Generalized I of strongly Lacunary of χ 2 over p metric spaces defined by Musielak Orlicz function
|
|
- Crystal Parks
- 6 years ago
- Views:
Transcription
1 Available at htt://vamuedu/aam Al Al Math ISSN: Vol 11, Issue 2 (December 2016), Alications and Alied Mathematics: An International Journal (AAM) Generalized I of strongly Lacunary of χ 2 over metric saces defined by Musielak Orlicz function 1 Deemala, 2 N Subramanian & 3 Lakshmi N Mishra 1 SQC and OR Unit, Indian Statistical Institute, 203 B T Road Kolkata West Bengal, India dmrai23@gmailcom; deemaladm23@gmailcom 2 Deartment of Mathematics, SASTRA University Thanjavur , India nsmaths@yahoocom 3 Deartment of Mathematics, National Institute of Technology Silchar Assam, India lakshminarayanmishra04@gmailcom Received: February 27, 2016; Acceted: May 31, 2016 Abstract In this aer, we introduce generalized difference sequence saces via ideal convergence, lacunary of χ 2 sequence saces over metric saces defined by Musielak function, and examine the Musielak-Orlicz function which satisfies uniform 2 condition, and we also discuss some toological roerties of the resulting saces of χ 2 with resect to ideal structures which is solid and monotone Hence, given an examle of the sace χ 2, this is not solid and not monotone This theory is very useful for statistical convergence and also is alicable to rough convergence Keywords: Analytic sequence; double sequences; χ 2 sace; difference sequence sace; Musielak Orlicz function; metric sace; Lacunary sequence; ideal 888
2 AAM: Intern J, Vol 11, Issue 2 (December 2016) 889 MSC 2010 No: 40A05, 40C05, 40D05 1 Introduction Throughout w, χ and Λ denote the classes of all gai and analytic scalar valued single sequences, resectively We write w 2 for the set of all comlex sequences (x mn ), where m, n N, the set of ositive integers Then, w 2 is a linear sace under coordinatewise addition and scalar multilication Some initial work on double sequence saces is found in Bromwich (1965) Later on, they were investigated by Hardy (1917), Moricz (1991), Moricz and Rhoades (1988), Basarir and Solankan (1999), Triathy (2003), Turkmenoglu (1999), Mishra et al (2007, 2012) and many others We rocure the following sets of double sequences: M u (t) := (x mn ) w 2 : su m,n N x mn tmn <, C (t) := (x mn ) w 2 : lim m,n x mn l tmn = 1 for some l C, C 0 (t) := (x mn ) w 2 : lim m,n x mn tmn = 1, L u (t) := (x mn ) w 2 : m=1 n=1 x mn tmn <, C b (t) := C (t) M u (t) and C 0b (t) = C 0 (t) M u (t), where t = (t mn ) is the sequence of strictly ositive reals t mn for all m, n N and lim m,n denotes the limit in the Pringsheim s sense In the case t mn = 1 for all m, n N; M u (t), C (t), C 0 (t), L u (t), C b (t) and C 0b (t) reduce to the sets M u, C, C 0, L u, C b and C 0b, resectively Now, we may summarize the knowledge given in some documents related to the double sequence saces Gökhan and Colak (2004, 2005) have roved that M u (t) and C (t), C b (t) are comlete aranormed saces of double sequences and gave the α, β, γ duals of the saces M u (t) and C b (t) Quite recently, in her PhD thesis, Zelter (2001) has essentially studied both the theory of toological double sequence saces and the theory of summability of double sequences Mursaleen and Edely (2003) and Triathy (2003) have indeendently introduced the statistical convergence and Cauchy for double sequences and given the relation between statistical convergent and strongly Cesàro summable double sequences Altay and Basar (2005) have defined the saces BS, BS (t), CS, CS b, CS r and BV of double sequences consisting of all double series whose sequence of artial sums are in the saces M u, M u (t), C, C b, C r and L u, resectively, and also examined some roerties of those sequence saces and determined the α duals of the saces BS, BV, CS b and the β (ϑ) duals of the saces CS b and CS r of double series Basar and Sever (2009) have introduced the Banach sace L q of double sequences corresonding to the well-known sace l q of single sequences and examined some roerties of the sace L q Quite recently Subramanian and Mishra (2010) have studied the sace χ 2 M (, q, u) of double sequences and gave some inclusion relations
3 890 Deemala et al The class of sequences which are strongly Cesàro summable with resect to a modulus was introduced by Maddox (1986) as an extension of the definition of strongly Cesàro summable sequences Connor (1989) further extended this definition to a definition of strong A summability with resect to a modulus where A = (a n,k ) is a nonnegative regular matrix and established some connections between strong A summability, strong A summability with resect to a modulus, and A statistical convergence In Pringsheim (1900) the notion of convergence of double sequences was resented by A Pringsheim Also, in Hamilton (1936, 1938), the four dimensional matrix transformation (Ax) k,l = m=1 Robison (1926) and Hamilton (1939) The double series m,n=1 convergent, where s mn = n=1 amn kl x mn was studied extensively by x mn is called convergent if and only if the double sequence (s mn ) is m,n i,j=1 x ij (m, n N) A sequence x = (x mn ) is said to be double analytic if su mn x mn 1/m+n < The vector sace of all double analytic sequences will be denoted by Λ 2 A sequence x = (x mn ) is called a double gai sequence if ((m + n)! x mn ) 1/m+n 0 as m, n The double gai sequences will be denoted by χ 2 Let φ = all finite sequences Consider a double sequence x = (x ij ) The (m, n) th section x m,n] of the sequence is defined by m,n x m,n] = x ij I ij for all m, n N, where I ij denotes the double sequence whose only non i,j=0 zero term is 1 (i+j)! in the (i, j) th lace for each i, j N An FK-sace (or a metric sace) X is said to have AK roerty if (I mn ) is a Schauder basis for X, or equivalently x m,n] x An FDK-sace is a double sequence sace endowed with a comlete metrizable locally convex toology under which the coordinate maings x = (x k ) (x mn )(m, n N) are also continuous Let M and Φ be mutually comlementary modulus functions Then, we have: (i) For all u, y 0, uy M (u) + Φ (y), (Young s inequality) (see Kamthan et al (1981)), (1) (ii) for all u 0, uη (u) = M (u) + Φ (η (u)), (2) and
4 AAM: Intern J, Vol 11, Issue 2 (December 2016) 891 (iii) for all u 0 and 0 < λ < 1, M (λu) λm (u) (3) Lindenstrauss and Tzafriri (1971) used the idea of Orlicz function to construct the Orlicz sequence sace l M = x w : ( ) k=1 M xk <, for some ρ > 0 ρ The sace l M with the norm x = inf ρ > 0 : ( ) k=1 M xk 1, ρ becomes a Banach sace which is called an Orlicz sequence sace For M (t) = t (1 < ), the saces l M coincide with the classical sequence sace l A sequence f = (f mn ) of modulus function is called a Musielak-modulus function A sequence g = (g mn ) defined by g mn (v) = su v u (f mn ) (u) : u 0, m, n = 1, 2, is called the comlementary function of a Musielak-modulus function f For a given Musielak modulus function f, the Musielak-modulus sequence sace t f and its subsace h f are defined as follows: t f = x w 2 : I f ( x mn ) 1/m+n 0 as m, n, h f = where I f is a convex modular defined by x w 2 : I f ( x mn ) 1/m+n 0 as m, n, I f (x) = m=1 n=1 f mn ( x mn ) 1/m+n, x = (x mn ) t f We consider t f equied with the Luxemburg metric d (x, y) = su mn inf ( m=1 n=1 f mn If X is a sequence sace, we give the following definitions: ( xmn 1/m+n mn )) 1 (1) X = the continuous dual of X, (2) X α = a = (a mn ) : m,n=1 a mn x mn <, for each x X, (3) X β = a = (a mn ) : m,n=1 a mn x mn is convegent, for each x X, (4) X γ = a = (a mn ) : su mn 1 M,N m,n=1 a mnx mn <, for each x X, (5) let X be an FK-sace φ; then X f = f(i mn ) : f X,
