Selections associated to the metric projection

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Authors

Stefan Cobzas
“Tiberiu Popovicviu” Institute of Numerical Analysis, Romanian Academy, Romania

Costica Mustata
“Tiberiu Popovicviu” Institute of Numerical Analysis, Romanian Academy, Romania

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Şt. Cobzaş, C. Mustăţa, Selections associated to the metric projection, Rev. Anal. Numér. Théor. Approx., 24 (1995) nos. 1-2, 45-52.

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Revue d’Analyse Numer. Theor. Approx.

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Publishing Romanian Academy

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2457-6794

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2501-059X

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[1] E.W. Cheney and D.E. Wulbert, The Existence and Unicity of Best Approximations, Math. Scand. 24(1969), 113-140.
[2] F. Deutsch, Linear Selections for the Metric Projection, J. Funct. Anal. 49(1983), 269-292.
[3] F. Deutsch, Wu Li, Sung-Ho Park, Characterizations of Continuous and Lipschitz Continuous Metric Selections in Normed Linear Spaces, J.A.T., 58 (1989), 297-314.
[4] C. Mustata, On the Selections Associated to the Metric Projecitons, Revue d’Analyse Numerique et de Theorie l’Approximation 23 (I) (1994), 89-93.

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1995-Mustata-Selections associated to the metric projection-Jnaat

