v2010.10.26 - Convex Optimization
v2010.10.26 - Convex Optimization v2010.10.26 - Convex Optimization
430 CHAPTER 5. EUCLIDEAN DISTANCE MATRIXdistances d i and given constant matrices A k (950) and symmetric matricesΦ i (889) and B j per (919), then a molecular conformation problem can beexpressed:findG ℵ ∈S M , G X ∈S N , Z∈R M×N G Xsubject to d i ≤ tr(G X Φ i ) ≤ d i , ∀i ∈ I 1cos ϕ j ≤ tr(G ℵ B j ) ≤ cos ϕ j , ∀j ∈ I 2〈Z , A k 〉 = 0 , ∀k ∈ I 3G X 1 = 0δ(G ℵ ) = 1[ ]Gℵ ZZ T ≽ 0G X[ ]Gℵ ZrankZ T = 3G X(952)where G X 1=0 provides a geometrically centered list X (5.4.2.2). Ignoringthe rank constraint would tend to force cross-product matrix Z to zero.What binds these three variables is the rank constraint; we show how tosatisfy it in4.4.5.4.3 Inner-product form EDM definitionWe might, for example, want to realize a constellation given onlyinterstellar distance (or, equivalently, parsecs from our Sun andrelative angular measurement; the Sun as vertex to two distantstars); called stellar cartography.(p.22)Equivalent to (887) is [380,1-7] [331,3.2]d ij = d ik + d kj − 2 √ d ik d kj cos θ ikj= [√ d ik√dkj] [ 1 −e ıθ ikj−e −ıθ ikj1] [√ ]d ik√dkj(953)called law of cosines where ı √ −1 , i , j , k are positive integers, and θ ikjis the angle at vertex x k formed by vectors x i − x k and x j − x k ;cos θ ikj =1(d 2 ik + d kj − d ij )√ = (x i − x k ) T (x j − x k )dik d kj ‖x i − x k ‖ ‖x j − x k ‖(954)
5.4. EDM DEFINITION 431where ([√the numerator ]) forms an inner product of vectors. Distance-squaredikd ij √dkj is a convex quadratic function 5.23 on R 2 + whereas d ij (θ ikj ) isquasiconvex (3.8) minimized over domain {−π ≤θ ikj ≤π} by θ ⋆ ikj =0, weget the Pythagorean theorem when θ ikj = ±π/2, and d ij (θ ikj ) is maximizedwhen θ ⋆ ikj =±π ; d ij = (√ d ik + √ ) 2,d kj θikj = ±πsod ij = d ik + d kj , θ ikj = ± π 2d ij = (√ d ik − √ d kj) 2,θikj = 0(955)| √ d ik − √ d kj | ≤ √ d ij ≤ √ d ik + √ d kj (956)Hence the triangle inequality, Euclidean metric property 4, holds for anyEDM D .We may construct an inner-product form of the EDM definition formatrices by evaluating (953) for k=1 : By defining⎡√ √ √ ⎤d 12 d12 d 13 cos θ 213 d12 d 14 cos θ 214 · · · d12 d 1N cos θ 21N√ √ √ d12 d 13 cos θ 213 d 13 d13 d 14 cos θ 314 · · · d13 d 1N cos θ 31NΘ T Θ √ √ √ d12 d 14 cos θ 214 d13 d 14 cos θ 314 d 14... d14 d 1N cos θ 41N∈ S N−1⎢⎥⎣ ........√ √. ⎦√d12 d 1N cos θ 21N d13 d 1N cos θ 31N d14 d 1N cos θ 41N · · · d 1Nthen any EDM may be expressed[ ]0D(Θ) δ(Θ T 1Θ)T + 1 [ [0 δ(Θ T Θ) T] 0 0T− 20 Θ T Θ=[0 δ(Θ T Θ) Tδ(Θ T Θ) δ(Θ T Θ)1 T + 1δ(Θ T Θ) T − 2Θ T Θ]](957)∈ EDM N(958)EDM N = { D(Θ) | Θ ∈ R N−1×N−1} (959)for which all Euclidean metric properties hold. Entries of Θ T Θ result fromvector inner-products as in (954); id est,[]5.23 1 −e ıθ ikj−e −ıθ ≽ 0, having eigenvalues {0,2}. Minimum is attained forikj1[ √ ] {dik√dkjµ1, µ ≥ 0, θikj = 0=0, −π ≤ θ ikj ≤ π , θ ikj ≠ 0 . (D.2.1, [61, exmp.4.5])