5 892 Deemala et al (6) X δ = a = (a mn ) : su mn a mn x mn 1/m+n <, for each x X, where X α, X β and X γ are called α (or Köthe-Toelitz) dual of X, β (or generalized-köthe- Toelitz) dual of X, γ dual of X, and δ dual of X, resectively X α is defined by Guta and Kamtan (1981) It is clear that X α X β and X α X γ, but X β X γ does not hold, since the sequence of artial sums of a double convergent series need not to be bounded The notion of difference sequence saces (for single sequences) was introduced by Kizmaz as follows Z ( ) = x = (x k ) w : ( x k ) Z, for Z = c, c 0 and l, where x k = x k x k+1, for all k N Here, c, c 0 and l denote the classes of convergent, null,and bounded scalar valued single sequences, resectively The difference sequence sace bv of the classical sace l is introduced and studied in the case 1 by Başar and Altay and in the case 0 < < 1 by Altay and Başar (2005) The saces c ( ), c 0 ( ), l ( ) and bv are Banach saces normed by x = x 1 + su k 1 x k and x bv = ( k=1 x k ) 1/, (1 < ) Later on the notion was further investigated by many others We now introduce the following difference double sequence saces defined by where Z = Λ 2, χ 2, and Z ( ) = x = (x mn ) w 2 : ( x mn ) Z, x mn = (x mn x mn+1 ) (x m+1n x m+1n+1 ) = x mn x mn+1 x m+1n + x m+1n+1, m, n N The generalized difference double notion has the following reresentation: m x mn = m 1 x mn m 1 x mn+1 m 1 x m+1n + m 1 x m+1n+1, and also this generalized difference double notion has the following binomial reresentation: m m ( )( ) m m m x mn = ( 1) i+j x m+i,n+j i j i=0 j=0 2 Definition and Preliminaries Let n N and X be a real vector sace of dimension w, where n w A real valued function d (x 1,, x n ) = (d 1 (x 1 ),, d n (x n )) on X satisfes the following four conditions: (1) (d 1 (x 1 ),, d n (x n )) = 0 if and and only if d 1 (x 1 ),, d n (x n ) are linearly deendent,
6 AAM: Intern J, Vol 11, Issue 2 (December 2016) 893 (2) (d 1 (x 1 ),, d n (x n )) is invariant under ermutation, (3) (αd 1 (x 1 ),, d n (x n )) = α (d 1 (x 1 ),, d n (x n )), α R, (4) d ((x 1, y 1 ), (x 2, y 2 ) (x n, y n )) = (d X (x 1, x 2, x n ) + d Y (y 1, y 2, y n ) ) 1/, for 1 <, or (5) d ((x 1, y 1 ), (x 2, y 2 ), (x n, y n )) := su d X (x 1, x 2, x n ), d Y (y 1, y 2, y n ), for x 1, x 2, x n X, y 1, y 2, y n Y is called the roduct metric A trivial examle of the roduct metric of n metric sace is the norm sace X = R equied with the following Euclidean metric in the roduct sace with the norm: (d 1 (x 1 ),, d n (x n )) E = su ( det(d mn (x mn )) ) d 11 (x 11 ) d 12 (x 12 ) d 1n (x 1n ) d 21 (x 21 ) d 22 (x 22 ) d 2n (x 1n ) = su, d n1 (x n1 ) d n2 (x n2 ) d nn (x nn ) where x i = (x i1, x in ) R n for each i = 1, 2, n If every Cauchy sequence in X converges to some L X, then X is said to be comlete with resect to the metric Any comlete metric sace is said to be a Banach metric sace Let X be a linear metric sace A function w : X R is called aranorm, if (1) w (x) 0, for all x X, (2) w ( x) = w (x), for all x X, (3) w (x + y) w (x) + w (y), for all x, y X, and (4) If (σ mn ) is a sequence of scalars with σ mn σ as m, n and (x mn ) is a sequence of vectors with w (x mn x) 0 as m, n, then w (σ mn x mn σx) 0 as m, n A aranorm w for which w (x) = 0 imlies x = 0 is called total aranorm and the air (X, w) is called a total aranormed sace It is well known that the metric of any linear metric sace is given by some total aranorm (see Wilansky (1984), Theorem 1042, 183) Let X be a non-emty set Then a family of sets I 2 X (the class of all subsets of X) is called an ideal if and only if for each A, B I, we have A B I and for each A I and each B A, we have B I A non-emty family of sets F 2 X is a filter on X if and only if φ / F, for each A, B F, we have A B F and each A F and A B, we have B F An ideal I is called non-trivial ideal if I φ and X I Clearly I 2 X is a non-trivial ideal if and only F = F (I) = X/A : A I is a filter on X A non-trivial ideal I 2 X is called admissible if and only if x x X I A sequence (x mn ) m,n N in X is said to be I convergent to 0 X, if for each ɛ > 0 the set A (ɛ) = m, n N : (d 1 (x 1 ),, d n (x n )) 0 ɛ belongs to I Further details on ideals of 2 X can be found in Kostyrko et al (2001) The notion was further investigated by Salat et al (2004) and others
7 894 Deemala et al By the convergence of a double sequence we mean the convergence on the Pringsheim sense, that is, a double sequence x = (x mn ) has Prinsheim limit L (denoted by P lim x = L) rovided that given ɛ > 0 there exists n N such that x mn L < ɛ whenever m, n > n We shall write this more briefly as P convergent The double sequence = (m r, n s ) is called a double lacunary sequence if there exist two increasing integers such that m 0 = 0, ϕ r = m r m r 1 as r and n 0 = 0, ϕ s = n s n s 1 as s Notations: m rs = m r n s, h rs = ϕ r ϕ s, are determined by I rs = (m, n) : m r 1 < m m r and n s 1 < n n s, q r = kr k r 1, q s = ns n s 1 and q rs = q r q s The notion of λ double gai and double analytic sequences are as follows Let λ = (λ mn ) m,n=0 be a strictly increasing sequence of ositive real numbers tending to infinity, that is, 0 < λ 00 < λ 11 < and λ mn as m, n and said that a sequence x = (x mn ) w 2 is λ convergent to 0 is called λ limit of x, if where µ m n (x) = 1 ϕ rs m I rs µ m n (x) 0 as m, n, n I rs ( m 1 λ m,n m 1 λ m,n+1 m 1 λ m+1,n + m 1 λ m+1,n+1 ) x mn 1/m+n The sequence x = (x mn ) w 2 is λ double analytic if su uv µ mn (x) < If lim mn x mn = 0 in the ordinary sense of convergence, then This imlies that 1 lim mn ϕ rs m I rs lim µ mn (x) 0 mn n I rs ( m 1 λ m,n m 1 λ m,n+1 m 1 λ m+1,n 1 = lim mn ϕ rs = 0, + m 1 λ m+1,n+1 ) ((m + n)! x mn 0 ) 1/m+n = 0 m I rs n I rs ( m 1 λ m,n m 1 λ m,n+1 m 1 λ m+1,n + m 1 λ m+1,n+1 ) ((m + n)! x mn 0 ) 1/m+n
8 AAM: Intern J, Vol 11, Issue 2 (December 2016) 895 which yields that lim uv µ mn (x) = 0 and hence x = (x mn ) w 2 is λ convergent to 0 Let I 2 be an admissible ideal of 2 N N, be a double lacunary sequence, ) f = (f mn ) be a Musielak-modulus function, and (X, (d (x 1 ), d (x 2 ),, d (x n 1 )) be a metric sace, q = (q mn ) be double analytic sequence of strictly ositive real numbers By w 2 () X) we denote the sace of all sequences defined over (X, (d (x 1 ), d (x 2 ),, d (x n 1 )) The following inequality will be used throughout the aer If 0 q mn su q mn = H, K = max ( 1, 2 H 1), then for all m, n and a mn, b mn C ( Also a qmn max 1, a H) for all a C a mn + b mn qmn K a mn qmn + b mn qmn, (4) In the resent aer we define the following sequence saces: and fµ, (d (x 1), d (x 2 ),, d (x n 1 )) ϕ 2 θ rs = r, s I rs : f mn ( µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ɛ I 2, fµ, (d (x 1), d (x 2 ),, d (x n 1 )) ϕ 2 θ rs = r, s I rs : f mn ( µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) K I 2 If we take f mn (x) = x, we get and fµ, (d (x 1), d (x 2 ),, d (x n 1 )) ϕ 2 θ rs = r, s I rs : ( µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ɛ I 2, fµ, (d (x 1), d (x 2 ),, d (x n 1 )) ϕ 2 θ rs = r, s I rs : ( µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) K I 2 If we take q = (q mn ) = 1, we get χ 2 fµ, (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ θ rs )] = r, s I rs : f mn ( µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ɛ I 2,
