A CLASS OF EVEN DEGREE SPLINES OBTAINED THROUGH A MINIMUM CONDITION

Size: px
Start display at page:

Download "A CLASS OF EVEN DEGREE SPLINES OBTAINED THROUGH A MINIMUM CONDITION"

Transcription

1 STUDIA UNIV. BABEŞ BOLYAI, MATHEMATICA, Volume XLVIII, Number 3, September 2003 A CLASS OF EVEN DEGREE SPLINES OBTAINED THROUGH A MINIMUM CONDITION GH. MICULA, E. SANTI, AND M. G. CIMORONI Dedicated to Professor George Micula at is 60 t anniversary Abstract. A class of splines minimizing a special functional is investigated. Tis class is determined by te solution of quadratic programming problem. Convergence results and some numerical examples are given.. Introduction Te construction of splines, verifying minimum conditions as been proposed among oters in [2], [5], [6]. In suc papers te splines are interpolating te approximated function in te nodes and wile in [5] te constructive metod can be applied, in teory, for a spline of an arbitrary degree m, minimizing te integral I [g (x)] 2 dx, in [2] e [6] a cubic polynomial interpolating splines are considered satisfying some minimum conditions. In particular, in [6], te considered splines ave been applied for constructing quadrature sums approximating te Caucy principal value integrals I(wf; t) = w(x) f(x) dx. (.) x t In tis paper, utilizing te metod proposed in [2], we construct te spline of even degree minimizing te functional F (f) := [f (3) (x)] 2 dx f W2 3 (I) (.2) I were, denoting AC(I) te set of absolute continuous functions on I, { } W2 3 (I) := f : I IR, f (0) AC(I) and f (3) L 2 (I). (.3) Tis class of splines, called interpolating-derivative splines of degree 2m, m 2, as been determined in [3] by solving a linear system of m + n + equations, were n is te number of internal knots of te partition, and ten te autors proved tat te constructed spline solves problem (.2). Te convergence is proved by supposing f W m+ 2. Received by te editors:

2 GH. MICULA, E. SANTI, AND M. G. CIMORONI In tis paper, exploiting te different form tat we use for defining te interpolating - derivative spline, we can obtain convergence results under weaker conditions on f, tat gives more flexibility in te applications, as for example, wen we consider te numerical evaluation of Caucy singular integrals [8]. In Section 2 we give te details of te construction of te interpolatingderivative spline. In Section 3 we give some convergence results. Finally, in Section 4, some numerical experiments on test functions f are reported. In Appendix we prove some propositions wose results are necessary for proving teorem 2.5 and proposition 2.7 in Section 2 and teorems 3.2, 3.3 in Section Construction of derivative-interpolating spline Let m, n m two given integer positive numbers and Y IR n+,y := {y 0, y,..., y n} a given vector and n := {a = x 0 < x <... < x n < x n+ = b} a given partition of I [a, b] in n + subintervals I k := [x k, x k+ ), k = 0,,..., n, limiting ourselves, for te sake of simplicity, to consider an uniform partition n, wit = x i+ x i, i = 0,,..., n. We denote by IP k te set of polynomials of degree k. Consider te space of polynomial splines of degree 2m S 2m ( n ) = { s : s(x) = si (x) IP 2m, x I i, i = 0,,..., n; D j s i (x i ) = D j s i (x i ), j = 0,,..., 2m, i =, 2,..., n } (2.) wit simple knots x, x 2,..., x n. Te space S 2m ( n ) C 2m (I). A function s f S 2m ( n ) is called derivative-interpolating if s f (x 0 ) = y 0, s f (x i ) = y i, i =, 2,..., n; y 0 = f (x 0 ), y i = f(x i ). (2.2) Limiting ourselves to consider m = 2, if we set by successive integrations, we obtain M i = s (2m ) f (x i ), i = 0,,..., n +, s f (x) Ii = [M i+ (x x i ) 4 M i (x x i+ ) 4 ]/(4!)+ +a i (x x i ) 2 /2 + b i (x x i ) + c i, i = 0,,..., n. (2.3) By imposing te conditions (2.) and (2.2), we obtain 94

3 A CLASS OF EVEN DEGREE SPLINES system a i = y i+ y i 6 (M i+ M i ) i =,..., n b 0 = y 2 6 M a 0 b i = y i 2 6 M i i =,..., n c 0 = y M 0 c = c 0 + y 3 2 M 2 2 a 0 c i = c i + (y i + y i ) M i i = 2,..., n M i = a i a i i =,..., n. (2.4) Substituting te first equations of (2.4) in te last ones, we obtain a linear were à = à M = b (a 0, a n ) (2.5), M = M... M n, b = 6 [ y 2 y a 0,..., y i+ y i y i y i,..., y n y ] T n + a n. Te spline function s f (x) will be determined by solving te following problem wit M = [M 0,..., M n+ ] T, were { min M T ĀM à M = b (a 0, a n ) Ā = = A = e T 0 e A e n 0 e T n 2 (2.6), (2.7) (2.8) 95

4 GH. MICULA, E. SANTI, AND M. G. CIMORONI and e, e n are te vectors [, 0,..., 0] T, [0, 0,..., ] T respectively. We can write b = b e ã 0 + e n ã n wit b = 6 [ y 2 y,..., y i+ y i y i y i,..., y n y ] T n (2.9) and ã 0 = 6 a 0, ã n = 6 a n. Considering tat à is a symmetric positive definite and ten, non singular matrix, from (2.5) we get tus: M = à (b e ã 0 + e n ã n ) (2.0) ] min M T ĀM = min [M 0 M T M n+ 2 et 0 e A e n [ ] T M 0 M T M n+ 0 e T. (2.) n 2 Using (2.0), te problem amounts to find out firstly te vector solution of te linear system N = [ã 0, ã n, M 0, M n+ ] T, were, by setting C = à A Ã, [ ] B B B= 2 e T, B B 2 2I =[ Ce e T Ce n 2 e T n Ce e T n Ce n BN = P (2.2) ] [, B 2 = e T à e e T n à e e T à e n e T n à e n ]. (2.3) I 2 is te second order identity matrix and [ T P = e T Cb, et n Cb, et à b, e T n b] Ã. (2.4) Once determined N, we sall determine s f (x) by solving te system (2.5). Before proving te below teorem 2.5, we need to investigate some properties of matrices Ã, à and C. Proposition 2.. Te matrix à = (a ij) n i,j=, is: (a) symmetric, positive definite; (b) persymmetric, i.e. a ij = a n i+,n j+, i, j =,..., n; (c) totally positive (T.P.), i.e. all te minors are 0; (d) oscillatory, ten all te eigenvalues of à are distinct, real and positive. Proof. It is straigtforward to verify (a), (b), (c). Te property (d) follows by considering tat a non singular T.P. matrix aving te entries a ik 0, i k is oscillatory [4]. 96

5 A CLASS OF EVEN DEGREE SPLINES Proposition 2.2. Te infinitive norm of à satisfies te following relation 6 à 2. (2.5) Proof. (For te proof, see Appendix). Proposition 2.3. Te entries a j, j =,..., n of à ave decreasing absolute values, te sign of ( ) j, in particular, te following inequalities: old. 5 a = et à e 4, (2.6) a e n = T à e n Proof. (For te proof, see Appendix ). 24 if n = 2, 90 if n 3 (2.7) Proposition 2.4. Let C = à A Ã. For te entries c = e T Ce, c n = e T Ce n we ave: 0 < c < (2.8) Proof. ( For te proof, see Appendix). c n < c. (2.9) Now we prove te following: Teorem 2.5. Te system (2.2) is determined and te solution is [[ ] ] T N = e T à b, e T n à b B2, 0, 0. (2.20) Proof. Considering tat e T à e n = e T n à e and for te properties of à and te definition of te symmetric positive matrix A, one as e T Ce n = e T n Ce, (2.) can be written in te form [ ] [ ] [ ] B B 2 x t =, (2.2) B 2 2I 2 y t2 were x = [ã 0, ã n ], y = [ M 0, M n+ ], t = [ e T Cb, e T n Cb ] [ T T, t2 = e T à b, e T n b] Ã. Since A = à ( e e T + e n et n ), and ten, à A à = à à ( e e T + e n et n ) à tere results B = B 2 (I 2 B 2 ) (2.22) and te system (2.2) reduces to: [ B2 (I 2 B 2 ) B 2 B 2 2I 2 ] [ x y Te system (2.2) as unique solution. t = (I 2 B 2 ) t 2, (2.23) ] = [ ] (I2 B 2 ) t 2. (2.24) t 2 97