SELECTIONS ASSOCIATED TO THE METRIC PROJECTION

S. COBZAŞ, C. MUSTĂTA

(Cluj-Napoca)
Let X X XXX be a normed space, M M MMM a subspace of X X XXX and x x xxx an element of X X XXX. The distance from x x xxx to M M MMM is defined by
(1) d ( x , M ) := inf { x y : y M } . (1) d ( x , M ) := inf { x y : y M } . {:(1)d(x","M):=i n f{||x-y||:y in M}.:}\begin{equation*} \mathrm{d}(x, M):=\inf \{\|x-y\|: y \in M\} . \tag{1} \end{equation*}(1)d(x,M):=inf{xy:yM}.
An element y M y M y in My \in MyM verifying the equality x y = d ( x , M ) x y = d ( x , M ) ||x-y||=d(x,M)\|x-y\|=d(x, M)xy=d(x,M) is called an element of best approximation for x x xxx by elements in M M MMM. The set of all elements of best approximation for x x xxx is denoted by P M ( x ) P M ( x ) P_(M)(x)P_{M}(x)PM(x), i.e.
(2) P M ( x ) := { y M : x y = d ( x , M ) } . (2) P M ( x ) := { y M : x y = d ( x , M ) } . {:(2)P_(M)(x):={y in M:||x-y||=d(x","M)}.:}\begin{equation*} P_{M}(x):=\{y \in M:\|x-y\|=d(x, M)\} . \tag{2} \end{equation*}(2)PM(x):={yM:xy=d(x,M)}.
If P M ( x ) P M ( x ) P_(M)(x)!=O/P_{M}(x) \neq \varnothingPM(x) (respectively P M ( x ) P M ( x ) P_(M)(x)P_{M}(x)PM(x) is a singleton) for all x X x X x in Xx \in XxX, then M M MMM is called a proximinal (respectively a Chebyshevian) subspace of X X XXX.
The set-valued application P M : X 2 M P M : X 2 M P_(M):X rarr2^(M)P_{M}: X \rightarrow 2^{M}PM:X2M is called the metric projection of X X XXX on M M MMM and a function p : X M p : X M p:X rarr Mp: X \rightarrow Mp:XM such that p ( x ) P M ( x ) p ( x ) P M ( x ) p(x)inP_(M)(x)p(x) \in P_{M}(x)p(x)PM(x), for all x X x X x in Xx \in XxX, is called a selection for the metric projection P M P M P_(M)P_{M}PM. Observe that the existence of a selection for P M P M P_(M)P_{M}PM implies P M ( x ) P M ( x ) P_(M)(x)!=O/P_{M}(x) \neq \varnothingPM(x), for all x X x X x in Xx \in XxX, i.e. the subspace M M MMM is necessarily proximinal.
The set
(3) Ker P M := { x X : 0 P M ( x ) } , (3) Ker P M := x X : 0 P M ( x ) , {:(3)KerP_(M):={x in X:0inP_(M)(x)}",":}\begin{equation*} \operatorname{Ker} P_{M}:=\left\{x \in X: 0 \in P_{M}(x)\right\}, \tag{3} \end{equation*}(3)KerPM:={xX:0PM(x)},
is called the kernel of the metric projection P M P M P_(M)P_{M}PM.
In many situations for a given subspace M M MMM of X X XXX the problem of best approximation is not considered for the whole space X X XXX but rather for a subset K K KKK of X X XXX. This is the case which we consider in this paper and to this end we need some definitions and notation.
If K K KKK is a subset of the normed space X and P M ( x ) P M ( x ) P_(M)(x)!=O/P_{M}(x) \neq \varnothingPM(x) (respectively P M ( x ) P M ( x ) P_(M)(x)P_{M}(x)PM(x) is a singleton) for all x K x K x in Kx \in KxK, then the subspace M M MMM is called K K KKK-proximinal (respectively K K KKK-Chebyshevian). The restriction of the metric projection P M P M P_(M)P_{M}PM to K K KKK is denoted by P M K P M K P_(M∣K)P_{M \mid K}PMK and its kernel by Ker P M K Ker P M K KerP_(M∣K)\operatorname{Ker} P_{M \mid K}KerPMK :
Ker P M K := { x K : O P M ( x ) } . Ker P M K := x K : O P M ( x ) . KerP_(M∣K):={x in K:O inP_(M)(x)}.\operatorname{Ker} P_{M \mid K}:=\left\{x \in K: O \in P_{M}(x)\right\} .KerPMK:={xK:OPM(x)}.
For two nonvoid subsets U , V U , V U,VU, VU,V of X X XXX denote by U + V := { u + v : u U , v V } U + V := { u + v : u U , v V } U+V:={u+v:u in U,v in V}U+V:=\{u+v: u \in U, v \in V\}U+V:={u+v:uU,vV} their algebraic sum. If every x U + V x U + V x in U+Vx \in U+VxU+V can be uniquely written in the form x = u + v x = u + v x=u+vx=u+vx=u+v with u U u U u in Uu \in UuU and v V v V v in Vv \in VvV, then U + V U + V U+VU+VU+V is called the direct algebraic sum of the sets U U UUU and V V VVV and is denoted by U + V U + V U+VU+VU+V. If K = U + V K = U + V K=U+VK=U+VK=U+V and the application ( u , v ) u + v , u U , v V ( u , v ) u + v , u U , v V (u,v)rarr u+v,u in U,v in V(u, v) \rightarrow u+v, u \in U, v \in V(u,v)u+v,uU,vV, is a topological homeomorphism between U × V U × V U xx VU \times VU×V (endowed with the product topology) and K K KKK then K K KKK is called the direct topological sum of the sets U U UUU and V V VVV, denoted by K = U V K = U V K=U o+VK=U \oplus VK=UV.
F. Deutsch [2] proved that if M M MMM is a proximinal subspace of X X XXX then the metric projection P M P M P_(M)P_{M}PM admits a continuous and linear selection if and only if the subspace M M MMM is complemented in X X XXX by a closed subspace of Ker P M Ker P M KerP_(M)\operatorname{Ker} P_{M}KerPM ([2], Theorem 2.2).
In [4], one of the authors of the present paper considered a similar problem for a closed convex cone K K KKK in X X XXX and a K K KKK-proximinal subspace M M MMM of X X XXX, asking for P M { K P M { K P_(M{K)P_{M\{K}PM{K to admit a continuous, positively homogeneous and additive selection. The following sufficient condition for the existence of such a selection was obtained:
If there exist two closed convex cones C Ker P M C Ker P M C sub KerP_(M)C \subset \operatorname{Ker} P_{M}CKerPM and U M U M U sub MU \subset MUM, such that K = C U K = C U K=C o+UK=C \oplus UK=CU, then the metric projection P M J K P M J K P_(MJK)P_{M J K}PMJK admits a continuous, positively homogeneous and additive selection ([4], Theorem A).