9 896 Deemala et al and Λ 2 fµ, (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ 2 θ rs )] = r, s I rs : f mn ( µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) K I 2 In the resent aer we lan to study some toological roerties and inclusion relations between the above defined sequence saces, 2 2 fµ, (d (x 1), d (x 2 ),, d (x n 1 )) ϕ and fµ, (d (x 1), d (x 2 ),, d (x n 1 )) ϕ, which we shall discuss in this aer 3 Main Results Theorem 1 Let f = (f mn ) be a Musielak-Orlicz function and q = (q mn ) be a double analytic sequence of strictly ositive real numbers The sequence saces fµ, (d (x 1), d (x 2 ),, d (x n 1 )) ϕ 2 and 2 fµ, (d (x 1), d (x 2 ),, d (x n 1 )) ϕ are linear saces It is routine verification Therefore the roof is omitted Theorem 2 Let f = (f mn ) be a Musielak-Orlicz function and q = (q mn ) be a double analytic sequence of strictly ositive real numbers The sequence sace fµ, (d (x 1), d (x 2 ),, d (x n 1 )) ϕ 2 is a aranormed sace with resect to the aranorm defined by g (x) = inf f mn ( µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) 1 Clearly g (x) 0 for x = (x mn ) Since f mn (0) = 0, we get g (0) = 0 Conversely, suose that g (x) = 0 fµ, (d (x 1), d (x 2 ),, d (x n 1 )) ϕ 2
10 AAM: Intern J, Vol 11, Issue 2 (December 2016) 897 Then, inf f mn ( µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) 1 Suose that µ mn (x) 0 for each m, n N Then, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ It follows that (f mn ( µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ) 1/H, which is a contradiction Therefore, µ mn (x) = 0 ( ) 1/H Let (f mn µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) 1 and (f mn ( µ mn (y), (d (x 1 ), d (x 2 ),, d (x n 1 )) ) 1/H 1 Then, by using Minkowski s inequality, we have (f mn ( µ mn (x + y), (d (x 1 ), d (x 2 ),, d (x n 1 )) ) 1/H ) 1/H (f mn ( µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ( ) 1/H + (f mn µ mn (y), (d (x 1 ), d (x 2 ),, d (x n 1 )) So we have g(x + y) = inf f mn ( µ mn (x + y), (d (x 1 ), d (x 2 ),, d (x n 1 )) 1 inf f mn ( µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) 1 + inf f mn ( µ mn (y), (d (x 1 ), d (x 2 ),, d (x n 1 )) 1 Therefore, g (x + y) g (x) + g (y) Finally, to rove that the scalar multilication is continuous, let λ be any comlex number By definition, g (λx) = inf f mn ( µ mn (λx), (d (x 1 ), d (x 2 ),, d (x n 1 )) 1 Then, g (λx) = inf (( λ t) qmn/h : f mn ( µ mn (λx), (d (x 1 ), d (x 2 ),, d (x n 1 )) 1, where t = 1 Since max (1, λ su λ λ qmn mn ), we have )] g (λx) max (1, λ su qmn mn ) inf t qmn/h : f mn ( µ mn (λx), (d (x 1 ), d (x 2 ),, d (x n 1 )) 1
11 898 Deemala et al This comletes the roof Theorem 3 (i) If the sequence (f mn ) satisfies uniform 2 condition, then α = µ g, µ uv (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ (ii) If the sequence (g mn ) satisfies uniform 2 condition, then µ g, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ 2α = fµ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ Let the sequence (f mn ) satisfy uniform 2 condition We get µ g, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ 2 fµ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ To rove the inclusion α let a (x mn ) µ g fµ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ fµ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ m=1 n=1, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ 2α θrs 2 Then for all x mn with we have 2 2 2α 2, x mn a mn < (5) Since the sequence (f mn ) satisfies uniform 2 condition, then (y mn ) and we get ϕ m=1 n=1 rsy mna mn m λ mn(m+n)! < by (5) Thus (ϕ rs a mn ) fµ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ = µ g, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ, 2 2
12 AAM: Intern J, Vol 11, Issue 2 (December 2016) 899 and hence (a mn ) µ g, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ 2 This gives that α From above we have, µ g α (ii) Similarly, one can rove that µ g = µ g, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ 2α if the sequence (g mn ) satisfies uniform 2 condition Proosition 1 If 0 < q mn < mn < for each m and m, then, fµ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ Λ 2 fµ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ The roof is standard, so we omit it Proosition 2 (i) If 0 < inf q mn q mn < 1, then, Λ 2 fµ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ
13 900 Deemala et al (ii) If 1 q mn su q mn <, then, Λ 2 fµ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ 2 fµ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ The roof is standard, so we omit it Proosition 3 Let f = ( ) f mn and f = ( f mn) are sequences of Musielak functions, we have, µ f µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ 2 f µ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ The roof is easy so we omit it Proosition 4 2 f +f µ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ 2 For any sequence of Musielak functions f = (f mn ) and q = (q mn ) be double analytic sequence of strictly ositive real numbers Then, fµ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ The roof is easy so we omit it Proosition 5 The sequence sace Let x = (x mn ), ie is solid su mn fµ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ 2 < 2 2
14 AAM: Intern J, Vol 11, Issue 2 (December 2016) 901 Let (α mn ) be double sequence of scalars such that α mn 1 for all m, n N N Then we get su fµ, µ mn (αx), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ 2 mn su mn This comletes the roof Proosition 6 The sequence sace The roof follows from Proosition 5 Proosition 7 fµ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ If f = (f mn ) be any Musielak function Then, 2 is monotone 2 fµ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ fµ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ if and only if, su r,s 1 ϕ rs ϕ rs Let x we get < 2 fµ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ fµ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ rs Thus x = N 2 and N = su r,s 1 ϕ rs ϕ rs fµ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ rs 2, < Then 2 = 0 2 fµ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ Conversely, suose that fµ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ N θ u fµ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ and x 2 fµ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ Then θ ] rs I 2 fµ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ < ɛ, for every ɛ > 0 2
