Strong StabilityPreserving HighOrder Time Discretization Methods


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1 SIAM REVIEW Vol. 43,No.,pp. 89 c 00 Society for Industrial and Applied Mathematics Strong StabilityPreserving HighOrder Time Discretization Methods Sigal Gottlieb ChiWang Shu Eitan Tadmor Abstract. In this paper we review and further develop a class of strong stabilitypreserving SSP highorder time discretizations for semidiscrete method of lines approximations of partial differential equations.previously termed TVD total variation diminishing time discretizations, these highorder time discretization methods preserve the strong stability properties of firstorder Euler time stepping and have proved very useful, especially in solving hyperbolic partial differential equations.the new developments in this paper include the construction of optimal explicit SSP linear Runge Kutta methods, their application to the strong stability of coercive approximations, a systematic study of explicit SSP multistep methods for nonlinear problems, and the study of the SSP property of implicit Runge Kutta and multistep methods. Key words. strong stability preserving, Runge Kutta methods, multistep methods, highorder accuracy, time discretization AMS subject classifications. 65M0, 65L06 PII. S X. Introduction. It is a common practice in solving timedependent partial differential equations PDEs to first discretize the spatial variables to obtain a semidiscrete method of lines scheme. This is then an ordinarydifferential equation ODE system in the time variable, which can be discretized by an ODE solver. A relevant question here concerns stability. For problems with smooth solutions, usually a linear stabilityanalysis is adequate. For problems with discontinuous solutions, however, such as solutions to hyperbolic problems, a stronger measure of stability is usually required. Received by the editors February, 000; accepted for publication in revised form August, 000; published electronically February, 00. Department of Mathematics, University of Massachusetts at Dartmouth, Dartmouth, MA 0747 and Division of Applied Mathematics, Brown University, Providence, RI 09 umassd.edu. The research of this author was supported by ARO grant DAAG and NSF grant ECS Division of Applied Mathematics, Brown University, Providence, RI 09 The research of this author was supported by ARO grants DAAG and DAAD , NSF grants DMS , ECS , and INT , NASA Langley grant NAG070 and contract NAS while this author was in residence at ICASE, NASA Langley Research Center, Hampton, VA , and by AFOSR grant F Department of Mathematics, University of California at Los Angeles, Los Angeles, CA The research of this author was supported by NSF grant DMS and ONR grant N0004J
2 90 SIGAL GOTTLIEB,CHIWANG SHU,AND EITAN TADMOR In this paper we review and further develop a class of highorder strong stabilitypreserving SSP time discretization methods for the semidiscrete method of lines approximations of PDEs. These time discretization methods were first developed in [0] and [9] and were called TVD total variation diminishing time discretizations. This class of methods was further developed in [6]. The idea is to assume that the firstorder forward Euler time discretization of the method of lines ODE is strongly stable under a certain norm when the time step t is suitablyrestricted, and then to tryto find a higher order time discretization Runge Kutta or multistep that maintains strong stabilityfor the same norm, perhaps under a different time step restriction. In [0] and [9], the relevant norm was the total variation norm: the forward Euler time discretization of the method of lines ODE was assumed to be TVD, hence the class of highorder time discretization developed there was termed TVD time discretization. This terminologywas also used in [6]. In fact, the essence of this class of highorder time discretizations lies in its abilityto maintain the strong stabilityin the same norm as the firstorder forward Euler version, hence SSP time discretization is a more suitable term, which we will use in this paper. We begin this paper bydiscussing explicit SSP methods. We first give, in section, a brief introduction to the setup and basic properties of the methods. We then move, in section 3, to our new results on optimal SSP Runge Kutta methods of arbitraryorder of accuracyfor linear ODEs suitable for solving PDEs with linear spatial discretizations. This is used to prove strong stabilityfor a class of wellposed problems u t = Lu, where the operator L is linear and coercive, improving and simplifying the proofs for the results in [3]. We review and further develop the results in [0], [9], and [6] for nonlinear SSP Runge Kutta methods in section 4 and for multistep methods in section 5. Section 6 of this paper contains our new results on implicit SSP schemes. It starts with a numerical example showing the necessity of preserving the strong stabilitypropertyof the method, then it moves on to the analysis of the rather disappointing negative results concerning the nonexistence of SSP implicit Runge Kutta or multistep methods of order higher than. Concluding remarks are given in section 7.. Explicit SSP Methods... Why SSP Methods? Explicit SSP methods were developed in [0] and [9] termed TVD time discretizations there to solve systems of ODEs. d dt u = Lu, resulting from a method of lines approximation of the hyperbolic conservation law,. u t = fu x, where the spatial derivative, fu x, is discretized bya TVD finite difference or finite element approximation; see, e.g., [8], [6], [], [], [9], and consult [] for a recent overview. Denoted by Lu, it is assumed that the spatial discretization has the propertythat when it is combined with the firstorder forward Euler time discretization,.3 u n+ = u n + tlu n, then, for a sufficientlysmall time step dictated bythe Courant Friedrichs LevyCFL condition,.4 t t FE,
