OPTIMIZATION PROBLEMS IN MASS TRANSPORTATION THEORY ...

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We have then the optimization problem{min W p (f + , f − ) + H(f + ) + G(f − ) :}f + , f − probabilities on Ω .Theorem There exists an optimal pair(f + , f − ) solving the problem above.Also in this case we obtain some necessaryconditions of optimality. In particular, ifΩ is sufficiently large, the optimal structureof the city consists of a finite number ofdisjoint subcities: circular residential areaswith a service pole at the center.39
Problem 4 - Optimal Riemannian MetricsHere the domain Ω and the probabilities f +and f − are given, whereas the distance dis supposed to be conformally flat, that isgenerated by a coefficient a(x) through theformula{ ∫ 1d a (x, y) = inf a ( γ(t) ) |γ ′ (t)| dt :0}γ ∈ Lip(]0, 1[; Ω), γ(0) = x, γ(1) = y.We can then consider the cost functionalF (a) = MK(f + , f − , d a ).The goal is to prevent as much as possiblethe transportation of f + onto f − by maximizingthe cost F (a) among the admissiblecoefficients a(x). Of course, increasing a(x)would increase the values of the distance d a40
Page 1 and 2: OPTIMIZATION PROBLEMS INMASS TRANSP
Page 3 and 4: The Monge problem is thenmin{ ∫ d
Page 5 and 6: Then the class of admissible transp
Page 7 and 8: Monge-Kantorovich problem:{ ∫ }mi
Page 9 and 10: Shape optimization problemsThere is
Page 12 and 13: The shape optimization problem abov
Page 14 and 15: To overcome some of these difficult
Page 16 and 17: In the mass optimization problem th
Page 18 and 19: More precisely, we consider the geo
Page 20 and 21: produces an optimal µ which has a
Page 22 and 23: Variational integrals w.r.t. a meas
Page 24 and 25: Here are some cases where the optim
Page 26 and 27: -2 -1.5 -1 -0.5 0 0.5 1 1.5 21.510.
Page 28 and 29: Problem 1 - Optimal NetworksWe cons
Page 30 and 31: •Large town policy: several ticke
Page 32 and 33: It is interesting to study the opti
Page 34 and 35: The function B can be seen as a con
Page 36 and 37: Here is another case, with a single
Page 38 and 39: • there is a transportation cost
Page 42 and 43: and so the value of the cost F (a).
Page 44 and 45: is a delicate matter. Indeed, maxim
Page 46 and 47: Some Numerical ComputationsHere are
Page 48 and 49: Optimal sets of length 0.5 and 1 in
Page 50 and 51: Now we give a model that takes into
Page 52 and 53: We also give a model where the oppo
Page 54 and 55: Theorem Assume:• J is lower semic
Page 56 and 57: ∫∫ ∫J(µ) = f(µ a )dm+ f ∞
Page 58 and 59: Concentration case. The following f
Page 60 and 61: Diffusion case. The following facts
Page 62 and 63: Theorem Let (X, d, d ′ ) be a met
Page 64 and 65: • if q < 1 + 1/N for every µ 0 a

OPTIMIZATION PROBLEMS IN MASS TRANSPORTATION THEORY ...

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