15 902 Deemala et al Suose that su r,s 1 ϕ rs ϕ rs lim j,k ϕ jk ϕ jk = Hence, we have =, then there exists a sequence of members (rs jk ) such that 2 fµ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ rs = Therefore x / 2 fµ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ, which is a contradiction This comletes the roof Proosition 8 If f = (f mn ) be any Musielak function Then 2 fµ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ = 2 fµ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ, if and only if su r,s 1 ϕ rs ϕ rs It is easy to rove so we omit Proosition 9 The sequence sace <, su r,s 1 ϕ rs ϕ rs > The result follows from the following examle Examle Consider x = (x mn ) = Let is not solid 1 m+n 1 m+n 1 m+n 1 m+n 1 m+n 1 m+n α mn =, for all m, n N 1 m+n 1 m+n 1 m+n
16 AAM: Intern J, Vol 11, Issue 2 (December 2016) 903 Then α mn x mn / fµ, µ mn (x), (d (x 1 ), d (x 2 ),, d (x n 1 )) ϕ is not solid Proosition 10 The sequence sace The roof follows from Proosition 9 2 Hence is not monotone 4 Conclusion We introduce generalized difference sequence saces via ideal convergence, lacunary of χ 2 sequence saces over metric saces defined by Musielak-Orlicz function and also discuss some toological roerties of our roved results on these saces The growing interest in this field is strongly stimulated by the treatment of recent roblems in elasticity, fluid dynamics, calculus of variations, and differential equations One can extend our results for more general saces Acknowledgments The authors are extremely grateful to the anonymous learned referee(s) for their keen reading, valuable suggestion and constructive comments for the imrovement of the manuscrit The authors are thankful to the editor(s) and reviewers of Alications and Alied Mathematics, and also the second author wishes to thank the Deartment of Science and Technology, Government of India for the financial sanction towards this work under FIST rogram SR/FST/MSI-107/2015 REFERENCES Altay, B and BaŞar, F (2005) Some new saces of double sequences, J Math Anal Al, Vol 309, No 1, BaŞar, F and Sever, Y (2009) The sace L of double sequences, Math J Okayama Univ, Vol 51, Basarir, M and Solancan, O (1999) On some double sequence saces, J Indian Acad Math, Vol 21, No 2, Bromwich, TJI A (1965) An introduction to the theory of infinite series, Macmillan and Co Ltd, New York Hardy, GH (1917) On the convergence of certain multile series, Proc Camb Phil Soc, Vol 19, Chandra, P and Triathy, BC (2002) On generalized Kothe-Toelitz duals of some sequence saces, Indian Journal of Pure and Alied Mathematics, Vol 33, No 8, Cannor, J (1989) On strong matrix summability with resect to a modulus and statistical
17 904 Deemala et al convergence, Canad Math Bull, Vol 32, No 2, Goes, G and Goes, S (1970) Sequences of bounded variation and sequences of Fourier coefficients, Math Z, Vol 118, Guta, M and Pradhan, S (2008) On Certain Tye of Modular Sequence sace, Turk J Math, Vol 32, Gökhan, A and Çolak, R (2004) The double sequence saces c P 2 () and c P 2 B (), Al Math Comut, Vol 157, No 2, Gökhan, A and Çolak, R (2005) Double sequence saces l 2, ibid, Vol 160, No 1, Hamilton, HJ (1936) Transformations of multile sequences, Duke Math J, Vol 2, Hamilton, HJ (1938) A Generalization of multile sequences transformation, Duke Math J, Vol 4, Hamilton, HJ (1939) Preservation of artial Limits in Multile sequence transformations, Duke Math J, Vol 4, Kamthan, PK and Guta, M (1981) Sequence saces and series, Lecture notes, Pure and Alied Mathematics, Vol 65 Marcel Dekker, Inc, New York Krasnoselskii, MA and Rutickii, YB (1961) Convex functions and Orlicz saces, Gorningen, Netherlands Lindenstrauss, J and Tzafriri, L (1971) On Orlicz sequence saces, Israel J Math, Vol 10, Maddox, IJ (1986) Sequence saces defined by a modulus, Math Proc Cambridge Philos Soc, Vol 100, No 1, Moricz, F, (1991) Extentions of the saces c and c 0 from single to double sequences, Acta Math Hung, Vol 57, No 1-2, Moricz, F and Rhoades, B E (1988) Almost convergence of double sequences and strong regularity of summability matrices, Math Proc Camb Phil Soc, Vol 104, Mursaleen, M and Edely, OHH (2003) Statistical convergence of double sequences, J Math Anal Al, Vol 288, No 1, Mursaleen, M, (2004) Almost strongly regular matrices and a core theorem for double sequences, J Math Anal Al, Vol 293, No 2, Mursaleen, M and Edely, OHH (2004) Almost convergence and a core theorem for double sequences, J Math Anal Al, Vol 293, No 2, Mishra, VN (2007) Some roblems on aroximations of functions in Banach saces, PhD thesis, Indian Institute of Technology, Roorkee , Uttarakhand, India Mishra, VN and Mishra, L N (2012) Trigonometric Aroximation of signals (Functions) in L ( 1) norm, International Journal of Contemorary Mathematical Sciences, Vol 7, No 19, Nakano, H (1953) Concave modulars, J Math Soc Jaan, Vol 5, Pringsheim, A (1900) Zurtheorie derzweifach unendlichen zahlenfolgen, Math Ann, Vol 53, Robison, GM (1926) Divergent double sequences and series, Amer Math Soc Trans, Vol 28, Ruckle, WH (1973) FK saces in which the sequence of coordinate vectors is bounded, Canad
18 AAM: Intern J, Vol 11, Issue 2 (December 2016) 905 J Math, Vol 25, Subramanian, N and Misra, UK (2010) The semi normed sace defined by a double gai sequence of modulus function, Fasciculi Math, Vol 46 Triathy, BC (2003) On statistically convergent double sequences, Tamkang J Math, 34, No 3, Triathy, BC and Mahanta, S (2004) On a class of vector valued sequences associated with multilier sequences, Acta Math Alicata Sinica (Eng Ser), Vol 20, No 3, Turkmenoglu, A (1999) Matrix transformation between some classes of double sequences, J Inst Math Com Sci Math Ser, Vol 12, No 1, Triathy, BC and Sen, M (2006) Characterization of some matrix classes involving aranormed sequence saces, Tamkang Journal of Mathematics, Vol 37, No 2, Triathy, BC and Dutta, AJ (2007) On fuzzy real-valued double sequence saces 2 l F, Mathematical and Comuter Modelling, Vol 46, No 9-10, Triathy, BC and Sarma, B (2008) Statistically convergent difference double sequence saces, Acta Mathematica Sinica, Vol 24, No 5, Triathy, BC and Sarma, B (2009) Vector valued double sequence saces defined by Orlicz function, Mathematica Slovaca, Vol 59, No 6, Triathy, BC and Dutta, AJ (2010) Bounded variation double sequence sace of fuzzy real numbers, Comuters and Mathematics with Alications, Vol 59, No 2, Triathy, BC and Sarma, B (2011) Double sequence saces of fuzzy numbers defined by Orlicz function, Acta Mathematica Scientia, Vol 31, No B(1), Triathy, BC and Chandra, P (2011) On some generalized difference aranormed sequence saces associated with multilier sequences defined by modulus function, Anal Theory Al, Vol 27, No 1, Triathy, BC and Dutta, AJ (2013) Lacunary bounded variation sequence of fuzzy real numbers, Journal of Intelligent and Fuzzy Systems, Vol 24, No 1, Wilansky, A (1984) Summability through Functional Analysis, North-Holland Mathematical Studies, North-Holland Publishing, Amsterdam, Vol 85 Woo, JYT (1973) On Modular Sequence saces, Studia Math, Vol 48, Zeltser, M (2001) Investigation of Double Sequence Saces by Soft and Hard Analitical Methods, Dissertationes Mathematicae Universitatis Tartuensis 25, Tartu University Press, Univ of Tartu, Faculty of Mathematics and Comuter Science, Tartu
Received May 27, 2009; accepted January 14, 2011
MATHEMATICAL COMMUNICATIONS 53 Math. Coun. 6(20), 53 538. I σ -Convergence Fatih Nuray,, Hafize Gök and Uǧur Ulusu Departent of Matheatics, Afyon Kocatepe University, 03200 Afyonkarahisar, Turkey Received