3 STRONG STABILITYPRESERVING TIME DISCRETIZATION METHODS 9.5 u exact TVD.5 u exact nontvd x x Fig.. Secondorder TVD MUSCL spatial discretization. Solution after the shock moves 50 mesh points. Left: SSP time discretization;right: nonssp time discretization. the total variation TV of the onedimensional discrete solution u n := j u n j {xj x x j+ } does not increase in time; i.e., the following socalled TVD propertyholds:.5 TVu n+ TVu n, TVu n := j u n j+ u n j. The objective of the highorder SSP Runge Kutta or multistep time discretization is to maintain the strong stabilityproperty.5 while achieving higher order accuracyin time, perhaps with a modified CFL restriction measured here with a CFL coefficient, c.6 t c t FE. In [6] we gave numerical evidence to show that oscillations mayoccur when using a linearlystable, highorder method which lacks the strong stabilityproperty, even if the same spatial discretization is TVD when combined with the firstorder forward Euler time discretization. The example is illustrative, so we reproduce it here. We consider a scalar conservation law, the familiar Burgers equation.7 u t + u =0 with Riemann initial data.8 ux, 0 = x { if x 0, 0.5 if x>0. The spatial discretization is obtained bya second order MUSCL [], which is TVD for forward Euler time discretization under suitable CFL restriction. In Figure., we show the result of using an SSP secondorder Runge Kutta method for the time discretization left and that of using a nonssp secondorder Runge Kutta method right. We can clearlysee that the nonssp result is oscillatory there is an overshoot.
4 9 SIGAL GOTTLIEB,CHIWANG SHU,AND EITAN TADMOR This simple numerical example illustrates that it is safer to use an SSP time discretization for solving hyperbolic problems. After all, they do not increase the computational cost and have the extra assurance of provable stability. As we have alreadymentioned above, the highorder SSP methods discussed here are not restricted to preserving not increasing the TV. Our arguments below relyon convexity, hence these properties hold for any norm. Consequently, SSP methods have a wide range of applicability, as theycan be used to ensure stabilityin an arbitrary norm once the forward Euler time discretization is shown to be stronglystable, i.e., u n + tlu n u n. For linear examples we refer to [7], where weighted L SSP higher order discretizations of spectral schemes were discussed. For nonlinear scalar conservation laws in several space dimensions, the TVD propertyis ruled out for highresolution schemes; instead, strong stabilityin the maximum norm is sought. Applications of L SSP higher order discretization for discontinuous Galerkin and central schemes can be found in [3] and [9]. Finally, we note that since our arguments below are based on convex decompositions of highorder methods in terms of the firstorder Euler method, anyconvex function will be preserved bysuch highorder time discretizations. In this context we refer, for example, to the cell entropystability propertyof highorder schemes studied in [7] and [5]. We remark that the strong stabilityassumption for the forward Euler u n + tlu n u n can be relaxed to the more general stabilityassumption u n + tlu n +O t u n. This general stabilitypropertywill also be preserved by the highorder SSP time discretizations. The total variation bounded TVB methods discussed in [8] and [] belong to this category. However, if the forward Euler operator is not stable, the framework in this paper cannot be used to determine whether a highorder time discretization is stable or not... SSP Runge Kutta Methods. In [0], a general mstage Runge Kutta method for. is written in the form.9 u 0 = u n, i u i = α i,k u k + tβ i,k Lu k, α i,k 0, i =,...,m, k=0 u n+ = u m. Clearly, if all the β i,k s are nonnegative, β i,k 0; then since byconsistency i k=0 α i,k =, it follows that the intermediate stages in.9, u i, amount to convex combinations of forward Euler operators, with t replaced by β i,k α i,k t. We thus conclude with the following lemma. Lemma. see [0]. If the forward Euler method.3 is strongly stable under the CFL restriction.4, u n + tlu n u n, then the Runge Kutta method.9 with β i,k 0 is SSP, u n+ u n, provided the following CFL restriction.6 is fulfilled:.0 t c t FE, c = min i,k α i,k β i,k. By the notion of strong stability we refer to the fact that there is no temporal growth, as opposed to the general notion of stability, which allows a bounded temporal growth, u n Const u 0, with any arbitrary constant, possibly Const >.