This condition is not necessary for the existence of such a selection. In this paper we shall give a reformulation (called Theorem A A A^(')A^{\prime}A ) of Theorem A A AAA from [4] and prove that if P M K P M K P_(M∣K)P_{M \mid K}PMK admits a continuous, positively homogeneous and additive selection, satisfying some suplementary conditions, then the cone K K KKK admits a decompositions K = C U K = C U K=C o+UK=C \oplus UK=CU, with C Ker P M K C Ker P M K C sub KerP_(M∣K)C \subset \operatorname{Ker} P_{M \mid K}CKerPMK and U M U M U sub MU \subset MUM, closed convex cones (Theorem B). Although the conditions in theorems A' and B are very close to be necessary and sufficient for the existence of a continuous, positively homogeneous and additive selection for P M K P M K P_(M∣K)P_{M \mid K}PMK, we weren't able to find such conditions. Some situations which may occur are illustrated by some examples following Theorem B.
By a convex cone in X X XXX we understand a nonvoid subset K K KKK of X X XXX such that:
a) x 1 + x 2 K x 1 + x 2 K x_(1)+x_(2)in Kx_{1}+x_{2} \in Kx1+x2K, for all x 1 , x 2 K x 1 , x 2 K x_(1),x_(2)in Kx_{1}, x_{2} \in Kx1,x2K, and
b) λ x K λ x K lambda*x in K\lambda \cdot x \in KλxK, for all x K x K x in Kx \in KxK, and λ 0 λ 0 lambda >= 0\lambda \geq 0λ0.
A carefull examination of the statement and of the proof of Theorem A A AAA in [4] yields the following more detailed reformulation:
Theorem A'. Let M M MMM be a closed linear subspace of a normed space X X XXX and K K KKK a closed convex cone in X X XXX. If there exist two closed convex cones C Ker P M / K C Ker P M / K C sub KerP_(M//K)C \subset \operatorname{Ker} P_{M / K}CKerPM/K and U M U M U sub MU \subset MUM such that K = C U K = C U K=C o+UK=C \oplus UK=CU, then the application p : K M p : K M p:K rarr Mp: K \rightarrow Mp:KM,
defined by p ( x ) = z p ( x ) = z p(x)=zp(x)=zp(x)=z, for x = y + z K , y C , z U x = y + z K , y C , z U x=y+z in K,y in C,z in Ux=y+z \in K, y \in C, z \in Ux=y+zK,yC,zU, is a continuous, positively homogeneous and additive selection of the metric projection P M K P M K P_(M∣K)P_{M \mid K}PMK. The subspace M M MMM is K K KKK-proximinal and C = p 1 ( 0 ) , U = p ( K ) C = p 1 ( 0 ) , U = p ( K ) C=p^(-1)(0),U=p(K)C=p^{-1}(0), U=p(K)C=p1(0),U=p(K).
The following theorem shows that, in some cases, the existence of a continuous, positively homogeneous and additive selection for P M K P M K P_(M∣K)P_{M \mid K}PMK implies the decomposability of K in the form K = C U K = C U K=C o+UK=C \oplus UK=CU, with C C CCC and U U UUU closed convex cones.
THEOREM B. Let X X XXX be a normed space, M M MMM a closed subspace of X X XXX and K K KKK a closed convex cone in X X XXX. Suppose that the metric projection P M K P M K P_(M∣K)P_{M \mid K}PMK admits a continuous, positively homogeneous and additive selection p p ppp such that:
a) p ( K ) p ( K ) p(K)p(K)p(K) is closed and contained in K K KKK, and
b) x p ( x ) K x p ( x ) K x-p(x)in Kx-p(x) \in Kxp(x)K, for all x K x K x in Kx \in KxK.
Then p 1 ( 0 ) p 1 ( 0 ) p^(-1)(0)p^{-1}(0)p1(0) and p ( K ) p ( K ) p(K)p(K)p(K) are closed convex cones contained in Ker P M K Ker P M K KerP_(M∣K)\operatorname{Ker} P_{M \mid K}KerPMK and M M MMM respectively, and K = p 1 ( 0 ) p ( K ) K = p 1 ( 0 ) p ( K ) K=p^(-1)(0)o+p(K)K=p^{-1}(0) \oplus p(K)K=p1(0)p(K).
If p ( K ) p ( K ) p(K)p(K)p(K) is a closed subspace of K K KKK or M K M K M sub KM \subset KMK then the conditions a) and b) are automatically fulfilled.
Proof. By the additivity, positive homogeneity of p p ppp and the fact that K K KKK is a convex cone, it follows immediately that p ( K ) p ( K ) p(K)p(K)p(K) is a convex cone contained in M M MMM. By hypothesis a a aaa ) it is also closed.
By the continuity of p p ppp the set p 1 ( 0 ) Ker P M K p 1 ( 0 ) Ker P M K p^(-1)(0)sub KerP_(M∣K)p^{-1}(0) \subset \operatorname{Ker} P_{M \mid K}p1(0)KerPMK is closed. If y p 1 ( 0 ) y p 1 ( 0 ) y inp^(-1)(0)y \in p^{-1}(0)yp1(0) and λ 0 λ 0 lambda >= 0\lambda \geq 0λ0 then p ( λ y ) = λ p ( y ) = 0 p ( λ y ) = λ p ( y ) = 0 p(lambda*y)=lambda*p(y)=0p(\lambda \cdot y)=\lambda \cdot p(y)=0p(λy)=λp(y)=0, showing that λ y p 1 ( 0 ) λ y p 1 ( 0 ) lambda*y inp^(-1)(0)\lambda \cdot y \in p^{-1}(0)λyp1(0). Similarly, y 1 , y 2 p 1 ( 0 ) y 1 , y 2 p 1 ( 0 ) y_(1),y_(2)inp^(-1)(0)y_{1}, y_{2} \in p^{-1}(0)y1,y2p1(0) and the additivity of p p ppp imply p ( y 1 + y 2 ) = p ( y 1 ) + p ( y 2 ) = 0 p y 1 + y 2 = p y 1 + p y 2 = 0 p(y_(1)+y_(2))=p(y_(1))+p(y_(2))=0p\left(y_{1}+y_{2}\right)=p\left(y_{1}\right)+p\left(y_{2}\right)=0p(y1+y2)=p(y1)+p(y2)=0, showing that p 1 ( 0 ) p 1 ( 0 ) p^(-1)(0)p^{-1}(0)p1(0) is a closed convex cone contained in Ker P M K Ker P M K KerP_(M∣K)\operatorname{Ker} P_{M \mid K}KerPMK.
