimum can be found by setting the gradient (with respect to ξ) ofthe strictly convex norm-square to 0. We find the minimizing argumentξ ⋆ = −(Z T Z) −1 Z T x (1946)soy ⋆ = ( I − Z(Z T Z) −1 Z T) x (1947)and from (1910)a TP0 = y ⋆ = a(a T a) −1 a T x = a‖a‖ ‖a‖ x =a‖a‖ 2 aT x A † Ax = a b‖a‖ 2 (1948)In words, any point x in the hyperplane ∂H projected on its normal a(confer (1972)) yields that point y ⋆ in the hyperplane closest to the origin.