5 STRONG STABILITYPRESERVING TIME DISCRETIZATION METHODS 93 If some of the β i,k s are negative, we need to introduce an associated operator L corresponding to stepping backward in time. The requirement for L is that it approximate the same spatial derivatives as L, but that the strong stabilityproperty hold with u n+ u n with respect to either the TV or another relevant norm for the firstorder Euler scheme solved backward in time, i.e.,. u n+ = u n t Lu n. This can be achieved for hyperbolic conservation laws by solving the negativeintime version of.,. u t = fu x. Numerically, the only difference is the change of upwind direction. Clearly, L can be computed with the same cost as that of computing L. We then have the following lemma. Lemma. see [0]. If the forward Euler method combined with the spatial discretization L in.3 is strongly stable under the CFL restriction.4, u n + tlu n u n, and if Euler s method solved backward in time in combination with the spatial discretization L in. is also strongly stable under the CFL restriction.4, u n t Lu n u n, then the Runge Kutta method.9 is SSP, u n+ u n, under the CFL restriction.6,.3 t c t FE, c = min i,k α i,k β i,k, provided β i,k L is replaced by β i,k L whenever βi,k is negative. Notice that if, for the same k, both Lu k and Lu k must be computed, the cost as well as the storage requirement for this k is doubled. For this reason, we would like to avoid negative β i,k as much as possible. However, as shown in [6] it is not always possible to avoid negative β i,k..3. SSP Multistep Methods. SSP multistep methods of the form.4 u n+ = αi u n+ i + tβ i Lu n+ i, α i 0, i= were studied in [9]. Since α i =, it follows that u n+ is given bya convex combination of forward Euler solvers with suitablyscaled t s, and hence, similar to our discussion for Runge Kutta methods, we arrive at the following lemma. Lemma.3 see [9]. If the forward Euler method combined with the spatial discretization L in.3 is strongly stable under the CFL restriction.4, u n + tlu n u n, and if Euler s method solved backward in time in combination with the spatial discretization L in. is also strongly stable under the CFL restriction.4, u n t Lu n u n, then the multistep method.4 is SSP, u n+ u n, under the CFL restriction.6,.5 t c t FE, c = min i α i β i, provided β i L is replaced by β i L whenever βi is negative.
6 94 SIGAL GOTTLIEB,CHIWANG SHU,AND EITAN TADMOR 3. Linear SSP Runge Kutta Methods of Arbitrary Order. 3.. SSP Runge Kutta Methods with Optimal CFL Condition. In this section we present a class of optimal in the sense of CFL number SSP Runge Kutta methods of anyorder for the ODE., where L is linear. With a linear L being realized as a finitedimensional matrix, we denote Lu = Lu. We will first show that the mstage, mthorder SSP Runge Kutta method can have, at most, CFL coefficient c = in.0. We then proceed to construct optimal SSP linear Runge Kutta methods. Proposition 3.. Consider the family of mstage, mthorder SSP Runge Kutta methods.9 with nonnegative coefficients α i,k and β i,k. The maximum CFL restriction attainable for such methods is the one dictated by the forward Euler scheme, t t FE ; i.e.,.6 holds with maximal CFL coefficient c =. Proof. We consider the special case where L is linear and prove that even in this special case the maximum CFL coefficient c attainable is. Any mstage method.9, for this linear case, can be rewritten as i u i = + A i,k tl k+ u 0, i =,...,m, where A i,k = k=0 i i A,0 = β,0, A i,0 = α i,k A k,0 + β i,k, i j=k+ k= k=0 i α i,j A j,k + β i,j A j,k, k =,...,i. j=k In particular, using induction, it is easyto show that the last two terms of the final stage can be expanded as A m,m = A m,m = m β l,l, l= β k,k k= m l=k+ k β l,l l= β l,l + α k,k k= m β l,l l=,l k For an mstage, mthorder linear Runge Kutta scheme, A m,k = k+!. Using A m,m = m l= β l,l = m!, we can rewrite α k,k m k A m,m = + β k,k β l,l β l,l. m!β k,k k= k= l=k+ With the nonnegative assumption on β i,k s and the fact A m,m = m l= β l,l = m! we have β l,l > 0 for all l. For the CFL coefficient c wemusthave α k,k β k,k for all k. Clearly, A m,m = m! is possible under these restrictions onlyif β k,k =0 and α k,k β k,k = for all k, in which case the CFL coefficient c. l=.