More informationOn the smallest abundant number not divisible by the first k primes
On the smallest abundant number not divisible by the first k rimes Douglas E. Iannucci Abstract We say a ositive integer n is abundant if σ(n) > 2n, where σ(n) denotes the sum of the ositive divisors of
More informationSUBORDINATION BY ORTHOGONAL MARTINGALES IN L p, 1 < p Introduction: Orthogonal martingales and the Beurling-Ahlfors transform
SUBORDINATION BY ORTHOGONAL MARTINGALES IN L, 1 < PRABHU JANAKIRAMAN AND ALEXANDER VOLBERG 1. Introduction: Orthogonal martingales and the Beurling-Ahlfors transform We are given two martingales on the
More informationSupplemental Material: Buyer-Optimal Learning and Monopoly Pricing
Sulemental Material: Buyer-Otimal Learning and Monooly Pricing Anne-Katrin Roesler and Balázs Szentes February 3, 207 The goal of this note is to characterize buyer-otimal outcomes with minimal learning
More informationBrownian Motion, the Gaussian Lévy Process
Brownian Motion, the Gaussian Lévy Process Deconstructing Brownian Motion: My construction of Brownian motion is based on an idea of Lévy s; and in order to exlain Lévy s idea, I will begin with the following
More informationNon replication of options
Non replication of options Christos Kountzakis, Ioannis A Polyrakis and Foivos Xanthos June 30, 2008 Abstract In this paper we study the scarcity of replication of options in the two period model of financial
More informationA NOTE ON SKEW-NORMAL DISTRIBUTION APPROXIMATION TO THE NEGATIVE BINOMAL DISTRIBUTION
A NOTE ON SKEW-NORMAL DISTRIBUTION APPROXIMATION TO THE NEGATIVE BINOMAL DISTRIBUTION JYH-JIUAN LIN 1, CHING-HUI CHANG * AND ROSEMARY JOU 1 Deartment of Statistics Tamkang University 151 Ying-Chuan Road,
More informationStatistics and Probability Letters. Variance stabilizing transformations of Poisson, binomial and negative binomial distributions
Statistics and Probability Letters 79 (9) 6 69 Contents lists available at ScienceDirect Statistics and Probability Letters journal homeage: www.elsevier.com/locate/staro Variance stabilizing transformations
More informationON JARQUE-BERA TESTS FOR ASSESSING MULTIVARIATE NORMALITY
Journal of Statistics: Advances in Theory and Alications Volume, umber, 009, Pages 07-0 O JARQUE-BERA TESTS FOR ASSESSIG MULTIVARIATE ORMALITY KAZUYUKI KOIZUMI, AOYA OKAMOTO and TAKASHI SEO Deartment of
More informationWorst-case evaluation complexity for unconstrained nonlinear optimization using high-order regularized models
Worst-case evaluation comlexity for unconstrained nonlinear otimization using high-order regularized models E. G. Birgin, J. L. Gardenghi, J. M. Martínez, S. A. Santos and Ph. L. Toint 2 Aril 26 Abstract
More informationInformation and uncertainty in a queueing system
Information and uncertainty in a queueing system Refael Hassin December 7, 7 Abstract This aer deals with the effect of information and uncertainty on rofits in an unobservable single server queueing system.
More informationSINGLE SAMPLING PLAN FOR VARIABLES UNDER MEASUREMENT ERROR FOR NON-NORMAL DISTRIBUTION
ISSN -58 (Paer) ISSN 5-5 (Online) Vol., No.9, SINGLE SAMPLING PLAN FOR VARIABLES UNDER MEASUREMENT ERROR FOR NON-NORMAL DISTRIBUTION Dr. ketki kulkarni Jayee University of Engineering and Technology Guna
More informationGame Theory. Lecture Notes By Y. Narahari. Department of Computer Science and Automation Indian Institute of Science Bangalore, India October 2012
Game Theory Lecture Notes By Y. Narahari Department of Computer Science and Automation Indian Institute of Science Bangalore, India October 22 COOPERATIVE GAME THEORY Correlated Strategies and Correlated
More informationA No-Arbitrage Theorem for Uncertain Stock Model
Fuzzy Optim Decis Making manuscript No (will be inserted by the editor) A No-Arbitrage Theorem for Uncertain Stock Model Kai Yao Received: date / Accepted: date Abstract Stock model is used to describe
More informationTheorem 1.3. Every finite lattice has a congruence-preserving embedding to a finite atomistic lattice.
CONGRUENCE-PRESERVING EXTENSIONS OF FINITE LATTICES TO SEMIMODULAR LATTICES G. GRÄTZER AND E.T. SCHMIDT Abstract. We prove that every finite lattice hasa congruence-preserving extension to a finite semimodular
More informationPart 3: Trust-region methods for unconstrained optimization. Nick Gould (RAL)
Part 3: Trust-region methods for unconstrained optimization Nick Gould (RAL) minimize x IR n f(x) MSc course on nonlinear optimization UNCONSTRAINED MINIMIZATION minimize x IR n f(x) where the objective
More informationOn the h-vector of a Lattice Path Matroid
On the h-vector of a Lattice Path Matroid Jay Schweig Department of Mathematics University of Kansas Lawrence, KS 66044 jschweig@math.ku.edu Submitted: Sep 16, 2009; Accepted: Dec 18, 2009; Published:
More informationON THE MEAN VALUE OF THE SCBF FUNCTION
ON THE MEAN VALUE OF THE SCBF FUNCTION Zhang Xiaobeng Deartment of Mathematics, Northwest University Xi an, Shaani, P.R.China Abstract Keywords: The main urose of this aer is using the elementary method
More informationForecast Horizons for Production Planning with Stochastic Demand
Forecast Horizons for Production Planning with Stochastic Demand Alfredo Garcia and Robert L. Smith Department of Industrial and Operations Engineering Universityof Michigan, Ann Arbor MI 48109 December
More informationInterpolation of κ-compactness and PCF
Comment.Math.Univ.Carolin. 50,2(2009) 315 320 315 Interpolation of κ-compactness and PCF István Juhász, Zoltán Szentmiklóssy Abstract. We call a topological space κ-compact if every subset of size κ has
More information4: SINGLE-PERIOD MARKET MODELS
4: SINGLE-PERIOD MARKET MODELS Marek Rutkowski School of Mathematics and Statistics University of Sydney Semester 2, 2016 M. Rutkowski (USydney) Slides 4: Single-Period Market Models 1 / 87 General Single-Period
More informationSome Bounds for the Singular Values of Matrices
Applied Mathematical Sciences, Vol., 007, no. 49, 443-449 Some Bounds for the Singular Values of Matrices Ramazan Turkmen and Haci Civciv Department of Mathematics, Faculty of Art and Science Selcuk University,
More informationThe ruin probabilities of a multidimensional perturbed risk model
MATHEMATICAL COMMUNICATIONS 231 Math. Commun. 18(2013, 231 239 The ruin probabilities of a multidimensional perturbed risk model Tatjana Slijepčević-Manger 1, 1 Faculty of Civil Engineering, University
More informationNumerical Solution of BSM Equation Using Some Payoff Functions
Mathematics Today Vol.33 (June & December 017) 44-51 ISSN 0976-38, E-ISSN 455-9601 Numerical Solution of BSM Equation Using Some Payoff Functions Dhruti B. Joshi 1, Prof.(Dr.) A. K. Desai 1 Lecturer in
More informationAn Optimal Odd Unimodular Lattice in Dimension 72
An Optimal Odd Unimodular Lattice in Dimension 72 Masaaki Harada and Tsuyoshi Miezaki September 27, 2011 Abstract It is shown that if there is an extremal even unimodular lattice in dimension 72, then
More informationarxiv: v2 [math.lo] 13 Feb 2014
A LOWER BOUND FOR GENERALIZED DOMINATING NUMBERS arxiv:1401.7948v2 [math.lo] 13 Feb 2014 DAN HATHAWAY Abstract. We show that when κ and λ are infinite cardinals satisfying λ κ = λ, the cofinality of the