6 GH. MICULA, E. SANTI, AND M. G. CIMORONI In fact [4], since 2B 2 I 2 = 2I 2 B 2, det [ ] B2 (I 2 B 2 ) B 2 = det(b B 2 2I 2 ) det(2i 2 3B 2 ) 2 and using te results of propositions 2.2 and 2.3, it is straigtforward to verify tat det(b 2 ) 0 and det(2i 2 3B 2 ) 0. From te second equation of (2.24) we obtain x = B 2 (t 2 2I 2 y), and, substituting in te first one, tere results: tat implicates B 2 (I 2 B 2 ) B 2 (t 2 2y) + B 2y = (I 2 B 2 ) t 2, (3B 2 2I 2 )y = 0. (2.25) Terefore we obtain te solution y = 0, x = B2 t 2 and teorem 2.5 is proved. Corollary 2.6. For te spline s f (x) te following property olds. M = s (2m ) f (x ) = M n = s (2m ) f (x n ) = 0 (2.26) Proof. Taking into account tat, from te relation B 2 x + 2y = t 2, we obtain [ ] [ ] [ ] [ M0 = e T à b M n+ 2 e T + ] e T à e e T à e n ã0 n à b 2 e T n à e e T, (2.27) n à e n ã n we get te tesis considering te first and te last equation in (2.0), tat is: [ ] [ ] [ ] [ ] M e = T à b e M n e T T à e e T à e n ã0 n à b e T n à e e T. (2.28) n à e n ã n From teorem 2.5 and corollary 2.6 we can deduce tat, as aspected, te obtained spline s f (x) reduces to a polynomial of second degree in te subintervals I 0 and I n. Remark 2.. In [3, teorem 2] te construction of suc spline is obtained by solving a linear system of m + n + equations tat can ave an increasing condition number wen n increases. Our metod is based on te solution of two linear systems, aving matrix à and B respectively. ) By proposition 2.2, for eac n, for te condition number of Ã, we ave K (à 3; now we prove te following Proposition 2.7. For te condition number K (B) te inequality olds. 98 K (B) , if n 3, (2.29)

7 A CLASS OF EVEN DEGREE SPLINES [ ] B B Proof. B = 2, ten B 2 2I 2 [ B (2I2 3B = 2 ) 0 0 (2I 2 3B 2 ) ] [ 2B 2 I 2 I 2 I 2 B 2 Let n 3. Using te results obtained in te propositions 2.3 and 2.4, one obtains B 2 + a + a 407 n 80, (2.30) B 2 = a + a n 47 (2.3) 80 and B 2 = a a 5. n Besides: B (2I 2 3B 2 ) ( 2 B + ). (2.32) Using te results in [7], since for all vector x suc tat x =, one as and ten Terefore and we get te tesis. 3. Convergence results 2 (2I 2 3B 2 ) x = 73 60, (2.33) ]. B (2.34) K (B) , if n Consider I = [a, b] and te set W2 3. In [3] as been proved te following Teorem 3.. Let f W2 3 (I) and let s f be te derivative-interpolating spline, ten { f (k) s (k) C f 2 2 f (3) 2 if k = 0, C 2 2 k+ 2 f (3) (3.) 2 if k =, 2, were C = 2 (b a) and C 2 = 2. We sall prove a new convergence teorem under weaker ypotesis on function f. For all g C (I), we denote by ω (g ; ; I) = max x,x+δ I, 0 δ g (x + δ) g (x) te modulus of continuity of g. Supposing f C (I), from (2.9), we obtain ten, using (2.5), (2.20): b 2 2 ω (f ; ; I) (3.2) 99

8 GH. MICULA, E. SANTI, AND M. G. CIMORONI a 0, a n 5 ω (f ; ; I). (3.3) and consequently, from (2.0), we obtain: M = M 2 2 ω (f ; ; I). (3.4) If we consider tat f (x i ) = y i and by (2.4): a i = y i+ y i 6 (M i+ M i ) i =,..., n, we can write Terefore, a i 8 ω (f ; ; I). (3.5) a 8 ω (f ; ; I) (3.6) were a = [a 0, a,..., a n ] T. Teorem 3.2. Let f C (I) and s f (x) te interpolating-derivative spline quoted in Section 2 for a given partition n. Ten were C is a constant independent of. ω ( s f ; ; I ) Cω (f ; ; I) (3.7) Proof. It suffices to sow tat for u, v I, u < v : s f (v) s f (u) Cω (f ; v u; I). Firstly consider u, v [x i, x i+ ], i = 0,..., n; using te mean value teorem, we can derive: s f (v) s f (u) = s f (ξ) v u ξ (u, v), were v u. Since for any ξ (u, v), from (3.3), (3.4), (3.5), tere results s f (ξ) 29 ω (f ; ; I) if u, v [x i, x i+ ] t, i =, 2,..., n and s f (ξ) 5 ω (f ; ; I) if u, v [x 0, x ] or u, v [x n, x n+ ], recalling tat [9], v u ω (f ; ; I) 2ω (f ; v u ; I), we get s f (v) s f (u) C ω (f ; v u ; I), C = 58. (3.8) If u [x i, x i+ ], v [x j, x j+ ], i+ j, ten using (3.8) and te smootness of modulus of continuity and, since being x i+ and x j internal nodes, s f (x i+) = f (x i+ ), s f (x j) = f (x j ) : s f (v) s f (u) s f (v) s f (x j ) + s f (x j ) s f (x i+ ) + s f (x i+ ) s f (u) = s f (v) s f (x j ) + f (x j ) f (x i+ ) + s f (x i+ ) s f (u) (2C + ) ω (f ; v u ; I). Tis proves te teorem wit C = (2C + ). 00

9 A CLASS OF EVEN DEGREE SPLINES Supposing f C (I), we define r (x) = f (x) s f (x) and r (x) = f (x) s f (x), were s f (x) is te interpolating-derivative spline quoted in Section 2. For x I i, i = 0,..., n we can write { r r (x) = (x ) + (x x ) [x x] r if x I 0, r (x i ) + (x x i ) [x i x] r (3.9) if x I i, i =,..., n. were [x i x] r, i =,..., n, denotes te first divise difference of r. Terefore, from (2.2) and teorem 3.2: r (x) Ii (C + ) ω (f ; ; I), i = 0,,..., n. (3.0) We are ready to prove te following convergence result. Teorem 3.3. Let f C (I) and s f (x) te interpolating-derivative spline. Tere results f s f (b a) (C + ) ω (f ; ; I). (3.) Proof. We can write, for x I i, i = 0,,..., n x r (x) = r (t) dt max x x I r (x) x x 0 (b a) (C + ) ω (f ; ; I) (3.2) 0 and (3.) is proved. We remark tat te above teorems old even wen te partition n is quasiuniform, i.e. suc tat: is bounded for n were i = x i+ x i and max 0 i n i is te norm of te partition. [E.Santi, M.G.Cimoroni: Some new convergence results and applications of a class of interpolating-derivative splines. In preparation]. We add now a property of te splines considered in tis paper. Te derivativeinterpolating spline s f (x) defined in (2.3), considering a uniform partition n, reproduces any f IP 2. In fact, for f =, x, x 2 it is straigtforward to verify tat, b (a 0, a n ) = M = 0. Terefore te coefficients of s f (x) Ii are: and tus, for x I i, i = 0,..., n : 4. Numerical results a i = 0, b i = 0, c i =, if f(x) =, a i = 0, b i =, c i = x i, if f(x) = x, a i = 2, b i = 2x i, c i = x 2 i, if f(x) = x2, s f (x) =, if f(x) =, s f (x) = (x x i ) + x i = x, if f(x) = x, s f (x) = 2(x xi) x i (x x i ) + x 2 i = x2, if f(x) = x 2. We present now, some numerical results obtained by approximating some test functions by te spline considered in tis paper. We denote r n (x) = f (x) s f (x) te error at x obtained by using a uniform partition of te interval [, ] in n + subintervals. In table we report te results relative to a test function f(x) aving only f (x) C[, ] and considering different uniform partition wit 0