7 STRONG STABILITYPRESERVING TIME DISCRETIZATION METHODS 95 We remark that the conclusion of Proposition 3. is valid onlyif the mstage Runge Kutta method is mthorder accurate. In [9], we constructed an mstage, firstorder SSP Runge Kutta method with a CFL coefficient c = m which is suitable for steadystate calculations. The proof above also suggests a construction for the optimal linear mstage, mthorder SSP Runge Kutta methods. Proposition 3.. The class of mstage schemes given recursively by 3. u i = u i + tlu i, i =,...,m, u m = where α,0 =and m k=0 α m,k u k + α m,m u m + tlu m, 3. α m,k = k α m,k, k =,...,m, α m,m = m m!, α m,0 = k= α m,k is an mthorder linear Runge Kutta method which is SSP with CFL coefficient c =, t t FE. Proof. The firstorder case is forward Euler, which is firstorder accurate and SSP with CFL coefficient c = bydefinition. The other schemes will be SSP with a CFL coefficient c = byconstruction, as long as the coefficients are nonnegative. We now show that scheme is mthorder accurate when L is linear. In this case, clearly i u i =+ tl i u 0 i! = k!i k! tlk u 0, i =,...,m, k=0 hence scheme results in m j u m = j! m! α m,j k!j k! tlk + α m,m k!m k! tlk u 0. j=0 k=0 Clearly, by 3., the coefficient of tl m m! is α m,m m! = m!, the coefficient of tl m is α m,m = m!, and the coefficient of tl0 is m m! + α m,j =. j=0 It remains to show that, for k m, the coefficient of tl k is equal to k! : k=0 3.3 m k!m k! + j! α m,j k!j k! = k!. j=k
8 96 SIGAL GOTTLIEB,CHIWANG SHU,AND EITAN TADMOR This will be shown byinduction. Thus we assume 3.3 is true for m, and then for m + we have, for 0 k m, that the coefficient of tl k+ is equal to m k +!m k! + = k +! j=k+ α m+,j j! k +!j k! m m k! + l=k l=k l +! α m+,l+ l k! m = k +! m k! + l + α l +! m,l l k! = = k +! k +!, m m k! + l=k l! α m,l l k! where in the second equalitywe used 3. and in the last equalitywe used the induction hypothesis 3.3. This finishes the proof. Finally, we show that all the α s are nonnegative. Clearly α,0 = α, = > 0. If we assume α m,j 0 for all j =0,...,m, then α m+,j = j α m,j 0, j =,...,m ; α m+,m = and, bynoticing that α m+,j α m,j for all j =,...,m,wehave m +! 0, α m+,0 = j= α m+,j α m,j =0. j= As the mstage, mthorder linear Runge Kutta method is unique, we have in effect proved that this unique mstage, mthorder linear Runge Kutta method is SSP under CFL coefficient c =. If L is nonlinear, scheme is still SSP under CFL coefficient c =, but it is no longer mthorder accurate. Notice that all but the last stage of these methods are simple forward Euler steps. We note in passing the examples of the ubiquitous third and fourthorder Runge Kutta methods, which admit the following convex, and hence SSP, decompositions: k=0 4 k=0 k! tlk = 3 + I + tl+ 6 I + tl3, k! tlk = I + tl+ 4 I + tl + 4 I + tl4. We list, in Table 3., the coefficients α m,j of these optimal methods in 3. up to m =8.
