Partial Differential Equations - Modelling and ... - ResearchGate
Partial Differential Equations - Modelling and ... - ResearchGate Partial Differential Equations - Modelling and ... - ResearchGate
88 J.Ch. Gilbert and P. Joly 4.2 Numerical Results Computing α m,k Table 1 shows the computed values of α m,k for 1 ≤ m ≤ 8and0≤ k ≤ 8. The computed solutions were always satisfying the optimality conditions, so that we are pretty confident in the values of α m,k in the table. In particular, the small ε v > 0 hardly modifies these values. The column k = 0 of Table 1 corresponds to the polynomials Q m defined by (11), for which the first values of the α m,0 ’s were already given in (13) (there denoted α m ). We observe that the convergence of α 2m+1,0 (resp. α 2m,0 )to π 2 ≃ 9.87 (resp. 4π 2 ≃ 39.48), predicted by Theorem 3, is rather fast. On the other hand, we observe that the values α m,k can be made spectacularly larger than α m,0 , which was our objective. We have verified that the optimal polynomials corresponding to m =1 are, indeed, related to the Chebyshev polynomials through formula (29), as claimed by Corollary 3. This fact can be observed in the first row of the table, whose values of α 1,k are, indeed, those given by (28). Another observation is that the oscillating behaviour of α m with m, highlighted in the analysis leading to Theorem 3, is recovered in the sequences {α m,k } m≥1 . The reason is similar. The first positive stationary point of the optimal polynomial, which is close to the one of Q ∞ , is (resp. is not) a tangent point when m is odd (resp. even). This observation leads to the following conjecture: if we denote by τ m,k,j the jth tangent point of the optimal polynomial ψ m (R m,k )(1≤ j ≤ k), then, when m goes to infinity, τ 2m+1,k,j (resp. τ 2m,k,j ) converges the jth (resp. (j+1)th) positive stationary point of Q ∞ , the polynomial defined by (14). More specifically, τ 2m+1,k,j → j 2 π 2 and τ 2m,k,j → (j+1) 2 π 2 , when m →∞. (31) In practice, these values can be used to choose a good starting point for the algorithm when m is large. Table 1. Computed values of the first α m,k ’s k =0 k =1 k =2 k =3 k =4 k =5 k =6 k =7 k =8 m = 1 4.00 16.00 36.00 64.00 100.00 144.00 196.00 256.00 324.00 m = 2 12.00 32.43 60.56 96.61 140.64 192.66 252.67 320.68 396.69 m = 3 7.57 23.40 45.72 75.06 111.58 155.38 206.51 265.04 331.00 m = 4 21.48 44.03 73.45 110.01 153.83 204.98 263.51 329.49 402.92 m = 5 9.53 31.61 58.23 90.77 129.90 175.84 228.71 288.59 355.23 m = 6 30.72 57.23 89.78 128.89 174.84 227.71 287.61 354.59 428.71 m = 7 9.85 37.37 68.93 108.35 151.08 199.56 255.61 317.90 357.95 m = 8 37.08 70.89 107.67 150.35 199.32 254.89 317.22 386.35 462.27
Optimal Higher Order Time Discretizations 89 Diagonal schemes k = m We have found interesting to have a particular look at the case k = m. First it gives a computational effort per time step that is twice the one for the original (2m)th order scheme, which corresponds to k = 0. The second reason is more related to intuition: if one wants to get α m,k roughly proportional to m 2 , we have to control the first m maxima or minima of the optimal polynomial ψ m (R m,k ), for which we think that we need m parameters, which corresponds to k = m. Below, we qualify such a scheme as diagonal. Figure 4 shows the optimal polynomials ψ m (R m,m ), for m = 1,...,8. The tangent points are quoted by circles on the graphs, while the α m,m ’s are quoted by dots. Table 2 investigates the asymptotic behaviour of the diagonal schemes: 1. Its first column highlights the growth of the ratio between the maximum time step allowed by the stability analysis in a diagonal scheme ∆t m,m and in the second order scheme ∆t 1,0 . According to Section 2.2, there holds ( ) 1/2 ∆t m,m αm,m = = α1/2 m,m . (32) ∆t 1,0 α 1,0 2 2. The computational cost C m,m (T ) of the diagonal scheme of order 2m on an integration time T is proportional to the