10 GH. MICULA, E. SANTI, AND M. G. CIMORONI n = 4, 9, 39, 99. In table 2 we report, for confirming te polynomial reproducibility, te results relative to a function f(x) IP 2 considering a uniform partition wit n = 4. Table 3 contains te results relative to a more regular function f(x) C [, ] wit different uniform partition, taking n = 4, 9, 39, 79. Table f(x) = sign(x)x 2 /2 + e x x r 4 (x) r 9 (x) r 39 (x) r 99 (x) (-3).2 (-4).4 (-5) 8.6 (-7) (-3).8 (-4).5 (-5) 8.6 (-7) (-2).7 (-3) 4.3 (-4) 6.8 (-5) (-2).8 (-3) 4.3 (-4) 6.8 (-5) 5.6 (-3) 2.5 (-3) 5.2 (-4) 7.4 (-5) Table 2 f(x) = x 2 + 2x 5 x r 4 (x) (-6) (-5) (-6) 8.9 (-6) Table 3 f(x) = /(x ) x r 4 (x) r 9 (x) r 39 (x) r 79 (x) (-5) 3.4 (-7) 4.4 (-8) 5.6 (-9) (-5) 3.4 (-7) 4.4 (-8) 5.6 (-9) (-5) 3.4 (-7) 4.4 (-8) 5.6 (-9) (-5) 3.4 (-7) 4.4 (-8) 5.6 (-9) (-7) Appendix Proposition 2.2. Te infinitive norm of à satisfies te following relation 6 à 2. (5.) Proof. It is straigtforward to verify tat à = 6 and ten, being à Ã, we obtain te left inequality in (5.). For proving tat à 2, we write à = 4I + H, were 02 H =

11 A CLASS OF EVEN DEGREE SPLINES Tus, for all x : x =, tere results Ãx = (4I + H)x 4x Hx 2, and ten [7], (5.) is proved. Proposition 2.3. Te entries a j, j =,..., n of à ave decreasing absolute values, te sign of ( ) j, in particular, te following inequalities: old. a n = 5 a = et à e 4, (5.2) 24 if n = 2, e T à e n 90 if n 3 (5.3) Proof. Using te results in [], for te evaluation of te inverse matrix of a tridiagonal symmetric matrix, we can write and ten, were l ij = 0 for i j. In our case, tere results à = L + uv T (5.4) a ij = l ij + u i v j (5.5) u =, u 2 = 5, u i = 4u i u i 2, i = 3,..., n (5.6) v i = α u n i+, i =,..., n (5.7) wit α = 5u n + u n.te matrix à is symmetric and a i a in a = a i = α u n i+, = a ni = α u i, i =, 2,..., n. Terefore, using proposition 2., we deduce tat a = a nn = u n /α, a j = n,n j+, j =, 2,..., n are decreasing in absolute value and ave te sign of ( )j, and, in particular, a n = /α. For proving (5.2) consider tat a = u v = u n /α = 5u n+u n u n 5 5u n+u n = ( ) 5 un 5u n+u n, and ten, using (5.6), 0 < un 5u n+u n = u n+u n 2 4 5u n+u n < 4, (5.3) follows. We get te inequality (5.3) considering tat a n = 5u n+u n and ten, from (5.6), a n = 24 if n = 2, a n = 90 for n = 3, and a n decreases wen n increases. Terefore te proposition is completely proved. Proposition 2.4. Let C = à A Ã. For te entries c = e T Ce, c n = e T Ce n we ave: 0 < c < (5.8) c n < c. (5.9) 03

12 GH. MICULA, E. SANTI, AND M. G. CIMORONI Proof. For n 3, [ by (2.22), using (5.2) and (5.3), it is straigtforward to verify tat (a 0 < c = a ) 2 ( ) + a 2 ] n < and c n = a ( ) n 2a < c. References [] Bevilacqua, R., Structural and computational properties of band matrices. Complexity of structured computational problems. Applied Matematics monograps. Comitato Nazionale per le Scienze Matematice, C.N.R. (99), [2] Bini, D., Capovani, M., A class of cubic splines obtained troug minimum conditions. Matematics of Computation 46, n.73 (986), [3] Blaga, P., Micula, G. Natural spline functions of even degree. Studia Univ. Babes-Bolyai Cluj-Napoca Matematica, 38, (993) Nr. 2, [4] Gantmacer, F.R., Te teory of matrices, Celsea Publ. Company N.Y [5] Gizzetti, A., Interpolazione con splines verificanti un opportuna condizione. Calcolo 20 (983), [6] Gori, L., Splines and Caucy principal value integrals. Proc. Intern. Worksop on Advanced Mat. Tools in Metrology (Ed. Ciarlini, Cox, Monaco, Pavese) (994), [7] Kersaw, D., A note on te convergence of interpolatory cubic splines. Siam J.Numer. Anal. 8 (97), [8] Rabinowitz, P., Numerical integration based on approximating splines. J. Comp. Appl. Mat. 33 (990), [9] Rabinowitz, P., Application on approximating splines for te solution of Caucy singular integral equations. Appl. Numer. Mat. 5 (994), Dept. of Applied Matematics, Babeş-Bolyai Univ. Cluj-Napoca, Romania address: gmicula@mat.ubbcluj.ro Dipartimento di Energetica, Universita degli Studi de l Aquila, Italy address: esanti@dsiaqi.ing.univaq.it Dipartimento di Energetica, Universita degli Studi de l Aquila, Italy 04

Polynomial Interpolation

Polynomial Interpolation Capter 4 Polynomial Interpolation In tis capter, we consider te important problem of approximatinga function fx, wose values at a set of distinct points x, x, x,, x n are known, by a polynomial P x suc

More information

A Nodal Spline Collocation Method for the Solution of Cauchy Singular Integral Equations 1

A Nodal Spline Collocation Method for the Solution of Cauchy Singular Integral Equations 1 European Society of Computational Methods in Sciences and Engineering (ESCMSE) Journal of Numerical Analysis, Industrial and Applied Mathematics (JNAIAM) vol. 3, no. 3-4, 2008, pp. 211-220 ISSN 1790 8140

More information

Polynomial Interpolation

Polynomial Interpolation Capter 4 Polynomial Interpolation In tis capter, we consider te important problem of approximating a function f(x, wose values at a set of distinct points x, x, x 2,,x n are known, by a polynomial P (x

More information

Math Spring 2013 Solutions to Assignment # 3 Completion Date: Wednesday May 15, (1/z) 2 (1/z 1) 2 = lim