9 STRONG STABILITYPRESERVING TIME DISCRETIZATION METHODS 97 Table 3. Coefficients α m,j of the SSP methods Order m α m,0 α m, α m, α m,3 α m,4 α m,5 α m,6 α m, Application to Coercive Approximations. We now applythe optimal linear SSP Runge Kutta methods to coercive approximations. We consider the linear system of ODEs of the general form, with possibly variable, timedependent coefficients, 3.6 d ut =Ltut. dt As an example we refer to [7], where the farfromnormal character of the spectral differentiation matrices defies the straightforward von Neumann stabilityanalysis when augmented with highorder time discretizations. We begin our stabilitystudyfor Runge Kutta approximations of 3.6 with the firstorder forward Euler scheme with, denoting the usual Euclidean inner product u n+ = u n + t n Lt n u n, based on variable time steps, t n := n j=0 t j. Taking L norms on both sides one finds u n+ = u n + t n Re Lt n u n,u n + t n Lt n u n, and hence strong stabilityholds, u n+ u n, provided the following restriction on the time step, t n, is met: t n Re Lt n u n,u n / Lt n u n. Following Levyand Tadmor [3] we therefore make the following assumption. Assumption 3. coercivity. The operator Lt is uniformly coercive in the sense that there exists ηt > 0 such that 3.7 u ηt := inf Re Ltu, u = Ltu > 0. We conclude that for coercive L s, the forward Euler scheme is stronglystable, I + t n Lt n, if and onlyif t n ηt n.
10 98 SIGAL GOTTLIEB,CHIWANG SHU,AND EITAN TADMOR In a generic case, Lt n represents a spatial operator with a coercivitybound ηt n, which is proportional to some power of the smallest spatial scale. In this context the above restriction on the time step amounts to the celebrated CFL stabilitycondition. Our aim is to show that the general mstage, mthorder accurate Runge Kutta scheme is stronglystable under the same CFL condition. Remark. Observe that the coercivityconstant, η, is an upper bound in the size of L; indeed, bycauchy Schwartz, ηt Ltu u / Ltu and hence 3.8 Lt = sup u Ltu u ηt. To make one point, we consider the fourthorder Runge Kutta approximation of 3.6: k = Lt n u n, k = Lt n+ u n + t n k, k 3 = Lt n+ u n + t n k, k 4 = Lt n+ u n + t n k 3, u n+ = u n + t [ n k +k +k 3 + k 4]. 6 Starting with secondorder and higher, the Runge Kutta intermediate steps depend on the time variation of L, and hence we require a minimal smoothness in time, making the following assumption. Assumption 3. Lipschitz regularity. We assume that L is Lipschitz. Thus, there exists a constant K>0 such that 3.4 Lt Ls K t s. ηt We are now readyto make our main result, stating the following proposition. Proposition 3.3. Consider the coercive systems of ODEs , with Lipschitz continuous coefficients 3.4. Then the fourthorder Runge Kutta scheme is stable under CFL condition 3.5 t n ηt n, and the following estimate holds: 3.6 u n e 3Ktn u 0. Remark. The result along these lines was introduced bylevyand Tadmor [3, Main Theorem], stating the strong stabilityof the constant coefficients sorder Runge Kutta scheme under CFL condition t n C s ηt n. Here we improve in both simplicityand generality. Thus, for example, the previous bound of C 4 =/3 [3, Theorem 3.3] is now improved to a practical timestep restriction with our uniform C s =.
11 STRONG STABILITYPRESERVING TIME DISCRETIZATION METHODS 99 Proof. We proceed in two steps. We first freeze the coefficients at t = t n, considering here we abbreviate L n = Lt n j = L n u n, j = L n u n + t n j L n I + t n j 3 = L n u n + t n j Ln L n [ I + t n Ln j 4 = L n u n + t n j 3, v n+ = u n + t [ n j +j +j 3 + j 4]. 6 u n, I + t ] n Ln u n, Thus, v n+ = P 4 t n L n u n, where following 3.5, P 4 t n L n := 3 8 I + 3 I + tl+ 4 I + tl + 4 I + tl4. Since the CFL condition 3.5 implies the strong stabilityof forward Euler, i.e., I + t n L n, it follows that P 4 t n L n 3/8+/3+/4+/4 =. Thus, 3. v n+ u n. Next, we include the time dependence. We need to measure the difference between the exact and the frozen intermediate values the k s and the j s. We have k j =0, [ ] k j = Lt n+ Lt n I + t n Ln u n, k 3 j 3 = Lt n+ t [ ] n k j + Lt n+ Lt n tn j, k 4 j 4 = Lt n+ t n k 3 j 3 + [ Lt n+ Lt n ] t n j 3. Lipschitz continuity3.4 and the strong stabilityof forward Euler imply 3.7 k j K t n ηt n un K u n. Also, since L n ηt n, we find from 3.8 that j u n /ηt n, and hence 3.5 followed by3.7 and the CFL condition 3.5 imply 3.8 k 3 j 3 t n ηt n k j + K t n ηt n t n ηt n un tn K ηt n u n K u n.