Now we prove that K = p 1 ( 0 ) + p ( K ) K = p 1 ( 0 ) + p ( K ) K=p^(-1)(0)+p(K)K=p^{-1}(0)+p(K)K=p1(0)+p(K). If x K x K x in Kx \in KxK then by Condition b b bbb ), y := x p ( x ) K y := x p ( x ) K y:=x-p(x)in Ky:=x-p(x) \in Ky:=xp(x)K. By Condition a), p ( x ) K p ( x ) K p(x)in Kp(x) \in Kp(x)K implying x = y + p ( x ) x = y + p ( x ) x=y+p(x)x=y+p(x)x=y+p(x) with y , p ( x ) K y , p ( x ) K y,p(x)in Ky, p(x) \in Ky,p(x)K : Using the additivity of the function p p ppp and the fact that p ( p ( x ) ) = p ( x ) p ( p ( x ) ) = p ( x ) p(p(x))=p(x)p(p(x))=p(x)p(p(x))=p(x) (in fact p ( m ) = m p ( m ) = m p(m)=mp(m)=mp(m)=m for all m M m M m in Mm \in MmM ) we obtain p ( x ) = p ( y ) + p ( p ( x ) ) = p ( y ) + p ( x ) p ( x ) = p ( y ) + p ( p ( x ) ) = p ( y ) + p ( x ) p(x)=p(y)+p(p(x))=p(y)+p(x)p(x)=p(y)+p(p(x))=p(y)+p(x)p(x)=p(y)+p(p(x))=p(y)+p(x). It follows p ( y ) = 0 p ( y ) = 0 p(y)=0p(y)=0p(y)=0, i.e. y p 1 ( 0 ) y p 1 ( 0 ) y inp^(-1)(0)y \in p^{-1}(0)yp1(0) and K p 1 ( 0 ) + p ( K ) K p 1 ( 0 ) + p ( K ) K subp^(-1)(0)+p(K)K \subset p^{-1}(0)+p(K)Kp1(0)+p(K). Since p 1 ( 0 ) p 1 ( 0 ) p^(-1)(0)p^{-1}(0)p1(0) and p ( K ) p ( K ) p(K)p(K)p(K) are contained in K K KKK and K K KKK is a convex cone, it follows that p 1 ( 0 ) + p ( K ) K p 1 ( 0 ) + p ( K ) K p^(-1)(0)+p(K)sub Kp^{-1}(0)+p(K) \subset Kp1(0)+p(K)K and K = p 1 ( 0 ) + p ( K ) K = p 1 ( 0 ) + p ( K ) K=p^(-1)(0)+p(K)K=p^{-1}(0)+p(K)K=p1(0)+p(K).
To show that this is a direct algebraic sum, suppose that an element x K x K x in Kx \in KxK admits two representations: x = y + p ( x ) x = y + p ( x ) x=y+p(x)x=y+p(x)x=y+p(x) and x = y + z x = y + z x=y^(')+z^(')x=y^{\prime}+z^{\prime}x=y+z, with y , y p 1 ( 0 ) y , y p 1 ( 0 ) y,y^(')inp^(-1)(0)y, y^{\prime} \in p^{-1}(0)y,yp1(0) and z p ( K ) M z p ( K ) M z^(')in p(K)sub Mz^{\prime} \in p(K) \subset Mzp(K)M. It follows p ( z ) = z p z = z p(z^('))=z^(')p\left(z^{\prime}\right)=z^{\prime}p(z)=z and, by the additivity of p , p ( x ) = p ( y ) + p ( z ) = 0 + z = z p , p ( x ) = p y + p z = 0 + z = z p,p(x)=p(y^('))+p(z^('))=0+z^(')=z^(')p, p(x)=p\left(y^{\prime}\right)+p\left(z^{\prime}\right)=0+z^{\prime}=z^{\prime}p,p(x)=p(y)+p(z)=0+z=z, implying y = x p ( x ) = y y = x p ( x ) = y y^(')=x-p(x)=yy^{\prime}=x-p(x)=yy=xp(x)=y and z = p ( x ) z = p ( x ) z^(')=p(x)z^{\prime}=p(x)z=p(x).
It remains to show that the comespondence ( y , z ) y + z , y p 1 ( 0 ) , z p ( K ) ( y , z ) y + z , y p 1 ( 0 ) , z p ( K ) (y,z)rarr y+z,y inp^(-1)(0),z in p(K)(y, z) \rightarrow y+z, y \in p^{-1}(0), z \in p(K)(y,z)y+z,yp1(0),zp(K), is a homeomophism between p 1 ( 0 ) × p ( K ) p 1 ( 0 ) × p ( K ) p^(-1)(0)xx p(K)p^{-1}(0) \times p(K)p1(0)×p(K), equipped with the product topology,
and K K KKK. To this end consider a sequence ( y n , z n ) p 1 ( 0 ) × p ( K ) , n N y n , z n p 1 ( 0 ) × p ( K ) , n N (y_(n),z_(n))inp^(-1)(0)xx p(K),n in N\left(y_{n}, z_{n}\right) \in p^{-1}(0) \times p(K), n \in N(yn,zn)p1(0)×p(K),nN, converging to ( y , z ) p 1 ( 0 ) × p ( K ) ( y , z ) p 1 ( 0 ) × p ( K ) (y,z)inp^(-1)(0)xx p(K)(y, z) \in p^{-1}(0) \times p(K)(y,z)p1(0)×p(K). It follows y n y y n y y_(n)rarr yy_{n} \rightarrow yyny and z n z z n z z_(n)rarr zz_{n} \rightarrow zznz, implying ( y n , z n ) y + z y n , z n y + z (y_(n),z_(n))rarr y+z\left(y_{n}, z_{n}\right) \rightarrow y+z(yn,zn)y+z, which proves the continuity of the application ( y , z ) y + z ( y , z ) y + z (y,z)rarr y+z(y, z) \rightarrow y+z(y,z)y+z.
To prove the continuity of the inverse application x ( y , z ) x ( y , z ) x|->(y,z)x \mapsto(y, z)x(y,z), where x = y + z , y p 1 ( 0 ) , z p ( K ) x = y + z , y p 1 ( 0 ) , z p ( K ) x=y+z,y inp^(-1)(0),z in p(K)x=y+z, y \in p^{-1}(0), z \in p(K)x=y+z,yp1(0),zp(K), take again a sequence x n = y n + z n K , y n p 1 ( 0 ) , z n p ( K ) x n = y n + z n K , y n p 1 ( 0 ) , z n p ( K ) x_(n)=y_(n)+z_(n)in K,y_(n)inp^(-1)(0),z_(n)in p(K)x_{n}=y_{n}+z_{n} \in K, y_{n} \in p^{-1}(0), z_{n} \in p(K)xn=yn+znK,ynp1(0),znp(K), converging to x = y + z K x = y + z K x=y+z in Kx=y+z \in Kx=y+zK, where y p 1 ( 0 ) y p 1 ( 0 ) y inp^(-1)(0)y \in p^{-1}(0)yp1(0) and z p ( K ) z p ( K ) z in p(K)z \in p(K)zp(K). It follows z n = p ( x n ) , n N , z = p ( x ) z n = p x n , n N , z = p ( x ) z_(n)=p(x_(n)),n in N,z=p(x)z_{n}=p\left(x_{n}\right), n \in N, z=p(x)zn=p(xn),nN,z=p(x), and, by the continuity of the application p , z n = p ( x n ) p ( x n ) = z p , z n = p x n p x n = z p,z_(n)=p(x_(n))rarr p(x_(n))=zp, z_{n}=p\left(x_{n}\right) \rightarrow p\left(x_{n}\right)=zp,zn=p(xn)p(xn)=z. But then y n = x n z n x z = y y n = x n z n x z = y y_(n)=x_(n)-z_(n)rarr x-z=yy_{n}=x_{n}-z_{n} \rightarrow x-z=yyn=xnznxz=y, proving that the sequence ( ( y n , z n ) ) n N y n , z n n N ((y_(n),z_(n)))_(n in N)\left(\left(y_{n}, z_{n}\right)\right)_{n \in N}((yn,zn))nN converges to ( y , z ) ( y , z ) (y,z)(y, z)(y,z) with respect to the product topology of p 1 ( 0 ) × p ( K ) p 1 ( 0 ) × p ( K ) p^(-1)(0)xx p(K)p^{-1}(0) \times p(K)p1(0)×p(K). This shows that the application x ( y , z ) x ( y , z ) x|->(y,z)x \mapsto(y, z)x(y,z), x = y + z p 1 ( 0 ) + p ( K ) x = y + z p 1 ( 0 ) + p ( K ) x=y+z inp^(-1)(0)+p(K)x=y+z \in p^{-1}(0)+p(K)x=y+zp1(0)+p(K), is continuous too and, consequently, the application ( y , z ) y + z ( y , z ) y + z (y,z)|->y+z(y, z) \mapsto y+z(y,z)y+z is a homeomorphism between p 1 ( 0 ) × p ( K ) p 1 ( 0 ) × p ( K ) p^(-1)(0)xx p(K)p^{-1}(0) \times p(K)p1(0)×p(K) and K K KKK.