Math Spring 2013 Solutions to Assignment # 3 Completion Date: Wednesday May 15, (1/z) 2 (1/z 1) 2 = lim Mat 311 - Spring 013 Solutions to Assignment # 3 Completion Date: Wednesday May 15, 013 Question 1. [p 56, #10 (a)] 4z Use te teorem of Sec. 17 to sow tat z (z 1) = 4. We ave z 4z (z 1) = z 0 4 (1/z) (1/z

More information

Continuity. Example 1

Continuity. Example 1 Continuity MATH 1003 Calculus and Linear Algebra (Lecture 13.5) Maoseng Xiong Department of Matematics, HKUST A function f : (a, b) R is continuous at a point c (a, b) if 1. x c f (x) exists, 2. f (c)

More information

POLYNOMIAL AND SPLINE ESTIMATORS OF THE DISTRIBUTION FUNCTION WITH PRESCRIBED ACCURACY

POLYNOMIAL AND SPLINE ESTIMATORS OF THE DISTRIBUTION FUNCTION WITH PRESCRIBED ACCURACY APPLICATIONES MATHEMATICAE 36, (29), pp. 2 Zbigniew Ciesielski (Sopot) Ryszard Zieliński (Warszawa) POLYNOMIAL AND SPLINE ESTIMATORS OF THE DISTRIBUTION FUNCTION WITH PRESCRIBED ACCURACY Abstract. Dvoretzky

More information

OSCILLATION OF SOLUTIONS TO NON-LINEAR DIFFERENCE EQUATIONS WITH SEVERAL ADVANCED ARGUMENTS. Sandra Pinelas and Julio G. Dix

OSCILLATION OF SOLUTIONS TO NON-LINEAR DIFFERENCE EQUATIONS WITH SEVERAL ADVANCED ARGUMENTS. Sandra Pinelas and Julio G. Dix Opuscula Mat. 37, no. 6 (2017), 887 898 ttp://dx.doi.org/10.7494/opmat.2017.37.6.887 Opuscula Matematica OSCILLATION OF SOLUTIONS TO NON-LINEAR DIFFERENCE EQUATIONS WITH SEVERAL ADVANCED ARGUMENTS Sandra

More information

University Mathematics 2

University Mathematics 2 University Matematics 2 1 Differentiability In tis section, we discuss te differentiability of functions. Definition 1.1 Differentiable function). Let f) be a function. We say tat f is differentiable at

More information

On convexity of polynomial paths and generalized majorizations

On convexity of polynomial paths and generalized majorizations On convexity of polynomial pats and generalized majorizations Marija Dodig Centro de Estruturas Lineares e Combinatórias, CELC, Universidade de Lisboa, Av. Prof. Gama Pinto 2, 1649-003 Lisboa, Portugal

More information

Packing polynomials on multidimensional integer sectors

Packing polynomials on multidimensional integer sectors Pacing polynomials on multidimensional integer sectors Luis B Morales IIMAS, Universidad Nacional Autónoma de México, Ciudad de México, 04510, México lbm@unammx Submitted: Jun 3, 015; Accepted: Sep 8,

More information

Convexity and Smoothness

Convexity and Smoothness Capter 4 Convexity and Smootness 4.1 Strict Convexity, Smootness, and Gateaux Differentiablity Definition 4.1.1. Let X be a Banac space wit a norm denoted by. A map f : X \{0} X \{0}, f f x is called a

More information

Numerical Experiments Using MATLAB: Superconvergence of Nonconforming Finite Element Approximation for Second-Order Elliptic Problems

Numerical Experiments Using MATLAB: Superconvergence of Nonconforming Finite Element Approximation for Second-Order Elliptic Problems Applied Matematics, 06, 7, 74-8 ttp://wwwscirporg/journal/am ISSN Online: 5-7393 ISSN Print: 5-7385 Numerical Experiments Using MATLAB: Superconvergence of Nonconforming Finite Element Approximation for

More information

Generic maximum nullity of a graph

Generic maximum nullity of a graph Generic maximum nullity of a grap Leslie Hogben Bryan Sader Marc 5, 2008 Abstract For a grap G of order n, te maximum nullity of G is defined to be te largest possible nullity over all real symmetric n

More information

arxiv: v1 [math.dg] 4 Feb 2015

arxiv: v1 [math.dg] 4 Feb 2015 CENTROID OF TRIANGLES ASSOCIATED WITH A CURVE arxiv:1502.01205v1 [mat.dg] 4 Feb 2015 Dong-Soo Kim and Dong Seo Kim Abstract. Arcimedes sowed tat te area between a parabola and any cord AB on te parabola

More information

A Structural Theorem of the Generalized Spline Functions 1

A Structural Theorem of the Generalized Spline Functions 1 General Mathematics Vol. 17, No. 2 (2009), 135 143 A Structural Theorem of the Generalized Spline Functions 1 Adrian Branga Abstract In the introduction of this paper is presented the definition of the

More information

Differentiation in higher dimensions

Differentiation in higher dimensions Capter 2 Differentiation in iger dimensions 2.1 Te Total Derivative Recall tat if f : R R is a 1-variable function, and a R, we say tat f is differentiable at x = a if and only if te ratio f(a+) f(a) tends

More information

A SHORT INTRODUCTION TO BANACH LATTICES AND

A SHORT INTRODUCTION TO BANACH LATTICES AND CHAPTER A SHORT INTRODUCTION TO BANACH LATTICES AND POSITIVE OPERATORS In tis capter we give a brief introduction to Banac lattices and positive operators. Most results of tis capter can be found, e.g.,

More information

lecture 26: Richardson extrapolation

lecture 26: Richardson extrapolation 43 lecture 26: Ricardson extrapolation 35 Ricardson extrapolation, Romberg integration Trougout numerical analysis, one encounters procedures tat apply some simple approximation (eg, linear interpolation)

More information

4. The slope of the line 2x 7y = 8 is (a) 2/7 (b) 7/2 (c) 2 (d) 2/7 (e) None of these.

4. The slope of the line 2x 7y = 8 is (a) 2/7 (b) 7/2 (c) 2 (d) 2/7 (e) None of these. Mat 11. Test Form N Fall 016 Name. Instructions. Te first eleven problems are wort points eac. Te last six problems are wort 5 points eac. For te last six problems, you must use relevant metods of algebra

More information

ch (for some fixed positive number c) reaching c

ch (for some fixed positive number c) reaching c GSTF Journal of Matematics Statistics and Operations Researc (JMSOR) Vol. No. September 05 DOI 0.60/s4086-05-000-z Nonlinear Piecewise-defined Difference Equations wit Reciprocal and Cubic Terms Ramadan

More information

Convexity and Smoothness

Convexity and Smoothness Capter 4 Convexity and Smootness 4. Strict Convexity, Smootness, and Gateaux Di erentiablity Definition 4... Let X be a Banac space wit a norm denoted by k k. A map f : X \{0}!X \{0}, f 7! f x is called

More information

Order of Accuracy. ũ h u Ch p, (1)

Order of Accuracy. ũ h u Ch p, (1) Order of Accuracy 1 Terminology We consider a numerical approximation of an exact value u. Te approximation depends on a small parameter, wic can be for instance te grid size or time step in a numerical

More information

MATH745 Fall MATH745 Fall

MATH745 Fall MATH745 Fall MATH745 Fall 5 MATH745 Fall 5 INTRODUCTION WELCOME TO MATH 745 TOPICS IN NUMERICAL ANALYSIS Instructor: Dr Bartosz Protas Department of Matematics & Statistics Email: bprotas@mcmasterca Office HH 36, Ext