12 00 SIGAL GOTTLIEB,CHIWANG SHU,AND EITAN TADMOR Finally, since by 3.9 j 3 does not exceed j 3 < ηt n followed by3.8 and the CFL condition 3.5, 3.9 k 4 j 4 t n ηt n k3 j 3 + K t n ηt n K We conclude that u n+, t n ηt n tn ηt n un, we find from + t n ηt n 3 tn tn ηt n + ηt n u n K u n. u n+ = v n+ + t n 6 is upper bounded byconsult 3., u n+ v n+ + t n 6 u n [ ] k j +k 3 j 3 +k 4 j 4, [ K u n +4K u n +K u n +3K t n u n and the result 3.6 follows. 4. Nonlinear SSP Runge Kutta Methods. In the previous section we derived SSP Runge Kutta methods for linear spatial discretizations. As explained in the introduction, SSP methods are often required for nonlinear spatial discretizations. Thus, most of the research to date has been in the derivation of SSP methods for nonlinear spatial discretizations. In [0], schemes up to third order were found to satisfythe conditions in Lemma. with CFL coefficient c =. In [6] it was shown that all fourstage, fourthorder Runge Kutta methods with positive CFL coefficient c in.3 must have at least one negative β i,k, and a method which seems optimal was found. For largescale scientific computing in three space dimensions, storage is usuallya paramount consideration. We review the results presented in [6] about SSP properties among such lowstorage Runge Kutta methods. 4.. Nonlinear Methods of Second, Third, and Fourth Order. Here we review the optimal in the sense of CFL coefficient and the cost incurred by L if it appears SSP Runge Kutta methods of mstage, mthorder for m =, 3, 4, written in the form.9. Proposition 4. see [6]. If we require β i,k 0, then an optimal secondorder SSP Runge Kutta method.9 is given by 4. u = u n + tlu n, u n+ = un + u + tlu, with a CFL coefficient c =in.0. An optimal thirdorder SSP Runge Kutta method.9 is given by u = u n + tlu n, ] 4. u = 3 4 un + 4 u + 4 tlu, with a CFL coefficient c =in.0. u n+ = 3 un + 3 u + 3 tlu,
13 STRONG STABILITYPRESERVING TIME DISCRETIZATION METHODS 0 In the fourthorder case we proved in [6] that we cannot avoid the appearance of negative β i,k, as demonstrated in the following proposition. Proposition 4. see [6]. The fourstage, fourthorder SSP Runge Kutta scheme.9 with a nonzero CFL coefficient c in.3 must have at least one negative β i,k. We thus must settle for finding an efficient fourthorder scheme containing L, which maximizes the operation cost measured by c 4+i, where c is the CFL coefficient.3 and i is the number of L s. This waywe are looking for an SSP method that reaches a fixed time T with a minimal number of evaluations of L or L. The best method we could find in [6] is 4.3 u = u n + tlun, u = u t Lu n u tlu, u 3 = un t Lu n u t Lu u tlu, u n+ = 5 un + 0 tlun u + 6 tlu u + 3 u3 + 6 tlu3, with a CFL coefficient c =0.936 in.3. Notice that two L s must be computed. The effective CFL coefficient, compared with an ideal case without L s, is =0.64. Since it is difficult to solve the global optimization problem, we do not claim that 4.3 is an optimal four stage, fourthorder SSP Runge Kutta method. 4.. Low Storage Methods. Storage is usuallyan important consideration for large scale scientific computing in three space dimensions. Therefore, lowstorage Runge Kutta methods [3], [], which onlyrequire two storage units per ODE variable, maybe desirable. Here we review the results presented in [6] concerning SSP properties among such lowstorage Runge Kutta methods. The general lowstorage Runge Kutta schemes can be written in the form [3], [] u 0 = u n, du 0 =0, 4.4 du i = A i du i + tlu i, i =,...,m, u i = u i + B i du i, i =,...,m, B = c, u n+ = u m. Only u and du must be stored, resulting in two storage units for each variable. Following Carpenter and Kennedy[], the best SSP thirdorder method found by numerical search in [6] is given bythe system z = 36c 4 +36c 3 35c +84c, z =c + c, z 3 =c 4 8c 3 +8c c +, z 4 =36c 4 36c 3 +3c 8c +4, z 5 =69c 3 6c +8c 8, z 6 =34c 4 46c 3 +34c 3c +,