If p ( K ) p ( K ) p(K)p(K)p(K) is a closed subspace of K K KKK then Condition a) holds and, for x K , p ( x ) x K , p ( x ) x in K,p(x)x \in K, p(x)xK,p(x) and p ( x ) p ( x ) -p(x)-p(x)p(x) are in p ( K ) K p ( K ) K p(K)sub Kp(K) \subset Kp(K)K so that x p ( x ) K x p ( x ) K x-p(x)in Kx-p(x) \in Kxp(x)K, showing that Condition b b bbb ) holds too. If M K M K M sub KM \subset KMK then M = p ( M ) p ( K ) M = p ( M ) p ( K ) M=p(M)sub p(K)M=p(M) \subset p(K)M=p(M)p(K) and, since p ( K ) M p ( K ) M p(K)sub Mp(K) \subset Mp(K)M, it follows that p ( K ) = M p ( K ) = M p(K)=Mp(K)=Mp(K)=M is a closed subspace of K K KKK. Theorem B is completely proved.
Remark. Conditions a a aaa ) and b b bbb ) are fulfilled by the selection p p ppp given in Theorem A'. Indeed, K = p 1 ( 0 ) p ( K ) K = p 1 ( 0 ) p ( K ) K=p^(-1)(0)o+p(K)K=p^{-1}(0) \oplus p(K)K=p1(0)p(K) implies that p ( K ) p ( K ) p(K)p(K)p(K) is a closed convex cone contained in K . Since every x K x K x in Kx \in KxK can be written in the form x = y + z x = y + z x=y+zx=y+zx=y+z with p ( y ) = 0 p ( y ) = 0 p(y)=0p(y)=0p(y)=0 and z = p ( x ) z = p ( x ) z=p(x)z=p(x)z=p(x), it follows that x p ( x ) = x z = y K x p ( x ) = x z = y K x-p(x)=x-z=y in Kx-p(x)=x-z=y \in Kxp(x)=xz=yK for all x K x K x in Kx \in KxK.
In the following examples, there always exists a continuous, positively homogeneous and additive selection of the metric projections but, the equality K = p 1 ( 0 ) p ( K ) K = p 1 ( 0 ) p ( K ) K=p^(-1)(0)o+p(K)K=p^{-1}(0) \oplus p(K)K=p1(0)p(K) is not true in all these cases.
Example 1. Take X = R 2 X = R 2 X=R^(2)X=R^{2}X=R2 with the Euclidean norm and M { ( x 1 , 0 ) : x 1 R } M x 1 , 0 : x 1 R M{(x_(1),0):x_(1)in R}M\left\{\left(x_{1}, 0\right): x_{1} \in R\right\}M{(x1,0):x1R}. Then P M ( ( x 1 , x 2 ) ) = { ( x 1 , 0 ) } P M x 1 , x 2 = x 1 , 0 P_(M)((x_(1),x_(2)))={(x_(1),0)}P_{M}\left(\left(x_{1}, x_{2}\right)\right)=\left\{\left(x_{1}, 0\right)\right\}PM((x1,x2))={(x1,0)}, for all ( x 1 , x 2 ) R 2 x 1 , x 2 R 2 (x_(1),x_(2))inR^(2)\left(x_{1}, x_{2}\right) \in R^{2}(x1,x2)R2, i.e. M M MMM is a Chebyshevian subspace of X X XXX and the only selection of the metric projection is p ( ( x 1 , x 2 ) ) = ( x 1 , 0 ) p x 1 , x 2 = x 1 , 0 p((x_(1),x_(2)))=(x_(1),0)p\left(\left(x_{1}, x_{2}\right)\right)=\left(x_{1}, 0\right)p((x1,x2))=(x1,0), for ( x 1 , x 2 ) R 2 x 1 , x 2 R 2 (x_(1),x_(2))inR^(2)\left(x_{1}, x_{2}\right) \in R^{2}(x1,x2)R2. Let R + 2 := { ( x 1 , x 2 ) R 2 : x 1 0 , x 2 0 } R + 2 := x 1 , x 2 R 2 : x 1 0 , x 2 0 R_(+)^(2):={(x_(1),x_(2))inR^(2):x_(1) >= 0,x_(2) >= 0}R_{+}^{2}:=\left\{\left(x_{1}, x_{2}\right) \in R^{2}: x_{1} \geq 0, x_{2} \geq 0\right\}R+2:={(x1,x2)R2:x10,x20}.
a) Take K = { ( x 1 , x 2 ) : x 1 = x 2 , x 1 0 } K = x 1 , x 2 : x 1 = x 2 , x 1 0 K={(x_(1),x_(2)):x_(1)=x_(2),x_(1) >= 0}K=\left\{\left(x_{1}, x_{2}\right): x_{1}=x_{2}, x_{1} \geq 0\right\}K={(x1,x2):x1=x2,x10}. In this case Ker P M K = { ( 0 , 0 ) } Ker P M K = { ( 0 , 0 ) } KerP_(M∣K)={(0,0)}\operatorname{Ker} P_{M \mid K}=\{(0,0)\}KerPMK={(0,0)} so that the only closed convex cone contained in Ker P M K Ker P M K KerP_(M∣K)\operatorname{Ker} P_{M \mid K}KerPMK is C = { ( 0 , 0 ) } C = { ( 0 , 0 ) } C={(0,0)}C=\{(0,0)\}C={(0,0)}. The subspace M M MMM contains two nontrivial closed cones U + = { ( x 1 , 0 ) : x 1 0 } U + = x 1 , 0 : x 1 0 U_(+)={(x_(1),0):x_(1) >= 0}U_{+}=\left\{\left(x_{1}, 0\right): x_{1} \geq 0\right\}U+={(x1,0):x10} and U = { ( x 1 , 0 ) : x 1 0 } p ( K ) = U + U = x 1 , 0 : x 1 0 p ( K ) = U + U_(-)={(x_(1),0):x_(1) <= 0}quad p(K)=U_(+)U_{-}=\left\{\left(x_{1}, 0\right): x_{1} \leq 0\right\} \quad p(K)=U_{+}U={(x1,0):x10}p(K)=U+and K C U + = U + K C U + = U + K!=C o+U_(+)=U_(+)K \neq C \oplus U_{+}=U_{+}KCU+=U+.
b) Let K = { ( x 1 , x 2 ) R 2 : x 2 x 1 , x 1 0 } K = x 1 , x 2 R 2 : x 2 x 1 , x 1 0 K={(x_(1),x_(2))inR^(2):x_(2) >= x_(1),x_(1) >= 0}K=\left\{\left(x_{1}, x_{2}\right) \in R^{2}: x_{2} \geq x_{1}, x_{1} \geq 0\right\}K={(x1,x2)R2:x2x1,x10}. In this case Ker P M K = { ( 0 , x 2 ) : x 2 0 } P M K = 0 , x 2 : x 2 0 P_(M∣K)={(0,x_(2)):x_(2) >= 0}P_{M \mid K}=\left\{\left(0, x_{2}\right): x_{2} \geq 0\right\}PMK={(0,x2):x20} and the only nontrivial closed convex cone contained in Ker P M K Ker P M K KerP_(M∣K)\operatorname{Ker} P_{M \mid K}KerPMK is C = Ker P M K C = Ker P M K C=KerP_(M∣K)C=\operatorname{Ker} P_{M \mid K}C=KerPMK. Again p ( K ) = U + p ( K ) = U + p(K)=U_(+)p(K)=U_{+}p(K)=U+but K C p ( K ) = R + 2 K C p ( K ) = R + 2 K!=C o+p(K)=R_(+)^(2)K \neq C \oplus p(K)=R_{+}^{2}KCp(K)=R+2.
c) Let K = { ( x 1 , x 2 ) : x 2 x 1 , x 1 0 } K = x 1 , x 2 : x 2 x 1 , x 1 0 K={(x_(1),x_(2)):x_(2) <= x_(1),x_(1) >= 0}K=\left\{\left(x_{1}, x_{2}\right): x_{2} \leq x_{1}, x_{1} \geq 0\right\}K={(x1,x2):x2x1,x10}. In this case Ker P M K = { ( 0 , 0 ) } Ker P M K = { ( 0 , 0 ) } KerP_(M∣K)={(0,0)}\operatorname{Ker} P_{M \mid K}=\{(0,0)\}KerPMK={(0,0)} implying C = { ( 0 , 0 ) } C = { ( 0 , 0 ) } C={(0,0)}C=\{(0,0)\}C={(0,0)}. We have p ( K ) = U + K M p ( K ) = U + K M p(K)=U_(+)sub K nn Mp(K)=U_{+} \subset K \cap Mp(K)=U+KM but K C p ( K ) = R + 2 K C p ( K ) = R + 2 K!=C o+p(K)=R_(+)^(2)K \neq C \oplus p(K)=R_{+}^{2}KCp(K)=R+2.