More information

N igerian Journal of M athematics and Applications V olume 23, (2014), 1 13

N igerian Journal of M athematics and Applications V olume 23, (2014), 1 13 N igerian Journal of M atematics and Applications V olume 23, (24), 3 c N ig. J. M at. Appl. ttp : //www.kwsman.com CONSTRUCTION OF POLYNOMIAL BASIS AND ITS APPLICATION TO ORDINARY DIFFERENTIAL EQUATIONS

More information

New Fourth Order Quartic Spline Method for Solving Second Order Boundary Value Problems

New Fourth Order Quartic Spline Method for Solving Second Order Boundary Value Problems MATEMATIKA, 2015, Volume 31, Number 2, 149 157 c UTM Centre for Industrial Applied Matematics New Fourt Order Quartic Spline Metod for Solving Second Order Boundary Value Problems 1 Osama Ala yed, 2 Te

More information

Numerical Differentiation

Numerical Differentiation Numerical Differentiation Finite Difference Formulas for te first derivative (Using Taylor Expansion tecnique) (section 8.3.) Suppose tat f() = g() is a function of te variable, and tat as 0 te function

More information

Solutions to the Multivariable Calculus and Linear Algebra problems on the Comprehensive Examination of January 31, 2014

Solutions to the Multivariable Calculus and Linear Algebra problems on the Comprehensive Examination of January 31, 2014 Solutions to te Multivariable Calculus and Linear Algebra problems on te Compreensive Examination of January 3, 24 Tere are 9 problems ( points eac, totaling 9 points) on tis portion of te examination.

More information

Poisson Equation in Sobolev Spaces

Poisson Equation in Sobolev Spaces Poisson Equation in Sobolev Spaces OcMountain Dayligt Time. 6, 011 Today we discuss te Poisson equation in Sobolev spaces. It s existence, uniqueness, and regularity. Weak Solution. u = f in, u = g on

More information

arxiv: v1 [math.na] 28 Apr 2017

arxiv: v1 [math.na] 28 Apr 2017 THE SCOTT-VOGELIUS FINITE ELEMENTS REVISITED JOHNNY GUZMÁN AND L RIDGWAY SCOTT arxiv:170500020v1 [matna] 28 Apr 2017 Abstract We prove tat te Scott-Vogelius finite elements are inf-sup stable on sape-regular

More information

Symmetry Labeling of Molecular Energies

Symmetry Labeling of Molecular Energies Capter 7. Symmetry Labeling of Molecular Energies Notes: Most of te material presented in tis capter is taken from Bunker and Jensen 1998, Cap. 6, and Bunker and Jensen 2005, Cap. 7. 7.1 Hamiltonian Symmetry

More information

A Finite Element Primer

A Finite Element Primer A Finite Element Primer David J. Silvester Scool of Matematics, University of Mancester d.silvester@mancester.ac.uk. Version.3 updated 4 October Contents A Model Diffusion Problem.................... x.

More information

MA455 Manifolds Solutions 1 May 2008

MA455 Manifolds Solutions 1 May 2008 MA455 Manifolds Solutions 1 May 2008 1. (i) Given real numbers a < b, find a diffeomorpism (a, b) R. Solution: For example first map (a, b) to (0, π/2) and ten map (0, π/2) diffeomorpically to R using

More information

Volume 29, Issue 3. Existence of competitive equilibrium in economies with multi-member households

Volume 29, Issue 3. Existence of competitive equilibrium in economies with multi-member households Volume 29, Issue 3 Existence of competitive equilibrium in economies wit multi-member ouseolds Noriisa Sato Graduate Scool of Economics, Waseda University Abstract Tis paper focuses on te existence of

More information

An approximation method using approximate approximations

An approximation method using approximate approximations Applicable Analysis: An International Journal Vol. 00, No. 00, September 2005, 1 13 An approximation metod using approximate approximations FRANK MÜLLER and WERNER VARNHORN, University of Kassel, Germany,

More information

1. Consider the trigonometric function f(t) whose graph is shown below. Write down a possible formula for f(t).

1. Consider the trigonometric function f(t) whose graph is shown below. Write down a possible formula for f(t). . Consider te trigonometric function f(t) wose grap is sown below. Write down a possible formula for f(t). Tis function appears to be an odd, periodic function tat as been sifted upwards, so we will use

More information

On convergence of the immersed boundary method for elliptic interface problems

On convergence of the immersed boundary method for elliptic interface problems On convergence of te immersed boundary metod for elliptic interface problems Zilin Li January 26, 2012 Abstract Peskin s Immersed Boundary (IB) metod is one of te most popular numerical metods for many

More information

1. Questions (a) through (e) refer to the graph of the function f given below. (A) 0 (B) 1 (C) 2 (D) 4 (E) does not exist

1. Questions (a) through (e) refer to the graph of the function f given below. (A) 0 (B) 1 (C) 2 (D) 4 (E) does not exist Mat 1120 Calculus Test 2. October 18, 2001 Your name Te multiple coice problems count 4 points eac. In te multiple coice section, circle te correct coice (or coices). You must sow your work on te oter

More information

Mathematics 5 Worksheet 11 Geometry, Tangency, and the Derivative

Mathematics 5 Worksheet 11 Geometry, Tangency, and the Derivative Matematics 5 Workseet 11 Geometry, Tangency, and te Derivative Problem 1. Find te equation of a line wit slope m tat intersects te point (3, 9). Solution. Te equation for a line passing troug a point (x

More information

EQUICONTINUITY AND SINGULARITIES OF FAMILIES OF MONOMIAL MAPPINGS

EQUICONTINUITY AND SINGULARITIES OF FAMILIES OF MONOMIAL MAPPINGS STUDIA UNIV. BABEŞ BOLYAI, MATHEMATICA, Volume LI, Number 3, September 2006 EQUICONTINUITY AND SINGULARITIES OF FAMILIES OF MONOMIAL MAPPINGS WOLFGANG W. BRECKNER and TIBERIU TRIF Dedicated to Professor

More information

The Derivative as a Function

The Derivative as a Function Section 2.2 Te Derivative as a Function 200 Kiryl Tsiscanka Te Derivative as a Function DEFINITION: Te derivative of a function f at a number a, denoted by f (a), is if tis limit exists. f (a) f(a + )

More information

Math 212-Lecture 9. For a single-variable function z = f(x), the derivative is f (x) = lim h 0

Math 212-Lecture 9. For a single-variable function z = f(x), the derivative is f (x) = lim h 0 3.4: Partial Derivatives Definition Mat 22-Lecture 9 For a single-variable function z = f(x), te derivative is f (x) = lim 0 f(x+) f(x). For a function z = f(x, y) of two variables, to define te derivatives,

More information

Research Article Error Analysis for a Noisy Lacunary Cubic Spline Interpolation and a Simple Noisy Cubic Spline Quasi Interpolation

Research Article Error Analysis for a Noisy Lacunary Cubic Spline Interpolation and a Simple Noisy Cubic Spline Quasi Interpolation Advances in Numerical Analysis Volume 204, Article ID 35394, 8 pages ttp://dx.doi.org/0.55/204/35394 Researc Article Error Analysis for a Noisy Lacunary Cubic Spline Interpolation and a Simple Noisy Cubic

More information

ERROR BOUNDS FOR THE METHODS OF GLIMM, GODUNOV AND LEVEQUE BRADLEY J. LUCIER*

ERROR BOUNDS FOR THE METHODS OF GLIMM, GODUNOV AND LEVEQUE BRADLEY J. LUCIER* EO BOUNDS FO THE METHODS OF GLIMM, GODUNOV AND LEVEQUE BADLEY J. LUCIE* Abstract. Te expected error in L ) attimet for Glimm s sceme wen applied to a scalar conservation law is bounded by + 2 ) ) /2 T

More information

Analytic Functions. Differentiable Functions of a Complex Variable

Analytic Functions. Differentiable Functions of a Complex Variable Analytic Functions Differentiable Functions of a Complex Variable In tis capter, we sall generalize te ideas for polynomials power series of a complex variable we developed in te previous capter to general

More information

Implicit-explicit variational integration of highly oscillatory problems

Implicit-explicit variational integration of highly oscillatory problems Implicit-explicit variational integration of igly oscillatory problems Ari Stern Structured Integrators Worksop April 9, 9 Stern, A., and E. Grinspun. Multiscale Model. Simul., to appear. arxiv:88.39 [mat.na].