More informationarxiv: v1 [q-fin.pm] 13 Mar 2014
MERTON PORTFOLIO PROBLEM WITH ONE INDIVISIBLE ASSET JAKUB TRYBU LA arxiv:143.3223v1 [q-fin.pm] 13 Mar 214 Abstract. In this paper we consider a modification of the classical Merton portfolio optimization
More informationLecture 4. Finite difference and finite element methods
Finite difference and finite element methods Lecture 4 Outline Black-Scholes equation From expectation to PDE Goal: compute the value of European option with payoff g which is the conditional expectation
More informationConditional Certainty Equivalent
Conditional Certainty Equivalent Marco Frittelli and Marco Maggis University of Milan Bachelier Finance Society World Congress, Hilton Hotel, Toronto, June 25, 2010 Marco Maggis (University of Milan) CCE
More informationConditional Square Functions and Dyadic Perturbations of the Sine-Cosine decomposition for Hardy Martingales
Conditional Square Functions and Dyadic Perturbations of the Sine-Cosine decomposition for Hardy Martingales arxiv:1611.02653v1 [math.fa] 8 Nov 2016 Paul F. X. Müller October 16, 2016 Abstract We prove
More informationIn Discrete Time a Local Martingale is a Martingale under an Equivalent Probability Measure
In Discrete Time a Local Martingale is a Martingale under an Equivalent Probability Measure Yuri Kabanov 1,2 1 Laboratoire de Mathématiques, Université de Franche-Comté, 16 Route de Gray, 253 Besançon,
More informationSolutions of Bimatrix Coalitional Games
Applied Mathematical Sciences, Vol. 8, 2014, no. 169, 8435-8441 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ams.2014.410880 Solutions of Bimatrix Coalitional Games Xeniya Grigorieva St.Petersburg
More informationApplication of an Interval Backward Finite Difference Method for Solving the One-Dimensional Heat Conduction Problem
Application of an Interval Backward Finite Difference Method for Solving the One-Dimensional Heat Conduction Problem Malgorzata A. Jankowska 1, Andrzej Marciniak 2 and Tomasz Hoffmann 2 1 Poznan University
More informationCHOICE THEORY, UTILITY FUNCTIONS AND RISK AVERSION
CHOICE THEORY, UTILITY FUNCTIONS AND RISK AVERSION Szabolcs Sebestyén szabolcs.sebestyen@iscte.pt Master in Finance INVESTMENTS Sebestyén (ISCTE-IUL) Choice Theory Investments 1 / 65 Outline 1 An Introduction
More informationA Convenient Way of Generating Normal Random Variables Using Generalized Exponential Distribution
A Convenient Way of Generating Normal Random Variables Using Generalized Exponential Distribution Debasis Kundu 1, Rameshwar D. Gupta 2 & Anubhav Manglick 1 Abstract In this paper we propose a very convenient
More informationMartingales. by D. Cox December 2, 2009
Martingales by D. Cox December 2, 2009 1 Stochastic Processes. Definition 1.1 Let T be an arbitrary index set. A stochastic process indexed by T is a family of random variables (X t : t T) defined on a
More informationSEQUENTIAL DECISION PROBLEM WITH PARTIAL MAINTENANCE ON A PARTIALLY OBSERVABLE MARKOV PROCESS. Toru Nakai. Received February 22, 2010
Scientiae Mathematicae Japonicae Online, e-21, 283 292 283 SEQUENTIAL DECISION PROBLEM WITH PARTIAL MAINTENANCE ON A PARTIALLY OBSERVABLE MARKOV PROCESS Toru Nakai Received February 22, 21 Abstract. In
More informationBETA DISTRIBUTION ON ARITHMETICAL SEMIGROUPS
Annales Univ Sci Budapest Sect Comp 47 (2018) 147 154 BETA DISTRIBUTION ON ARITHMETICAL SEMIGROUPS Gintautas Bareikis and Algirdas Mačiulis (Vilnius Lithuania) Communicated by Imre Kátai (Received February
More informationOutline. 1 Introduction. 2 Algorithms. 3 Examples. Algorithm 1 General coordinate minimization framework. 1: Choose x 0 R n and set k 0.
Outline Coordinate Minimization Daniel P. Robinson Department of Applied Mathematics and Statistics Johns Hopkins University November 27, 208 Introduction 2 Algorithms Cyclic order with exact minimization
More informationA Multi-Objective Approach to Portfolio Optimization
RoseHulman Undergraduate Mathematics Journal Volume 8 Issue Article 2 A MultiObjective Aroach to Portfolio Otimization Yaoyao Clare Duan Boston College, sweetclare@gmail.com Follow this and additional
More informationTHE OPTIMAL ASSET ALLOCATION PROBLEMFOR AN INVESTOR THROUGH UTILITY MAXIMIZATION
THE OPTIMAL ASSET ALLOCATION PROBLEMFOR AN INVESTOR THROUGH UTILITY MAXIMIZATION SILAS A. IHEDIOHA 1, BRIGHT O. OSU 2 1 Department of Mathematics, Plateau State University, Bokkos, P. M. B. 2012, Jos,
More informationGame Theory: Normal Form Games
Game Theory: Normal Form Games Michael Levet June 23, 2016 1 Introduction Game Theory is a mathematical field that studies how rational agents make decisions in both competitive and cooperative situations.
More informationCollinear Triple Hypergraphs and the Finite Plane Kakeya Problem
Collinear Triple Hypergraphs and the Finite Plane Kakeya Problem Joshua Cooper August 14, 006 Abstract We show that the problem of counting collinear points in a permutation (previously considered by the
More informationIntroduction to Probability Theory and Stochastic Processes for Finance Lecture Notes
Introduction to Probability Theory and Stochastic Processes for Finance Lecture Notes Fabio Trojani Department of Economics, University of St. Gallen, Switzerland Correspondence address: Fabio Trojani,
More informationTangent Lévy Models. Sergey Nadtochiy (joint work with René Carmona) Oxford-Man Institute of Quantitative Finance University of Oxford.
Tangent Lévy Models Sergey Nadtochiy (joint work with René Carmona) Oxford-Man Institute of Quantitative Finance University of Oxford June 24, 2010 6th World Congress of the Bachelier Finance Society Sergey
More informationOptimal Allocation of Policy Limits and Deductibles
Optimal Allocation of Policy Limits and Deductibles Ka Chun Cheung Email: kccheung@math.ucalgary.ca Tel: +1-403-2108697 Fax: +1-403-2825150 Department of Mathematics and Statistics, University of Calgary,
More informationOn the Distribution of Kurtosis Test for Multivariate Normality
On the Distribution of Kurtosis Test for Multivariate Normality Takashi Seo and Mayumi Ariga Department of Mathematical Information Science Tokyo University of Science 1-3, Kagurazaka, Shinjuku-ku, Tokyo,
More informationWorst-case evaluation complexity of regularization methods for smooth unconstrained optimization using Hölder continuous gradients
Worst-case evaluation comlexity of regularization methods for smooth unconstrained otimization using Hölder continuous gradients C Cartis N I M Gould and Ph L Toint 26 June 205 Abstract The worst-case
More informationOn Existence of Equilibria. Bayesian Allocation-Mechanisms
On Existence of Equilibria in Bayesian Allocation Mechanisms Northwestern University April 23, 2014 Bayesian Allocation Mechanisms In allocation mechanisms, agents choose messages. The messages determine
More informationarxiv: v5 [quant-ph] 16 Oct 2008
Violation of Equalities in Bipartite Qutrits Systems Hossein Movahhedian Department of Physics, Shahrood University of Technology, Seventh Tir Square, Shahrood, Iran We have recently shown that for the
More informationLecture 2. Main Topics: (Part II) Chapter 2 (2-7), Chapter 3. Bayes Theorem: Let A, B be two events, then. The probabilities P ( B), probability of B.