d) K = { ( x 1 , x 2 ) : x 1 0 , x 2 0 } K = x 1 , x 2 : x 1 0 , x 2 0 K={(x_(1),x_(2)):x_(1) >= 0,x_(2) >= 0}K=\left\{\left(x_{1}, x_{2}\right): x_{1} \geq 0, x_{2} \geq 0\right\}K={(x1,x2):x10,x20}. In this case Ker P M K = { ( 0 , x 2 ) : x 2 0 } Ker P M K = 0 , x 2 : x 2 0 KerP_(M∣K)={(0,x_(2)):x_(2) >= 0}\operatorname{Ker} P_{M \mid K}=\left\{\left(0, x_{2}\right): x_{2} \geq 0\right\}KerPMK={(0,x2):x20}, C = p 1 ( 0 ) = Ker P M K , p ( K ) = U + C = p 1 ( 0 ) = Ker P M K , p ( K ) = U + C=p^(-1)(0)=KerP_(M∣K),p(K)=U_(+)C=p^{-1}(0)=\operatorname{Ker} P_{M \mid K}, p(K)=U_{+}C=p1(0)=KerPMK,p(K)=U+and K = p 1 ( 0 ) p ( K ) K = p 1 ( 0 ) p ( K ) K=p^(-1)(0)o+p(K)K=p^{-1}(0) \oplus p(K)K=p1(0)p(K).
e) K = { ( x 1 , x 2 ) R 2 : x 2 0 } K = x 1 , x 2 R 2 : x 2 0 K={(x_(1),x_(2))inR^(2):x_(2) >= 0}K=\left\{\left(x_{1}, x_{2}\right) \in R^{2}: x_{2} \geq 0\right\}K={(x1,x2)R2:x20}. In this case p ( K ) = M K , Ker P K M == { ( 0 , x 2 ) : x 2 0 } p ( K ) = M K , Ker P K M == 0 , x 2 : x 2 0 p(K)=M sub K,KerP_(K∣M)=={(0,x_(2)):x_(2) >= 0}p(K)=M \subset K, \operatorname{Ker} P_{K \mid M}= =\left\{\left(0, x_{2}\right): x_{2} \geq 0\right\}p(K)=MK,KerPKM=={(0,x2):x20} and K = C p ( K ) K = C p ( K ) K=C o+p(K)K=C \oplus p(K)K=Cp(K), where C = Ker P M K C = Ker P M K C=KerP_(M∣K)C=\operatorname{Ker} P_{M \mid K}C=KerPMK.
Remarks. In Example 1.a) none of the Condition a a aaa ) and b b bbb ) from Theorem A A A^(')A^{\prime}A is verified.
In Example 1.b) condition b b bbb ) is fulfilled but p ( K ) K p ( K ) K p(K)⊄Kp(K) \not \subset Kp(K)K, while in Example 1.c), p ( K ) K p ( K ) K p(K)sub Kp(K) \subset Kp(K)K but x p ( x ) K x p ( x ) K x-p(x)in Kx-p(x) \in Kxp(x)K only for x = ( 0 , 0 ) x = ( 0 , 0 ) x=(0,0)x=(0,0)x=(0,0).
In Example 1.d) Conditions a a aaa ) and b b bbb ) are both verified but p ( K ) p ( K ) p(K)p(K)p(K) is not a subspace of K K KKK.
In Example 1.e) p ( K ) = M p ( K ) = M p(K)=Mp(K)=Mp(K)=M.
The following example shows that p ( K ) p ( K ) p(K)p(K)p(K) may be a closed subspace of K with p ( K ) M p ( K ) M p(K)!=Mp(K) \neq Mp(K)M.
Example 2. Let X = R 3 X = R 3 X=R^(3)X=R^{3}X=R3 with the Euclidean norm, M = { ( x 1 , x 2 , 0 ) : x 1 , x 2 R } M = x 1 , x 2 , 0 : x 1 , x 2 R M={(x_(1),x_(2),0):x_(1),x_(2)in R}M=\left\{\left(x_{1}, x_{2}, 0\right): x_{1}, x_{2} \in R\right\}M={(x1,x2,0):x1,x2R} and K = { ( 0 , x 2 , x 3 ) : x 2 R , x 3 0 } K = 0 , x 2 , x 3 : x 2 R , x 3 0 K={(0,x_(2),x_(3)):x_(2)in R,x_(3) >= 0}K=\left\{\left(0, x_{2}, x_{3}\right): x_{2} \in R, x_{3} \geq 0\right\}K={(0,x2,x3):x2R,x30}. Then p ( K ) = { ( 0 , x 2 , 0 ) : x 2 R } M p ( K ) = 0 , x 2 , 0 : x 2 R M p(K)={(0,x_(2),0):x_(2)in R}!=Mp(K)=\left\{\left(0, x_{2}, 0\right): x_{2} \in R\right\} \neq Mp(K)={(0,x2,0):x2R}M and Ker P M K = { ( 0 , x 3 , 0 ) : x 3 0 } Ker P M K = 0 , x 3 , 0 : x 3 0 KerP_(M∣K)={(0,x_(3),0):x_(3) >= 0}\operatorname{Ker} P_{M \mid K}=\left\{\left(0, x_{3}, 0\right): x_{3} \geq 0\right\}KerPMK={(0,x3,0):x30}. The equality K = C p ( K ) K = C p ( K ) K=C o+p(K)K=C \oplus p(K)K=Cp(K) holds with C = Ker P M K C = Ker P M K C=KerP_(M∣K)C=\operatorname{Ker} P_{M \mid K}C=KerPMK.
Example 3. Let X = C [ a , b ] X = C [ a , b ] X=C[a,b]X=C[a, b]X=C[a,b] be the Banach space of all continuous realvalued functions on the interval [ a , b ] [ a , b ] [a,b][a, b][a,b] with the sup-norm.
The set
M := { f C [ a , b ] : f ( a ) = f ( b ) = 0 } M := { f C [ a , b ] : f ( a ) = f ( b ) = 0 } M:={f in C[a,b]:f(a)=f(b)=0}M:=\{f \in C[a, b]: f(a)=f(b)=0\}M:={fC[a,b]:f(a)=f(b)=0}
is a closed subspace of C [ a , b ] C [ a , b ] C[a,b]C[a, b]C[a,b],
K := { f C [ a , b ] : f ( a ) = f ( b ) 0 } K := { f C [ a , b ] : f ( a ) = f ( b ) 0 } K:={f in C[a,b]:f(a)=f(b) >= 0}K:=\{f \in C[a, b]: f(a)=f(b) \geq 0\}K:={fC[a,b]:f(a)=f(b)0}
is a closed convex cone in C [ a , b ] C [ a , b ] C[a,b]C[a, b]C[a,b] and M K M K M sub KM \subset KMK.
First show that the subspace M M MMM is K K KKK-proximinal. For f K f K f in Kf \in KfK, the function g g ggg defined by g ( x ) := f ( x ) f ( a ) , x [ a , b ] g ( x ) := f ( x ) f ( a ) , x [ a , b ] g(x):=f(x)-f(a),x in[a,b]g(x):=f(x)-f(a), x \in[a, b]g(x):=f(x)f(a),x[a,b], is an element of best approximation for f f fff in K K KKK. Indeed, we have f g = f ( a ) f g = f ( a ) ||f-g||=f(a)\|f-g\|=f(a)fg=f(a) and f h | f ( a ) h ( a ) | f h | f ( a ) h ( a ) | ||f-h|| >= |f(a)-h(a)|\|f-h\| \geq|f(a)-h(a)|fh|f(a)h(a)|, for all h M h M h in Mh \in MhM. It follows that d ( f , M ) = f ( a ) d ( f , M ) = f ( a ) d(f,M)=f(a)d(f, M)=f(a)d(f,M)=f(a) and g P M K ( f ) g P M K ( f ) g inP_(M∣K)(f)g \in P_{M \mid K}(f)gPMK(f).
The kernel of the restricted metric projection is
Ker P M K = { f K : 0 P M K ( f ) } = = { f K : f ( a ) f ( x ) f ( a ) , for all x [ a , b ] } Ker P M K = f K : 0 P M K ( f ) = = { f K : f ( a ) f ( x ) f ( a ) ,  for all  x [ a , b ] } {:[KerP_(M∣K)={f in K:0inP_(M∣K)(f)}=],[={f in K:-f(a) <= f(x) <= f(a)","" for all "x in[a","b]}]:}\begin{aligned} \operatorname{Ker} P_{M \mid K} & =\left\{f \in K: 0 \in P_{M \mid K}(f)\right\}= \\ & =\{f \in K:-f(a) \leq f(x) \leq f(a), \text { for all } x \in[a, b]\} \end{aligned}KerPMK={fK:0PMK(f)}=={fK:f(a)f(x)f(a), for all x[a,b]}
It follows p ( f ) P M K ( f ) p ( f ) P M K ( f ) p(f)inP_(M∣K)(f)p(f) \in P_{M \mid K}(f)p(f)PMK(f) and the inequalities