- Page 379 and 380: 4.6. CARDINALITY AND RANK CONSTRAIN
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- Page 409 and 410: 5.4. EDM DEFINITION 4095.4.2.3.1 Ex
- Page 411 and 412: 5.4. EDM DEFINITION 411is the fact:
- Page 413 and 414: 5.4. EDM DEFINITION 413Figure 120:
- Page 415 and 416: 5.4. EDM DEFINITION 415is found fro
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- Page 419 and 420: 5.4. EDM DEFINITION 419equality con
- Page 421 and 422: 5.4. EDM DEFINITION 421How much dis
- Page 423 and 424: 5.4. EDM DEFINITION 423105ˇx 4ˇx
- Page 425 and 426: 5.4. EDM DEFINITION 425now implicit
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- Page 433 and 434: 5.4. EDM DEFINITION 433because (A.3
- Page 435 and 436: 5.5. INVARIANCE 4355.5.1.0.1 Exampl
- Page 437 and 438: 5.5. INVARIANCE 437x 2 x 2x 3 x 1 x
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- Page 465 and 466: 5.10. EDM-ENTRY COMPOSITION 465of a
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- Page 469 and 470: 5.11. EDM INDEFINITENESS 4695.11.1
- Page 471 and 472: 5.11. EDM INDEFINITENESS 471we have
- Page 473 and 474: 5.11. EDM INDEFINITENESS 473So beca
- Page 475 and 476: 5.11. EDM INDEFINITENESS 475holds o
- Page 477 and 478: 5.12. LIST RECONSTRUCTION 477where
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5.4. EDM DEFINITION 431where ([√the numerator ]) forms an inner product of vectors. Distance-squaredikd ij √dkj is a convex quadratic function 5.23 on R 2 + whereas d ij (θ ikj ) isquasiconvex (3.8) minimized over domain {−π ≤θ ikj ≤π} by θ ⋆ ikj =0, weget the Pythagorean theorem when θ ikj = ±π/2, and d ij (θ ikj ) is maximizedwhen θ ⋆ ikj =±π ; d ij = (√ d ik + √ ) 2,d kj θikj = ±πsod ij = d ik + d kj , θ ikj = ± π 2d ij = (√ d ik − √ d kj) 2,θikj = 0(955)| √ d ik − √ d kj | ≤ √ d ij ≤ √ d ik + √ d kj (956)Hence the triangle inequality, Euclidean metric property 4, holds for anyEDM D .We may construct an inner-product form of the EDM definition formatrices by evaluating (953) for k=1 : By defining⎡√ √ √ ⎤d 12 d12 d 13 cos θ 213 d12 d 14 cos θ 214 · · · d12 d 1N cos θ 21N√ √ √ d12 d 13 cos θ 213 d 13 d13 d 14 cos θ 314 · · · d13 d 1N cos θ 31NΘ T Θ √ √ √ d12 d 14 cos θ 214 d13 d 14 cos θ 314 d 14... d14 d 1N cos θ 41N∈ S N−1⎢⎥⎣ ........√ √. ⎦√d12 d 1N cos θ 21N d13 d 1N cos θ 31N d14 d 1N cos θ 41N · · · d 1Nthen any EDM may be expressed[ ]0D(Θ) δ(Θ T 1Θ)T + 1 [ [0 δ(Θ T Θ) T] 0 0T− 20 Θ T Θ=[0 δ(Θ T Θ) Tδ(Θ T Θ) δ(Θ T Θ)1 T + 1δ(Θ T Θ) T − 2Θ T Θ]](957)∈ EDM N(958)EDM N = { D(Θ) | Θ ∈ R N−1×N−1} (959)for which all Euclidean metric properties hold. Entries of Θ T Θ result fromvector inner-products as in (954); id est,[]5.23 1 −e ıθ ikj−e −ıθ ≽ 0, having eigenvalues {0,2}. Minimum is attained forikj1[ √ ] {dik√dkjµ1, µ ≥ 0, θikj = 0=0, −π ≤ θ ikj ≤ π , θ ikj ≠ 0 . (D.2.1, [61, exmp.4.5])