STT315, Section 701, Summer 006 Lecture (Part II) Main Toics: Chater (-7), Chater 3. Bayes Theorem: Let A, B be two events, then B A) = A B) B) A B) B) + A B) B) The robabilities P ( B), B) are called
More informationA Stochastic Model of Optimal Debt Management and Bankruptcy
A Stochastic Model of Otimal Debt Management and Bankrutcy Alberto Bressan (, Antonio Marigonda (, Khai T. Nguyen (, and Michele Palladino ( (* Deartment of Mathematics, Penn State University University
More informationINTERIM CORRELATED RATIONALIZABILITY IN INFINITE GAMES
INTERIM CORRELATED RATIONALIZABILITY IN INFINITE GAMES JONATHAN WEINSTEIN AND MUHAMET YILDIZ A. We show that, under the usual continuity and compactness assumptions, interim correlated rationalizability
More informationEfficiency in Decentralized Markets with Aggregate Uncertainty
Efficiency in Decentralized Markets with Aggregate Uncertainty Braz Camargo Dino Gerardi Lucas Maestri December 2015 Abstract We study efficiency in decentralized markets with aggregate uncertainty and
More informationGame Theory. Lecture Notes By Y. Narahari. Department of Computer Science and Automation Indian Institute of Science Bangalore, India August 2012
Game Theory Lecture Notes By Y. Narahari Department of Computer Science and Automation Indian Institute of Science Bangalore, India August 2012 Chapter 6: Mixed Strategies and Mixed Strategy Nash Equilibrium
More informationA class of coherent risk measures based on one-sided moments
A class of coherent risk measures based on one-sided moments T. Fischer Darmstadt University of Technology November 11, 2003 Abstract This brief paper explains how to obtain upper boundaries of shortfall
More informationNotes, Comments, and Letters to the Editor. Cores and Competitive Equilibria with Indivisibilities and Lotteries
journal of economic theory 68, 531543 (1996) article no. 0029 Notes, Comments, and Letters to the Editor Cores and Competitive Equilibria with Indivisibilities and Lotteries Rod Garratt and Cheng-Zhong
More informationLiquidation of a Large Block of Stock
Liquidation of a Large Block of Stock M. Pemy Q. Zhang G. Yin September 21, 2006 Abstract In the financial engineering literature, stock-selling rules are mainly concerned with liquidation of the security
More informationk-type null slant helices in Minkowski space-time
MATHEMATICAL COMMUNICATIONS 8 Math. Commun. 20(2015), 8 95 k-type null slant helices in Minkowski space-time Emilija Nešović 1, Esra Betül Koç Öztürk2, and Ufuk Öztürk2 1 Department of Mathematics and
More informationLECTURE NOTES 10 ARIEL M. VIALE
LECTURE NOTES 10 ARIEL M VIALE 1 Behavioral Asset Pricing 11 Prospect theory based asset pricing model Barberis, Huang, and Santos (2001) assume a Lucas pure-exchange economy with three types of assets:
More informationFUZZY PRIME L-FILTERS
International Journal of Applied Mathematical Sciences ISSN 0973-0176 Volume 9, Number 1 (2016), pp. 37-44 Research India Publications http://www.ripublication.com FUZZY PRIME L-FILTERS M. Mullai Assistant
More informationChapter 5 Finite Difference Methods. Math6911 W07, HM Zhu
Chapter 5 Finite Difference Methods Math69 W07, HM Zhu References. Chapters 5 and 9, Brandimarte. Section 7.8, Hull 3. Chapter 7, Numerical analysis, Burden and Faires Outline Finite difference (FD) approximation
More informationOn the Lower Arbitrage Bound of American Contingent Claims
On the Lower Arbitrage Bound of American Contingent Claims Beatrice Acciaio Gregor Svindland December 2011 Abstract We prove that in a discrete-time market model the lower arbitrage bound of an American
More informationMore On λ κ closed sets in generalized topological spaces
Journal of Algorithms and Computation journal homepage: http://jac.ut.ac.ir More On λ κ closed sets in generalized topological spaces R. Jamunarani, 1, P. Jeyanthi 2 and M. Velrajan 3 1,2 Research Center,
More informationCONGRUENCES AND IDEALS IN A DISTRIBUTIVE LATTICE WITH RESPECT TO A DERIVATION
Bulletin of the Section of Logic Volume 42:1/2 (2013), pp. 1 10 M. Sambasiva Rao CONGRUENCES AND IDEALS IN A DISTRIBUTIVE LATTICE WITH RESPECT TO A DERIVATION Abstract Two types of congruences are introduced
More informationStatistical Lacunary Invariant Summability
Theoetical Mathematics & Applications, vol.3, no.2, 203, 7-78 ISSN: 792-9687 (pint), 792-9709 (online) Scienpess Ltd, 203 Statistical Lacunay Invaiant Summability Nimet Pancaoglu and Fatih Nuay 2 Abstact
More informationA Preference Foundation for Fehr and Schmidt s Model. of Inequity Aversion 1
A Preference Foundation for Fehr and Schmidt s Model of Inequity Aversion 1 Kirsten I.M. Rohde 2 January 12, 2009 1 The author would like to thank Itzhak Gilboa, Ingrid M.T. Rohde, Klaus M. Schmidt, and
More informationEpimorphisms and Ideals of Distributive Nearlattices
Annals of Pure and Applied Mathematics Vol. 18, No. 2, 2018,175-179 ISSN: 2279-087X (P), 2279-0888(online) Published on 9 November 2018 www.researchmathsci.org DOI: http://dx.doi.org/10.22457/apam.v18n2a5
More informationAn Application of Ramsey Theorem to Stopping Games
An Application of Ramsey Theorem to Stopping Games Eran Shmaya, Eilon Solan and Nicolas Vieille July 24, 2001 Abstract We prove that every two-player non zero-sum deterministic stopping game with uniformly
More informationA Translation of Intersection and Union Types
A Translation of Intersection and Union Types for the λ µ-calculus Kentaro Kikuchi RIEC, Tohoku University kentaro@nue.riec.tohoku.ac.jp Takafumi Sakurai Department of Mathematics and Informatics, Chiba
More informationMarkets with convex transaction costs
1 Markets with convex transaction costs Irina Penner Humboldt University of Berlin Email: penner@math.hu-berlin.de Joint work with Teemu Pennanen Helsinki University of Technology Special Semester on Stochastics
More informationThe Limiting Distribution for the Number of Symbol Comparisons Used by QuickSort is Nondegenerate (Extended Abstract)
The Limiting Distribution for the Number of Symbol Comparisons Used by QuickSort is Nondegenerate (Extended Abstract) Patrick Bindjeme 1 James Allen Fill 1 1 Department of Applied Mathematics Statistics,
More informationLindner, Szimayer: A Limit Theorem for Copulas
Lindner, Szimayer: A Limit Theorem for Copulas Sonderforschungsbereich 386, Paper 433 (2005) Online unter: http://epub.ub.uni-muenchen.de/ Projektpartner A Limit Theorem for Copulas Alexander Lindner Alexander
More informationEquivalence between Semimartingales and Itô Processes
International Journal of Mathematical Analysis Vol. 9, 215, no. 16, 787-791 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/1.12988/ijma.215.411358 Equivalence between Semimartingales and Itô Processes
More informationFunctional vs Banach space stochastic calculus & strong-viscosity solutions to semilinear parabolic path-dependent PDEs.