p ( f 1 ) p ( f 2 ) f 1 f 2 + | f 1 ( a ) f 2 ( a ) | 2 f 1 f 2 p f 1 p f 2 f 1 f 2 + f 1 ( a ) f 2 ( a ) 2 f 1 f 2 ||p(f_(1))-p(f_(2))|| <= ||f_(1)-f_(2)||+|f_(1)(a)-f_(2)(a)| <= 2*||f_(1)-f_(2)||\left\|p\left(f_{1}\right)-p\left(f_{2}\right)\right\| \leq\left\|f_{1}-f_{2}\right\|+\left|f_{1}(a)-f_{2}(a)\right| \leq 2 \cdot\left\|f_{1}-f_{2}\right\|p(f1)p(f2)f1f2+|f1(a)f2(a)|2f1f2
for f 1 f 2 K f 1 f 2 K f_(1)f_(2)in Kf_{1} f_{2} \in Kf1f2K, imply the continuity of the application p p ppp.
Obviously that p p ppp is positively homogeneous and additive on K K KKK. Since M K M K M sub KM \subset KMK, Theorem B can be applied to obtain the equality K ˙ = p 1 ( 0 ) p ( K ) K ˙ = p 1 ( 0 ) p ( K ) K^(˙)=p^(-1)(0)o+p(K)\dot{K}=p^{-1}(0) \oplus p(K)K˙=p1(0)p(K). In this case p 1 ( 0 ) = { g K : c 0 , g ( x ) = c p 1 ( 0 ) = { g K : c 0 , g ( x ) = c p^(-1)(0)={g in K:EE c >= 0,g(x)=cp^{-1}(0)=\{g \in K: \exists c \geq 0, g(x)=cp1(0)={gK:c0,g(x)=c, for all x [ a , b ] } x [ a , b ] } x in[a,b]}x \in[a, b]\}x[a,b]} and f ( x ) = f ( a ) + ( f ( x ) f ( x ) = f ( a ) + ( f ( x ) f(x)=f(a)+(f(x)f(x)=f(a)+(f(x)f(x)=f(a)+(f(x) - f ( a ) f ( a ) f(a)f(a)f(a) ) is the unique decomposition of f K f K f in Kf \in KfK in the form f = g + h f = g + h f=g+hf=g+hf=g+h with g p 1 ( 0 ) g p 1 ( 0 ) g inp^(-1)(0)g \in p^{-1}(0)gp1(0) and h p ( K ) ( g ( x ) = f ( a ) h p ( K ) ( g ( x ) = f ( a ) h in p(K)quad(g(x)=f(a)h \in p(K) \quad(g(x)=f(a)hp(K)(g(x)=f(a) and h ( x ) = f ( x ) f ( a ) h ( x ) = f ( x ) f ( a ) h(x)=f(x)-f(a)h(x)=f(x)-f(a)h(x)=f(x)f(a) for all x [ a , b ] ) x [ a , b ] ) x in[a,b])x \in[a, b])x[a,b]).
In Examples 1d) and e), the subspace M M MMM is K K KKK-Chebyshevian and K = Ker P M K p ( K ) K = Ker P M K p ( K ) K=KerP_(M∣K)o+p(K)K=\operatorname{Ker} P_{M \mid K} \oplus p(K)K=KerPMKp(K). The following corollary shows that this is a general property of K K KKK-Chebyshevian subspaces.
COROLLARY 1. Let K K KKK be a closed convex cone in the normed space X X XXX and M M MMM a K-Chebyshevian subspace of X X XXX. If there exist two closed convex cones
C Ker P M K C Ker P M K C sub KerP_(M∣K)C \subset \operatorname{Ker} P_{M \mid K}CKerPMK and U M U M U sub MU \subset MUM such that K = C U K = C U K=C o+UK=C \oplus UK=CU, then C = Ker P M K C = Ker P M K C=KerP_(M∣K)C=\operatorname{Ker} P_{\mathrm{M} \mid \mathrm{K}}C=KerPMK and U = p ( K ) U = p ( K ) U=p(K)U=\mathrm{p}(\mathrm{K})U=p(K) where p : K M p : K M p:K rarr Mp: K \rightarrow Mp:KM is the only selection associated to the metric projection P M K P M K P_(M∣K)P_{M \mid K}PMK.
Proof. Since C Ker P M K C Ker P M K C sub KerP_(M∣K)C \subset \operatorname{Ker} P_{M \mid K}CKerPMK it remains to show that Ker P M K C Ker P M K C KerP_(M∣K)sub C\operatorname{Ker} P_{M \mid K} \subset CKerPMKC. Let x Ker P M K x Ker P M K x in KerP_(M∣K)x \in \operatorname{Ker} P_{M \mid K}xKerPMK and let y C , z U y C , z U y in C,z in Uy \in C, z \in UyC,zU be such that x = y + z x = y + z x=y+zx=y+zx=y+z. By Theorem A A A^(')A^{\prime}A the selection p p ppp is given by p ( x ) = z p ( x ) = z p(x)=zp(x)=zp(x)=z and by the additivity of p p ppp.
0 = p ( x ) = p ( y ) + p ( z ) = 0 + z = z 0 = p ( x ) = p ( y ) + p ( z ) = 0 + z = z 0=p(x)=p(y)+p(z)=0+z=z0=p(x)=p(y)+p(z)=0+z=z0=p(x)=p(y)+p(z)=0+z=z
implying x = y C x = y C x=y in Cx=y \in Cx=yC. The equality U = p ( K ) U = p ( K ) U=p(K)U=p(K)U=p(K) follows also from Theorem A A A^(')\mathrm{A}^{\prime}A.
A partial converse of Corollary 1 is also true:
Corollary 2. Let M M MMM be a closed subspace of the normed space X X XXX and K K KKK a closed convex cone in X X XXX. If K = Ker P M K M K = Ker P M K M K=KerP_(M∣K)o+MK=\operatorname{Ker} P_{M \mid K} \oplus MK=KerPMKM then the subspace M M MMM is K K KKK Chebyshevian and the only selection associated to the metric projection is continuous, positively homogeneous and additive on K K KKK.
Proof. First we prove that P M K ( y ) = { 0 } P M K ( y ) = { 0 } P_(M∣K)(y)={0}P_{M \mid K}(y)=\{0\}PMK(y)={0} for every y Ker P M K y Ker P M K y in KerP_(M∣K)y \in \operatorname{Ker} P_{M \mid K}yKerPMK. Indeed, y Ker P M K y Ker P M K y in KerP_(M∣K)y \in \operatorname{Ker} P_{M \mid K}yKerPMK is equivalent to 0 Ker P M K ( y ) 0 Ker P M K ( y ) 0in KerP_(M∣K)(y)0 \in \operatorname{Ker} P_{M \mid K}(y)0KerPMK(y). If z Ker P M K ( y ) z Ker P M K ( y ) z in KerP_(M∣K)(y)z \in \operatorname{Ker} P_{M \mid K}(y)zKerPMK(y) then, taking into account the fact that M M MMM is a subspace of X X XXX and z M z M z in Mz \in MzM, we obtain
y z = inf { y m : m M } = = inf { y z m : m M } = d ( y z , M ) y z = inf { y m : m M } = = inf y z m : m M = d ( y z , M ) {:[||y-z||=i n f{||y-m||:m in M}=],[=i n f{||y-z-m^(')||:m^(')in M}=d(y-z","M)]:}\begin{aligned} \|y-z\| & =\inf \{\|y-m\|: m \in M\}= \\ & =\inf \left\{\left\|y-z-m^{\prime}\right\|: m^{\prime} \in M\right\}=d(y-z, M) \end{aligned}yz=inf{ym:mM}==inf{yzm:mM}=d(yz,M)