More information

Consider a function f we ll specify which assumptions we need to make about it in a minute. Let us reformulate the integral. 1 f(x) dx.

Consider a function f we ll specify which assumptions we need to make about it in a minute. Let us reformulate the integral. 1 f(x) dx. Capter 2 Integrals as sums and derivatives as differences We now switc to te simplest metods for integrating or differentiating a function from its function samples. A careful study of Taylor expansions

More information

The Convergence of a Central-Difference Discretization of Rudin-Osher-Fatemi Model for Image Denoising

The Convergence of a Central-Difference Discretization of Rudin-Osher-Fatemi Model for Image Denoising Te Convergence of a Central-Difference Discretization of Rudin-Oser-Fatemi Model for Image Denoising Ming-Jun Lai 1, Bradley Lucier 2, and Jingyue Wang 3 1 University of Georgia, Atens GA 30602, USA mjlai@mat.uga.edu

More information

Notes on Multigrid Methods

Notes on Multigrid Methods Notes on Multigrid Metods Qingai Zang April, 17 Motivation of multigrids. Te convergence rates of classical iterative metod depend on te grid spacing, or problem size. In contrast, convergence rates of

More information

Click here to see an animation of the derivative

Click here to see an animation of the derivative Differentiation Massoud Malek Derivative Te concept of derivative is at te core of Calculus; It is a very powerful tool for understanding te beavior of matematical functions. It allows us to optimize functions,

More information

A best approximation property of the generalized spline functions

A best approximation property of the generalized spline functions General Mathematics Vol. 16, No. 4 (2008), 25 33 A best approximation property of the generalized spline functions Adrian Branga Abstract In the introduction of this paper is presented the definition of

More information

Global Existence of Classical Solutions for a Class Nonlinear Parabolic Equations

Global Existence of Classical Solutions for a Class Nonlinear Parabolic Equations Global Journal of Science Frontier Researc Matematics and Decision Sciences Volume 12 Issue 8 Version 1.0 Type : Double Blind Peer Reviewed International Researc Journal Publiser: Global Journals Inc.

More information

Numerical study of the Benjamin-Bona-Mahony-Burgers equation

Numerical study of the Benjamin-Bona-Mahony-Burgers equation Bol. Soc. Paran. Mat. (3s.) v. 35 1 (017): 17 138. c SPM ISSN-175-1188 on line ISSN-0037871 in press SPM: www.spm.uem.br/bspm doi:10.569/bspm.v35i1.8804 Numerical study of te Benjamin-Bona-Maony-Burgers

More information

A method for solving first order fuzzy differential equation

A method for solving first order fuzzy differential equation Available online at ttp://ijim.srbiau.ac.ir/ Int. J. Industrial Matematics (ISSN 2008-5621) Vol. 5, No. 3, 2013 Article ID IJIM-00250, 7 pages Researc Article A metod for solving first order fuzzy differential

More information

Research Article Discrete Mixed Petrov-Galerkin Finite Element Method for a Fourth-Order Two-Point Boundary Value Problem

Research Article Discrete Mixed Petrov-Galerkin Finite Element Method for a Fourth-Order Two-Point Boundary Value Problem International Journal of Matematics and Matematical Sciences Volume 2012, Article ID 962070, 18 pages doi:10.1155/2012/962070 Researc Article Discrete Mixed Petrov-Galerkin Finite Element Metod for a Fourt-Order

More information

5 Ordinary Differential Equations: Finite Difference Methods for Boundary Problems

5 Ordinary Differential Equations: Finite Difference Methods for Boundary Problems 5 Ordinary Differential Equations: Finite Difference Metods for Boundary Problems Read sections 10.1, 10.2, 10.4 Review questions 10.1 10.4, 10.8 10.9, 10.13 5.1 Introduction In te previous capters we

More information

Simpson s 1/3 Rule Simpson s 1/3 rule assumes 3 equispaced data/interpolation/integration points

Simpson s 1/3 Rule Simpson s 1/3 rule assumes 3 equispaced data/interpolation/integration points CE 05 - Lecture 5 LECTURE 5 UMERICAL ITEGRATIO COTIUED Simpson s / Rule Simpson s / rule assumes equispaced data/interpolation/integration points Te integration rule is based on approximating fx using

More information

Preconditioning in H(div) and Applications

Preconditioning in H(div) and Applications 1 Preconditioning in H(div) and Applications Douglas N. Arnold 1, Ricard S. Falk 2 and Ragnar Winter 3 4 Abstract. Summarizing te work of [AFW97], we sow ow to construct preconditioners using domain decomposition

More information

LECTURE 14 NUMERICAL INTEGRATION. Find

LECTURE 14 NUMERICAL INTEGRATION. Find LECTURE 14 NUMERCAL NTEGRATON Find b a fxdx or b a vx ux fx ydy dx Often integration is required. However te form of fx may be suc tat analytical integration would be very difficult or impossible. Use

More information

Math 102 TEST CHAPTERS 3 & 4 Solutions & Comments Fall 2006

Math 102 TEST CHAPTERS 3 & 4 Solutions & Comments Fall 2006 Mat 102 TEST CHAPTERS 3 & 4 Solutions & Comments Fall 2006 f(x+) f(x) 10 1. For f(x) = x 2 + 2x 5, find ))))))))) and simplify completely. NOTE: **f(x+) is NOT f(x)+! f(x+) f(x) (x+) 2 + 2(x+) 5 ( x 2

More information

FINITE DIFFERENCE APPROXIMATIONS FOR NONLINEAR FIRST ORDER PARTIAL DIFFERENTIAL EQUATIONS

FINITE DIFFERENCE APPROXIMATIONS FOR NONLINEAR FIRST ORDER PARTIAL DIFFERENTIAL EQUATIONS UNIVERSITATIS IAGELLONICAE ACTA MATHEMATICA, FASCICULUS XL 2002 FINITE DIFFERENCE APPROXIMATIONS FOR NONLINEAR FIRST ORDER PARTIAL DIFFERENTIAL EQUATIONS by Anna Baranowska Zdzis law Kamont Abstract. Classical

More information

Continuity and Differentiability of the Trigonometric Functions

Continuity and Differentiability of the Trigonometric Functions [Te basis for te following work will be te definition of te trigonometric functions as ratios of te sides of a triangle inscribed in a circle; in particular, te sine of an angle will be defined to be te

More information

The total error in numerical differentiation

The total error in numerical differentiation AMS 147 Computational Metods and Applications Lecture 08 Copyrigt by Hongyun Wang, UCSC Recap: Loss of accuracy due to numerical cancellation A B 3, 3 ~10 16 In calculating te difference between A and

More information

THE IMPLICIT FUNCTION THEOREM

THE IMPLICIT FUNCTION THEOREM THE IMPLICIT FUNCTION THEOREM ALEXANDRU ALEMAN 1. Motivation and statement We want to understand a general situation wic occurs in almost any area wic uses matematics. Suppose we are given number of equations

More information

Overlapping domain decomposition methods for elliptic quasi-variational inequalities related to impulse control problem with mixed boundary conditions

Overlapping domain decomposition methods for elliptic quasi-variational inequalities related to impulse control problem with mixed boundary conditions Proc. Indian Acad. Sci. (Mat. Sci.) Vol. 121, No. 4, November 2011, pp. 481 493. c Indian Academy of Sciences Overlapping domain decomposition metods for elliptic quasi-variational inequalities related