Functional vs Banach space stochastic calculus & strong-viscosity solutions to semilinear parabolic path-dependent PDEs Andrea Cosso LPMA, Université Paris Diderot joint work with Francesco Russo ENSTA,
More informationRichardson Extrapolation Techniques for the Pricing of American-style Options
Richardson Extrapolation Techniques for the Pricing of American-style Options June 1, 2005 Abstract Richardson Extrapolation Techniques for the Pricing of American-style Options In this paper we re-examine
More informationA Combinatorial Proof for the Circular Chromatic Number of Kneser Graphs
A Combinatorial Proof for the Circular Chromatic Number of Kneser Graphs Daphne Der-Fen Liu Department of Mathematics California State University, Los Angeles, USA Email: dliu@calstatela.edu Xuding Zhu
More informationOptimization Problem In Single Period Markets
University of Central Florida Electronic Theses and Dissertations Masters Thesis (Open Access) Optimization Problem In Single Period Markets 2013 Tian Jiang University of Central Florida Find similar works
More informationMathematics in Finance
Mathematics in Finance Steven E. Shreve Department of Mathematical Sciences Carnegie Mellon University Pittsburgh, PA 15213 USA shreve@andrew.cmu.edu A Talk in the Series Probability in Science and Industry
More informationMESURES DE RISQUE DYNAMIQUES DYNAMIC RISK MEASURES
from BMO martingales MESURES DE RISQUE DYNAMIQUES DYNAMIC RISK MEASURES CNRS - CMAP Ecole Polytechnique March 1, 2007 1/ 45 OUTLINE from BMO martingales 1 INTRODUCTION 2 DYNAMIC RISK MEASURES Time Consistency
More informationCalibration Estimation under Non-response and Missing Values in Auxiliary Information
WORKING PAPER 2/2015 Calibration Estimation under Non-response and Missing Values in Auxiliary Information Thomas Laitila and Lisha Wang Statistics ISSN 1403-0586 http://www.oru.se/institutioner/handelshogskolan-vid-orebro-universitet/forskning/publikationer/working-papers/
More informationFinite Additivity in Dubins-Savage Gambling and Stochastic Games. Bill Sudderth University of Minnesota
Finite Additivity in Dubins-Savage Gambling and Stochastic Games Bill Sudderth University of Minnesota This talk is based on joint work with Lester Dubins, David Heath, Ashok Maitra, and Roger Purves.
More informationÉcole normale supérieure, MPRI, M2 Year 2007/2008. Course 2-6 Abstract interpretation: application to verification and static analysis P.
École normale supérieure, MPRI, M2 Year 2007/2008 Course 2-6 Abstract interpretation: application to verification and static analysis P. Cousot Questions and answers of the partial exam of Friday November
More informationAmerican Option Pricing Formula for Uncertain Financial Market
American Option Pricing Formula for Uncertain Financial Market Xiaowei Chen Uncertainty Theory Laboratory, Department of Mathematical Sciences Tsinghua University, Beijing 184, China chenxw7@mailstsinghuaeducn
More informationStrategies and Nash Equilibrium. A Whirlwind Tour of Game Theory
Strategies and Nash Equilibrium A Whirlwind Tour of Game Theory (Mostly from Fudenberg & Tirole) Players choose actions, receive rewards based on their own actions and those of the other players. Example,
More informationNUMERICAL METHODS OF PARTIAL INTEGRO-DIFFERENTIAL EQUATIONS FOR OPTION PRICE
Trends in Mathematics - New Series Information Center for Mathematical Sciences Volume 13, Number 1, 011, pages 1 5 NUMERICAL METHODS OF PARTIAL INTEGRO-DIFFERENTIAL EQUATIONS FOR OPTION PRICE YONGHOON
More informationStochastic Optimal Control
Stochastic Optimal Control Lecturer: Eilyan Bitar, Cornell ECE Scribe: Kevin Kircher, Cornell MAE These notes summarize some of the material from ECE 5555 (Stochastic Systems) at Cornell in the fall of
More informationOptimization Approaches Applied to Mathematical Finance
Optimization Approaches Applied to Mathematical Finance Tai-Ho Wang tai-ho.wang@baruch.cuny.edu Baruch-NSD Summer Camp Lecture 5 August 7, 2017 Outline Quick review of optimization problems and duality
More informationPATH-PEPENDENT PARABOLIC PDES AND PATH-DEPENDENT FEYNMAN-KAC FORMULA
PATH-PEPENDENT PARABOLIC PDES AND PATH-DEPENDENT FEYNMAN-KAC FORMULA CNRS, CMAP Ecole Polytechnique Bachelier Paris, january 8 2016 Dynamic Risk Measures and Path-Dependent second order PDEs, SEFE, Fred
More informationRevenue Management Under the Markov Chain Choice Model
Revenue Management Under the Markov Chain Choice Model Jacob B. Feldman School of Operations Research and Information Engineering, Cornell University, Ithaca, New York 14853, USA jbf232@cornell.edu Huseyin
More informationSmarandache Curves on S 2. Key Words: Smarandache Curves, Sabban Frame, Geodesic Curvature. Contents. 1 Introduction Preliminaries 52
Bol. Soc. Paran. Mat. (s.) v. (04): 5 59. c SPM ISSN-75-88 on line ISSN-00787 in press SPM: www.spm.uem.br/bspm doi:0.569/bspm.vi.94 Smarandache Curves on S Kemal Taşköprü and Murat Tosun abstract: In
More information1 Directed sets and nets
subnets2.tex April 22, 2009 http://thales.doa.fmph.uniba.sk/sleziak/texty/rozne/topo/ This text contains notes for my talk given at our topology seminar. It compares 3 different definitions of subnets.
More informationStochastic Proximal Algorithms with Applications to Online Image Recovery
1/24 Stochastic Proximal Algorithms with Applications to Online Image Recovery Patrick Louis Combettes 1 and Jean-Christophe Pesquet 2 1 Mathematics Department, North Carolina State University, Raleigh,
More informationComparative Study between Linear and Graphical Methods in Solving Optimization Problems
Comparative Study between Linear and Graphical Methods in Solving Optimization Problems Mona M Abd El-Kareem Abstract The main target of this paper is to establish a comparative study between the performance
More informationGlobal convergence rate analysis of unconstrained optimization methods based on probabilistic models
Math. Program., Ser. A DOI 10.1007/s10107-017-1137-4 FULL LENGTH PAPER Global convergence rate analysis of unconstrained optimization methods based on probabilistic models C. Cartis 1 K. Scheinberg 2 Received:
More informationarxiv:math/ v1 [math.lo] 15 Jan 1991
ON A CONJECTURE OF TARSKI ON PRODUCTS OF CARDINALS arxiv:math/9201247v1 [mathlo] 15 Jan 1991 Thomas Jech 1 and Saharon Shelah 2 Abstract 3 We look at an old conjecture of A Tarski on cardinal arithmetic
More informationRolodex Game in Networks
Rolodex Game in Networks Björn Brügemann Pieter Gautier Vrije Universiteit Amsterdam Vrije Universiteit Amsterdam Guido Menzio University of Pennsylvania and NBER August 2017 PRELIMINARY AND INCOMPLETE
More informationA NEW NOTION OF TRANSITIVE RELATIVE RETURN RATE AND ITS APPLICATIONS USING STOCHASTIC DIFFERENTIAL EQUATIONS. Burhaneddin İZGİ
A NEW NOTION OF TRANSITIVE RELATIVE RETURN RATE AND ITS APPLICATIONS USING STOCHASTIC DIFFERENTIAL EQUATIONS Burhaneddin İZGİ Department of Mathematics, Istanbul Technical University, Istanbul, Turkey
More informationStudy of Monotonicity of Trinomial Arcs M(p, k, r, n) when 1 <α<+
International Journal of Algebra, Vol. 1, 2007, no. 10, 477-485 Study of Monotonicity of Trinomial Arcs M(p, k, r, n) when 1
More information3 Arbitrage pricing theory in discrete time.
3 Arbitrage pricing theory in discrete time. Orientation. In the examples studied in Chapter 1, we worked with a single period model and Gaussian returns; in this Chapter, we shall drop these assumptions
More information