showing that 0 P M K ( y z ) 0 P M K ( y z ) 0inP_(M∣K)(y-z)0 \in P_{M \mid K}(y-z)0PMK(yz) or, equivalently, y z Ker P M K y z Ker P M K y-z in KerP_(M∣K)y-z \in \operatorname{Ker} P_{M \mid K}yzKerPMK. But then y y yyy admits two representations y = y + 0 y = y + 0 y=y+0y=y+0y=y+0 and y = ( y z ) + z y = ( y z ) + z y=(y-z)+zy=(y-z)+zy=(yz)+z, with y , y z Ker P M K y , y z Ker P M K y,y-z in KerP_(M∣K)y, y-z \in \operatorname{Ker} P_{M \mid K}y,yzKerPMK and 0 , z M 0 , z M 0,z in M0, z \in M0,zM. The unicity of this representation implies z = 0 z = 0 z=0z=0z=0.
Now, writing an arbitrary element x K x K x in Kx \in KxK in the form x = y + z x = y + z x=y+zx=y+zx=y+z, with y Ker P M K y Ker P M K y in KerP_(M∣K)y \in \operatorname{Ker} P_{M \mid K}yKerPMK and z M z M z in Mz \in MzM, we obtain
P M K ( x ) = P M K ( y + z ) = z + P M K ( y ) = z + 0 = z P M K ( x ) = P M K ( y + z ) = z + P M K ( y ) = z + 0 = z P_(M∣K)(x)=P_(M∣K)(y+z)=z+P_(M∣K)(y)=z+0=zP_{M \mid K}(x)=P_{M \mid K}(y+z)=z+P_{M \mid K}(y)=z+0=zPMK(x)=PMK(y+z)=z+PMK(y)=z+0=z
showing that z z zzz is the only element of best approximation for x x xxx in M M MMM, i.e. the subspace M M MMM is K K KKK-Chebyshevian.
We conclude the paper by an example of a non-Chebyshevian subspace of R 2 R 2 R^(2)\mathrm{R}^{2}R2 for which the decomposition K = C M K = C M K=C o+MK=C \oplus MK=CM is true.
Example 4. Let X = R 2 X = R 2 X=R^(2)X=R^{2}X=R2 with the sup-norm
( x 1 , x 2 ) = max { | x 1 | , | x 2 | } , ( x 1 , x 2 ) R 2 , x 1 , x 2 = max x 1 , x 2 , x 1 , x 2 R 2 , ||(x_(1),x_(2))||=max{|x_(1)|,|x_(2)|},quad(x_(1),x_(2))inR^(2),\left\|\left(x_{1}, x_{2}\right)\right\|=\max \left\{\left|x_{1}\right|,\left|x_{2}\right|\right\}, \quad\left(x_{1}, x_{2}\right) \in R^{2},(x1,x2)=max{|x1|,|x2|},(x1,x2)R2,
and
M := { ( x 1 , 0 ) : x 1 R } K := { ( x 1 , x 2 ) : x 1 R , x 2 0 } M := x 1 , 0 : x 1 R K := x 1 , x 2 : x 1 R , x 2 0 {:[M:={(x_(1),0):x_(1)in R}],[K:={(x_(1),x_(2)):x_(1)in R,x_(2) >= 0}]:}\begin{aligned} & M:=\left\{\left(x_{1}, 0\right): x_{1} \in R\right\} \\ & K:=\left\{\left(x_{1}, x_{2}\right): x_{1} \in R, x_{2} \geq 0\right\} \end{aligned}M:={(x1,0):x1R}K:={(x1,x2):x1R,x20}
It is easily seen that
P M K ( ( x 1 , x 2 ) ) = { ( m , 0 ) : x 1 x 2 m x 1 + x 2 } , for ( x 1 , x 2 ) K . P M K x 1 , x 2 = ( m , 0 ) : x 1 x 2 m x 1 + x 2 ,  for  x 1 , x 2 K . P_(M∣K)((x_(1),x_(2)))={(m,0):x_(1)-x_(2) <= m <= x_(1)+x_(2)}," for "(x_(1),x_(2))in K.P_{M \mid K}\left(\left(x_{1}, x_{2}\right)\right)=\left\{(m, 0): x_{1}-x_{2} \leq m \leq x_{1}+x_{2}\right\}, \text { for }\left(x_{1}, x_{2}\right) \in K .PMK((x1,x2))={(m,0):x1x2mx1+x2}, for (x1,x2)K.
Indeed, x 1 x 2 m x 1 + x 2 x 1 x 2 m x 1 + x 2 x_(1)-x_(2) <= m <= x_(1)+x_(2)x_{1}-x_{2} \leq m \leq x_{1}+x_{2}x1x2mx1+x2 is equivalent to | x 1 m | x 2 x 1 m x 2 |x_(1)-m| <= x_(2)\left|x_{1}-m\right| \leq x_{2}|x1m|x2, implying
( x 1 , x 2 ) ( m , 0 ) = x 1 m , x 2 = x 2 . x 1 , x 2 ( m , 0 ) = x 1 m , x 2 = x 2 . ||(x_(1),x_(2))-(m,0)||=||x_(1)-m,x_(2)||=x_(2).\left\|\left(x_{1}, x_{2}\right)-(m, 0)\right\|=\left\|x_{1}-m, x_{2}\right\|=x_{2} .(x1,x2)(m,0)=x1m,x2=x2.
If ( m , 0 m , 0 m^('),0m^{\prime}, 0m,0 ) is an arbitrary element of M M MMM then
( x 1 , x 2 ) ( m , 0 ) = max { | x 1 m | , x 2 } x 2 , x 1 , x 2 m , 0 = max x 1 m , x 2 x 2 , ||(x_(1),x_(2))-(m^('),0)||=max{|x_(1)-m^(')|,x_(2)} >= x_(2),\left\|\left(x_{1}, x_{2}\right)-\left(m^{\prime}, 0\right)\right\|=\max \left\{\left|x_{1}-m^{\prime}\right|, x_{2}\right\} \geq x_{2},(x1,x2)(m,0)=max{|x1m|,x2}x2,
showing that d ( ( x 1 , x 2 ) , M ) = x 2 d x 1 , x 2 , M = x 2 d((x_(1),x_(2)),M)=x_(2)\mathrm{d}\left(\left(\mathrm{x}_{1}, x_{2}\right), M\right)=x_{2}d((x1,x2),M)=x2 and ( m , 0 ) P M K ( ( x 1 , x 2 ) ) ( m , 0 ) P M K x 1 , x 2 (m,0)inP_(M∣K)((x_(1),x_(2)))(m, 0) \in P_{M \mid K}\left(\left(x_{1}, x_{2}\right)\right)(m,0)PMK((x1,x2)) if and only if m R m R m in Rm \in RmR verifies the inequality | x 1 m | x 2 x 1 m x 2 |x_(1)-m| <= x_(2)\left|x_{1}-m\right| \leq x_{2}|x1m|x2.
The kernel of P M K P M K P_(M∣K)P_{M \mid K}PMK is
Ker P M K = { ( x 1 , x 2 ) R 2 : | x 1 | x 2 , x 2 0 } , Ker P M K = x 1 , x 2 R 2 : x 1 x 2 , x 2 0 , KerP_(M∣K)={(x_(1),x_(2))inR^(2):|x_(1)| <= x_(2),x_(2) >= 0},\operatorname{Ker} P_{M \mid K}=\left\{\left(x_{1}, x_{2}\right) \in R^{2}:\left|x_{1}\right| \leq x_{2}, x_{2} \geq 0\right\},KerPMK={(x1,x2)R2:|x1|x2,x20},
and K = C M K = C M K=C o+MK=C \oplus MK=CM, where C = { ( 0 , x 2 ) : x 2 0 } C = 0 , x 2 : x 2 0 C={(0,x_(2)):x_(2) >= 0}C=\left\{\left(0, x_{2}\right): x_{2} \geq 0\right\}C={(0,x2):x20} is a closed convex cone strictly contained in Ker P M K Ker P M K KerP_(M∣K)\operatorname{Ker} P_{M \mid K}KerPMK.

REFERENCES

  1. E. W. Cheney and D. E. Wulbert, The Existence and Unicity of Best Approximations, Math. Scand. 24(1969), 113-140.
  2. F. Deutsch, Linear Selections for the Metric Projection, J. Funct. Anal. 49(1983), 269-292.
  3. F. Deutsch, Wu Li, Sung-Ho Park. Characterizations of Continuous and Lipschitz Contimous Metric Selections in Normed Linear Spaces, J.A.T. 58(1989), 297-314.
  4. C. Mustăta, On the Selections Associated to the Metric Projections, Revue d'Analyse Numérique et de Theorie l'Approximation 23 (1)(1994), 89-93.
    Received 3 VIII 1994
    Academia Românã
    Institutul de Calcul
    "Tiberiu Popovicin"
    P.O. Box 68
3400 Cluj-Napoca 1
România
1995

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