More information

CLOSED CONVEX SHELLS Meunargia T. Mixed forms of stress-strain relations are given in the form. λ + 2µ θ + 1

CLOSED CONVEX SHELLS Meunargia T. Mixed forms of stress-strain relations are given in the form. λ + 2µ θ + 1 Seminar of I. Vekua Institute of Applied Matematics REPORTS, Vol. 43, 207 CLOSED CONVEX SHELLS Meunargia T. Abstract. If Ω is a closed convex sell, ten S : x 3 = 0 is an ovaloid. It is proved tat in tis

More information

A New Class of Zienkiewicz-Type Nonconforming Element in Any Dimensions

A New Class of Zienkiewicz-Type Nonconforming Element in Any Dimensions Numerisce Matematik manuscript No. will be inserted by te editor A New Class of Zienkiewicz-Type Nonconforming Element in Any Dimensions Wang Ming 1, Zong-ci Si 2, Jincao Xu1,3 1 LMAM, Scool of Matematical

More information

Section 3.1: Derivatives of Polynomials and Exponential Functions

Section 3.1: Derivatives of Polynomials and Exponential Functions Section 3.1: Derivatives of Polynomials and Exponential Functions In previous sections we developed te concept of te derivative and derivative function. Te only issue wit our definition owever is tat it

More information

3.1. COMPLEX DERIVATIVES 89

3.1. COMPLEX DERIVATIVES 89 3.1. COMPLEX DERIVATIVES 89 Teorem 3.1.4 (Cain rule) Let f : D D C and g : D C be olomorpic functions on te domains D and D respectively. Suppose tat bot f and g are C 1 -smoot. 10 Ten (g f) (z) g (f(z))

More information

How to Find the Derivative of a Function: Calculus 1

How to Find the Derivative of a Function: Calculus 1 Introduction How to Find te Derivative of a Function: Calculus 1 Calculus is not an easy matematics course Te fact tat you ave enrolled in suc a difficult subject indicates tat you are interested in te

More information

Mass Lumping for Constant Density Acoustics

Mass Lumping for Constant Density Acoustics Lumping 1 Mass Lumping for Constant Density Acoustics William W. Symes ABSTRACT Mass lumping provides an avenue for efficient time-stepping of time-dependent problems wit conforming finite element spatial

More information

Test 2 Review. 1. Find the determinant of the matrix below using (a) cofactor expansion and (b) row reduction. A = 3 2 =

Test 2 Review. 1. Find the determinant of the matrix below using (a) cofactor expansion and (b) row reduction. A = 3 2 = Test Review Find te determinant of te matrix below using (a cofactor expansion and (b row reduction Answer: (a det + = (b Observe R R R R R R R R R Ten det B = (((det Hence det Use Cramer s rule to solve:

More information

Math 161 (33) - Final exam

Math 161 (33) - Final exam Name: Id #: Mat 161 (33) - Final exam Fall Quarter 2015 Wednesday December 9, 2015-10:30am to 12:30am Instructions: Prob. Points Score possible 1 25 2 25 3 25 4 25 TOTAL 75 (BEST 3) Read eac problem carefully.

More information

DIFFERENTIAL POLYNOMIALS GENERATED BY SECOND ORDER LINEAR DIFFERENTIAL EQUATIONS

DIFFERENTIAL POLYNOMIALS GENERATED BY SECOND ORDER LINEAR DIFFERENTIAL EQUATIONS Journal o Applied Analysis Vol. 14, No. 2 2008, pp. 259 271 DIFFERENTIAL POLYNOMIALS GENERATED BY SECOND ORDER LINEAR DIFFERENTIAL EQUATIONS B. BELAÏDI and A. EL FARISSI Received December 5, 2007 and,

More information

Department of Mathematics, K.T.H.M. College, Nashik F.Y.B.Sc. Calculus Practical (Academic Year )

Department of Mathematics, K.T.H.M. College, Nashik F.Y.B.Sc. Calculus Practical (Academic Year ) F.Y.B.Sc. Calculus Practical (Academic Year 06-7) Practical : Graps of Elementary Functions. a) Grap of y = f(x) mirror image of Grap of y = f(x) about X axis b) Grap of y = f( x) mirror image of Grap

More information

Parametric Spline Method for Solving Bratu s Problem

Parametric Spline Method for Solving Bratu s Problem ISSN 749-3889 print, 749-3897 online International Journal of Nonlinear Science Vol4202 No,pp3-0 Parametric Spline Metod for Solving Bratu s Problem M Zarebnia, Z Sarvari 2,2 Department of Matematics,

More information

Some Review Problems for First Midterm Mathematics 1300, Calculus 1

Some Review Problems for First Midterm Mathematics 1300, Calculus 1 Some Review Problems for First Midterm Matematics 00, Calculus. Consider te trigonometric function f(t) wose grap is sown below. Write down a possible formula for f(t). Tis function appears to be an odd,

More information

On the best approximation of function classes from values on a uniform grid in the real line

On the best approximation of function classes from values on a uniform grid in the real line Proceedings of te 5t WSEAS Int Conf on System Science and Simulation in Engineering, Tenerife, Canary Islands, Spain, December 16-18, 006 459 On te best approximation of function classes from values on

More information

Math 242: Principles of Analysis Fall 2016 Homework 7 Part B Solutions

Math 242: Principles of Analysis Fall 2016 Homework 7 Part B Solutions Mat 22: Principles of Analysis Fall 206 Homework 7 Part B Solutions. Sow tat f(x) = x 2 is not uniformly continuous on R. Solution. Te equation is equivalent to f(x) = 0 were f(x) = x 2 sin(x) 3. Since

More information

The complex exponential function

The complex exponential function Capter Te complex exponential function Tis is a very important function!. Te series For any z C, we define: exp(z) := n! zn = + z + z2 2 + z3 6 + z4 24 + On te closed disk D(0,R) := {z C z R}, one as n!

More information

FUNDAMENTAL UNITS AND REGULATORS OF AN INFINITE FAMILY OF CYCLIC QUARTIC FUNCTION FIELDS

FUNDAMENTAL UNITS AND REGULATORS OF AN INFINITE FAMILY OF CYCLIC QUARTIC FUNCTION FIELDS J. Korean Mat. Soc. 54 (017), No., pp. 417 46 ttps://doi.org/10.4134/jkms.j16000 pissn: 0304-9914 / eissn: 34-3008 FUNDAMENTAL UNITS AND REGULATORS OF AN INFINITE FAMILY OF CYCLIC QUARTIC FUNCTION FIELDS

More information

Chapter 8. Numerical Solution of Ordinary Differential Equations. Module No. 2. Predictor-Corrector Methods

Chapter 8. Numerical Solution of Ordinary Differential Equations. Module No. 2. Predictor-Corrector Methods Numerical Analysis by Dr. Anita Pal Assistant Professor Department of Matematics National Institute of Tecnology Durgapur Durgapur-7109 email: anita.buie@gmail.com 1 . Capter 8 Numerical Solution of Ordinary

More information

Exercises for numerical differentiation. Øyvind Ryan

Exercises for numerical differentiation. Øyvind Ryan Exercises for numerical differentiation Øyvind Ryan February 25, 2013 1. Mark eac of te following statements as true or false. a. Wen we use te approximation f (a) (f (a +) f (a))/ on a computer, we can

More information

Numerical Analysis MTH603. dy dt = = (0) , y n+1. We obtain yn. Therefore. and. Copyright Virtual University of Pakistan 1

Numerical Analysis MTH603. dy dt = = (0) , y n+1. We obtain yn. Therefore. and. Copyright Virtual University of Pakistan 1 Numerical Analysis MTH60 PREDICTOR CORRECTOR METHOD Te metods presented so far are called single-step metods, were we ave seen tat te computation of y at t n+ tat is y n+ requires te knowledge of y n only.