14 0 SIGAL GOTTLIEB,CHIWANG SHU,AND EITAN TADMOR B = cc 3z z 3z z 44c3c c, B 3 = A = 43c c 3z z cc 3z z, z 6c 4c ++3z 3 c +z 3c + c, A 3 = z z c c 5 3c z 5 4z cc 4 +7cz 6 +7c 6 c 3, with c = , resulting in a CFL coefficient c =0.3 in.6. This is of course less optimal than 4. in terms of CFL coefficient, but the lowstorage form is useful for largescale calculations. Carpenter and Kennedy[] have also given classes of fivestage, fourthorder lowstorage Runge Kutta methods. We have been unable to find SSP methods in that class with positive α i,k and β i,k. A lowstorage method with negative β i,k cannot be made SSP, as L cannot be used without destroying the lowstorage property Hybrid Multistep Runge Kutta Methods. Hybrid multistep Runge Kutta methods e.g., [0], [4] are methods that combine the properties of Runge Kutta and multistep methods. We explore the twostep, twostage method 4.5 u n+ = α u n + α 0 u n + t β 0 Lu n +β Lu n, α k 0, 4.6 u n+ = α 30 u n + α 3 u n+ + α3 u n + t β 30 Lu n +β 3 Lu n+ +β3 Lu n, α 3k 0. Clearly, this method is SSP under the CFL coefficient.0 if β i,k 0. We could also consider the case allowing negative β i,k s, using instead.3 for the CFL coefficient and replacing β i,k L by β i,k L for the negative βi,k s. For thirdorder accuracy, we have a threeparameter family depending on c, α 30, and α 3 : α 0 =3c +c 3, β 0 = c + c 3, α = 3c c 3, β = c +c + c 3, 4.7 β 30 = +α 30 3c +3α 30 c + α 3 c 3, 6 + c β 3 = 5 α 30 3α 3 c α 3 c 3 6c +6c, α 3 = α 3 α 30, β 3 = 5+α 30 +9c +3α 30 c 3α 3 c α 3 c 3. 6c
15 STRONG STABILITYPRESERVING TIME DISCRETIZATION METHODS 03 The best method we were able to find is given by c =0.4043, α 30 =0.0605, and α 3 =0.635 and has a CFL coefficient c Clearly, this is not as good as the optimal thirdorder Runge Kutta method 4. with CFL coefficient c =. We hoped that a fourthorder scheme with a large CFL coefficient could be found, but unfortunatelythis is not the case, as is proven in the following proposition. Proposition 4.3. There are no fourthorder schemes 4.5 with all nonnegative α i,k. Proof. The fourthorder schemes are given bya twoparameter familydepending on c, α 30 and setting α 3 in 4.7 to be α 3 = 7 α 30 +0c α 30 c c 3+8c +4c. The requirement α 0 enforces see 4.7 c. The further requirement α 0 0 yields 3 c. α 3 has a positive denominator and a negative numerator for <c<, and its denominator is 0 when c = or c = 3, thus we require 3 c<. In this range, the denominator of α 3 is negative, hence we also require its numerator to be negative, which translates to α c +c. Finally, we would require α 3 = α 3 α 30 0, which translates to α 30 c c+c+3+7 0c c+c c+. The two restrictions on α 30 give us the following inequality: 7+0c +c c c + c c c + c c +, which, in the range of 3 c<, yields c a contradiction. 5. Linear and Nonlinear Multistep Methods. In this section we review and further studyssp explicit multistep methods.4, which were first developed in [9]. These methods are rthorder accurate if 5. α i =, i= m i k α i = k i k β i, k =,...,r. i= i= We first prove a proposition that sets the minimum number of steps in our search for SSP multistep methods. Proposition 5.. For m, there is no mstep, mthorder SSP method with all nonnegative β i, and there is no mstep SSP method of order m +. Proof. Bythe accuracycondition 5., we clearlyhave for an rthorder accurate method 5. piα i = p iβ i i= i= for anypolynomial px of degree at most r satisfying p0 = 0. When r = m, we could choose px =xm x m.
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