computational cost Cm,m 1 of one time step multiplied by the number of time steps. Hence, assuming that the largest time step allowed by the stability analysis is taken, one has C m,m (T ) ≃ C1 m,mT . ∆t m,m A similar expression holds for the computational cost C 1,0 (T ) of the second order scheme, with Cm,m 1 and ∆t m,m replaced by C1,0 1 and ∆t 1,0 , respectively. The second column of Table 2 gives the ratio of these two costs. Using (32) and the fact that Cm,m 1 ≃ 2mC1,0 1 (each time step of the diagonal scheme requires 2m times more operator multiplications than each time step of the second order scheme), the ratio can be estimated by C m,m (T ) C 1,0 (T ) ≃ 4m α 1/2 m,m The numbers in the second column of Table 2 suggest that this ratio is bounded. If the conjecture (33) below is correct, it should converge to 4 √ 2/π ≃ 1.80, when m goes to infinity. 3. Taking k = m and j = ⌈m/2⌉ in (31), and assuming that α m,m ∼ 2τ m,m,⌈m/2⌉ (suggested by the approximate symmetry of the optimal polynomials) lead us to the following conjecture: . α m,m m 2 → π2 , when m →∞. (33) 2
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88 J.Ch. Gilbert <strong>and</strong> P. Joly<br />
4.2 Numerical Results<br />
Computing α m,k<br />
Table 1 shows the computed values of α m,k for 1 ≤ m ≤ 8<strong>and</strong>0≤ k ≤ 8.<br />
The computed solutions were always satisfying the optimality conditions, so<br />
that we are pretty confident in the values of α m,k in the table. In particular,<br />
the small ε v > 0 hardly modifies these values.<br />
The column k = 0 of Table 1 corresponds to the polynomials Q m defined by<br />
(11), for which the first values of the α m,0 ’s were already given in (13) (there<br />
denoted α m ). We observe that the convergence of α 2m+1,0 (resp. α 2m,0 )to<br />
π 2 ≃ 9.87 (resp. 4π 2 ≃ 39.48), predicted by Theorem 3, is rather fast. On the<br />
other h<strong>and</strong>, we observe that the values α m,k can be made spectacularly larger<br />
than α m,0 , which was our objective.<br />
We have verified that the optimal polynomials corresponding to m =1<br />
are, indeed, related to the Chebyshev polynomials through formula (29), as<br />
claimed by Corollary 3. This fact can be observed in the first row of the table,<br />
whose values of α 1,k are, indeed, those given by (28).<br />
Another observation is that the oscillating behaviour of α m with m, highlighted<br />
in the analysis leading to Theorem 3, is recovered in the sequences<br />
{α m,k } m≥1 . The reason is similar. The first positive stationary point of the<br />
optimal polynomial, which is close to the one of Q ∞ , is (resp. is not) a tangent<br />
point when m is odd (resp. even). This observation leads to the following<br />
conjecture: if we denote by τ m,k,j the jth tangent point of the optimal polynomial<br />
ψ m (R m,k )(1≤ j ≤ k), then, when m goes to infinity, τ 2m+1,k,j (resp.<br />
τ 2m,k,j ) converges the jth (resp. (j+1)th) positive stationary point of Q ∞ ,<br />
the polynomial defined by (14). More specifically,<br />
τ 2m+1,k,j → j 2 π 2 <strong>and</strong> τ 2m,k,j → (j+1) 2 π 2 , when m →∞. (31)<br />
In practice, these values can be used to choose a good starting point for the<br />
algorithm when m is large.<br />
Table 1. Computed values of the first α m,k ’s<br />
k =0 k =1 k =2 k =3 k =4 k =5 k =6 k =7 k =8<br />
m = 1 4.00 16.00 36.00 64.00 100.00 144.00 196.00 256.00 324.00<br />
m = 2 12.00 32.43 60.56 96.61 140.64 192.66 252.67 320.68 396.69<br />
m = 3 7.57 23.40 45.72 75.06 111.58 155.38 206.51 265.04 331.00<br />
m = 4 21.48 44.03 73.45 110.01 153.83 204.98 263.51 329.49 402.92<br />
m = 5 9.53 31.61 58.23 90.77 129.90 175.84 228.71 288.59 355.23<br />
m = 6 30.72 57.23 89.78 128.89 174.84 227.71 287.61 354.59 428.71<br />
m = 7 9.85 37.37 68.93 108.35 151.08 199.56 255.61 317.90 357.95<br />
m = 8 37.08 70.89 107.67 150.35 199.32 254.89 317.22 386.35 462.27