More information

= 0 and states ''hence there is a stationary point'' All aspects of the proof dx must be correct (c)

= 0 and states ''hence there is a stationary point'' All aspects of the proof dx must be correct (c) Paper 1: Pure Matematics 1 Mark Sceme 1(a) (i) (ii) d d y 3 1x 4x x M1 A1 d y dx 1.1b 1.1b 36x 48x A1ft 1.1b Substitutes x = into teir dx (3) 3 1 4 Sows d y 0 and states ''ence tere is a stationary point''

More information

The Column and Row Hilbert Operator Spaces

The Column and Row Hilbert Operator Spaces Te Column and Row Hilbert Operator Spaces Roy M Araiza Department of Matematics Purdue University Abstract Given a Hilbert space H we present te construction and some properties of te column and row Hilbert

More information

1 (10) 2 (10) 3 (10) 4 (10) 5 (10) 6 (10) Total (60)

1 (10) 2 (10) 3 (10) 4 (10) 5 (10) 6 (10) Total (60) First Name: OSU Number: Last Name: Signature: OKLAHOMA STATE UNIVERSITY Department of Matematics MATH 2144 (Calculus I) Instructor: Dr. Matias Sculze MIDTERM 1 September 17, 2008 Duration: 50 minutes No

More information

3.4 Worksheet: Proof of the Chain Rule NAME

3.4 Worksheet: Proof of the Chain Rule NAME Mat 1170 3.4 Workseet: Proof of te Cain Rule NAME Te Cain Rule So far we are able to differentiate all types of functions. For example: polynomials, rational, root, and trigonometric functions. We are

More information

ERROR BOUNDS FOR FINITE-DIFFERENCE METHODS FOR RUDIN OSHER FATEMI IMAGE SMOOTHING

ERROR BOUNDS FOR FINITE-DIFFERENCE METHODS FOR RUDIN OSHER FATEMI IMAGE SMOOTHING ERROR BOUNDS FOR FINITE-DIFFERENCE METHODS FOR RUDIN OSHER FATEMI IMAGE SMOOTHING JINGYUE WANG AND BRADLEY J. LUCIER Abstract. We bound te difference between te solution to te continuous Rudin Oser Fatemi

More information

Blanca Bujanda, Juan Carlos Jorge NEW EFFICIENT TIME INTEGRATORS FOR NON-LINEAR PARABOLIC PROBLEMS

Blanca Bujanda, Juan Carlos Jorge NEW EFFICIENT TIME INTEGRATORS FOR NON-LINEAR PARABOLIC PROBLEMS Opuscula Matematica Vol. 26 No. 3 26 Blanca Bujanda, Juan Carlos Jorge NEW EFFICIENT TIME INTEGRATORS FOR NON-LINEAR PARABOLIC PROBLEMS Abstract. In tis work a new numerical metod is constructed for time-integrating

More information

Complexity of Decoding Positive-Rate Reed-Solomon Codes

Complexity of Decoding Positive-Rate Reed-Solomon Codes Complexity of Decoding Positive-Rate Reed-Solomon Codes Qi Ceng 1 and Daqing Wan 1 Scool of Computer Science Te University of Oklaoma Norman, OK73019 Email: qceng@cs.ou.edu Department of Matematics University

More information

Quasiperiodic phenomena in the Van der Pol - Mathieu equation

Quasiperiodic phenomena in the Van der Pol - Mathieu equation Quasiperiodic penomena in te Van der Pol - Matieu equation F. Veerman and F. Verulst Department of Matematics, Utrect University P.O. Box 80.010, 3508 TA Utrect Te Neterlands April 8, 009 Abstract Te Van

More information

REGULARITY OF SOLUTIONS OF PARTIAL NEUTRAL FUNCTIONAL DIFFERENTIAL EQUATIONS WITH UNBOUNDED DELAY

REGULARITY OF SOLUTIONS OF PARTIAL NEUTRAL FUNCTIONAL DIFFERENTIAL EQUATIONS WITH UNBOUNDED DELAY Proyecciones Vol. 21, N o 1, pp. 65-95, May 22. Universidad Católica del Norte Antofagasta - Cile REGULARITY OF SOLUTIONS OF PARTIAL NEUTRAL FUNCTIONAL DIFFERENTIAL EQUATIONS WITH UNBOUNDED DELAY EDUARDO

More information

Recent Progress in the Integration of Poisson Systems via the Mid Point Rule and Runge Kutta Algorithm

Recent Progress in the Integration of Poisson Systems via the Mid Point Rule and Runge Kutta Algorithm Recent Progress in te Integration of Poisson Systems via te Mid Point Rule and Runge Kutta Algoritm Klaus Bucner, Mircea Craioveanu and Mircea Puta Abstract Some recent progress in te integration of Poisson

More information

NON STANDARD FITTED FINITE DIFFERENCE METHOD FOR SINGULAR PERTURBATION PROBLEMS USING CUBIC SPLINE

NON STANDARD FITTED FINITE DIFFERENCE METHOD FOR SINGULAR PERTURBATION PROBLEMS USING CUBIC SPLINE Global and Stocastic Analysis Vol. 4 No. 1, January 2017, 1-10 NON STANDARD FITTED FINITE DIFFERENCE METHOD FOR SINGULAR PERTURBATION PROBLEMS USING CUBIC SPLINE K. PHANEENDRA AND E. SIVA PRASAD Abstract.

More information

Chapter 2 Limits and Continuity

Chapter 2 Limits and Continuity 4 Section. Capter Limits and Continuity Section. Rates of Cange and Limits (pp. 6) Quick Review.. f () ( ) () 4 0. f () 4( ) 4. f () sin sin 0 4. f (). 4 4 4 6. c c c 7. 8. c d d c d d c d c 9. 8 ( )(

More information

called the homomorphism induced by the inductive limit. One verifies that the diagram

called the homomorphism induced by the inductive limit. One verifies that the diagram Inductive limits of C -algebras 51 sequences {a n } suc tat a n, and a n 0. If A = A i for all i I, ten A i = C b (I,A) and i I A i = C 0 (I,A). i I 1.10 Inductive limits of C -algebras Definition 1.10.1

More information

WYSE Academic Challenge 2004 Sectional Mathematics Solution Set

WYSE Academic Challenge 2004 Sectional Mathematics Solution Set WYSE Academic Callenge 00 Sectional Matematics Solution Set. Answer: B. Since te equation can be written in te form x + y, we ave a major 5 semi-axis of lengt 5 and minor semi-axis of lengt. Tis means

More information

4.2 - Richardson Extrapolation

4.2 - Richardson Extrapolation . - Ricardson Extrapolation. Small-O Notation: Recall tat te big-o notation used to define te rate of convergence in Section.: Definition Let x n n converge to a number x. Suppose tat n n is a sequence

More information

The Laplace equation, cylindrically or spherically symmetric case

The Laplace equation, cylindrically or spherically symmetric case Numerisce Metoden II, 7 4, und Übungen, 7 5 Course Notes, Summer Term 7 Some material and exercises Te Laplace equation, cylindrically or sperically symmetric case Electric and gravitational potential,

More information

Stationary Gaussian Markov Processes As Limits of Stationary Autoregressive Time Series

Stationary Gaussian Markov Processes As Limits of Stationary Autoregressive Time Series Stationary Gaussian Markov Processes As Limits of Stationary Autoregressive Time Series Lawrence D. Brown, Pilip A. Ernst, Larry Sepp, and Robert Wolpert August 27, 2015 Abstract We consider te class,

More information