hyperplane constrained continuation methods for coupled ... · 2 iterative method for one-component...
TRANSCRIPT
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Hyperplane Constrained Continuation Methods
for Coupled Nonlinear Schrodinger Equations
Weichung Wang
Department of Mathematics
National Taiwan University
Workshop on Bose-Einstein Condensation: Modeling, Analysis, Computation and Applications
Institute of Mathematical Sciences, National University of Singapore
November 15, 2007
W. Wang (NTU) HCCM for NLSE Workshop on BEC 1 / 77
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Cooperators
Yuen-Cheng Kuo
Department of Applied Mathematics, National University of Kaohsiung
Wen-Wei Lin
Department of Mathematics, National Tsing-Hua University
Shih-Feng Shieh
Department of Mathematics, National Taiwan Normal University
W. Wang (NTU) HCCM for NLSE Workshop on BEC 2 / 77
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Outline
1 Introduction
2 Iterative method for one-component DNLSE
3 Stable continuation method for DNLSE
4 Analysis of 3-component DNLSE
5 Numerical Experiments
6 Rotating Bose-Einstein Condensates
7 Conclusions
W. Wang (NTU) HCCM for NLSE Workshop on BEC 3 / 77
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Outline
System of m-coupled nonlinear Schrodinger equations
Nonlinear optics for Kerr-like photorefractive media
Main numerical issue: ε-solutions
A hyperplane-constrained continuation method
Theoretical analysis and numerical experiments
Nonlinear Schrodinger equation
Single component rotating Bose-Einstein condensate
Main numerical issue: transformation invariant solutions
Another hyperplane-constrained continuation method
Bistability of solution curves
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The m-coupled Nonlinear Schrodinger equations
The time-independent m-coupled NLSE:
∆φj − λjφj + µj|φj|2φj +∑
i6=j βij |φi|2φj = 0,
φj > 0 in Rn, j = 1, . . . , m,
φj(x)→ 0, as |x| → ∞,
(1)
where λj , µj > 0, n ≤ 3, and βij = βji for i 6= j are coupling
constants.
2-component example:
∆φ1 − λ1φ1 + µ1|φ1|2φ1 + β21|φ2|2φ1 = 0
∆φ2 − λ2φ2 + µ2|φ2|2φ2 + β12|φ1|2φ2 = 0
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Physical Model
∆φj − λjφj + µj|φj|2φj +
∑
i6=j
βij|φi|2φj = 0
Nonlinear optics (Kerr-like photorefractive media)
φj: the j-th component of the beam
λj: chemical potential
µj: self-focusing in the j-th component of the beam
βij: interaction between the beams
βij > 0, the interaction between φi and φj is attractive
βij < 0, the interaction between φi and φj is repulsive
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Ground State Solution for m = 1
The weak solution of the decoupled NLSE can be obtained by solving
infφ ≥ 0
φ ∈ H1(Rn)
∫Rn |∇φ|2 + λ
∫Rn φ2
(∫Rn φ4
)1/2(2)
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Ground State Solution for m = 1
or equivalently
infφ∈N1
E(φ) (3a)
where the energy functional on the Nehari manifold are
E(φ) =1
2
∫
Rn
|∇φ|2 +λ
2
∫
Rn
φ2 −µ
4
∫
Rn
φ4 (3b)
and
N1 =
φ ∈ H1(Rn)| φ ≥ 0, φ ≡/ 0,
∫
Rn
|∇φ|2 + λ
∫
Rn
φ2 = µ
∫
Rn
φ4
, (3c)
respectively.
If φ satisfies (3) then φ is called a ground state solution.W. Wang (NTU) HCCM for NLSE Workshop on BEC 8 / 77
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Ground State Solution for m ≥ 2
Consider the minimization problem
infφ∈Nm
E(φ), (4a)
where
E(φ) =m∑
j=1
(1
2
∫
Rn
|∇φj |2 +
λj
2
∫
Rn
φ2j −
µj
4
∫
Rn
φ4j
)−
1
4
m∑
i6=j
βij
∫
Rn
φ2i φ2
j , (4b)
and
Nm =φ = (φ1, φ2, . . . , φm) ∈ (H1(Rn))m| φj ≥ 0, φj ≡/ 0,
∫
Rn
|∇φj |2 + λj
∫
Rn
φ2j = µj
∫
Rn
φ4j +
∑
i6=j
βij
∫
Rn
φ2i φ2
j , j = 1, . . . , m
(4c)
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Ground State Solution for m ≥ 2
Definition of the ground state solution:
If φ = (φ1, . . . , φm) satisfies the following properties
1 φj > 0 for all j and φ satisfies NLSE.
2 E(φ) ≤ E(ψ) for any other solution ψ of NLSE.
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Discrete m-coupled Nonlinear Schrodinger Eqs
We consider the m-coupled DNLSE:
Auj − λjuj + µju
©2j uj +
∑mi6=j βiju
©2i uj = 0,
uj > 0 for j = 1, . . . , m,(5)
where λj > 0, µj > 0 and βij = βji, are coupling constants.
A ∈ RN×N corresponding to the operator ∆, uj ∈ R
N is
defined by the a approximation of φj(x) for j = 1, . . . ,m.
For u = (u1, . . . , uN )⊤, v = (v1, . . . , vN )⊤ ∈ RN ,
u v = (u1v1, . . . , uNvN )⊤ is the Hadamard product of u & v.
u©r = u · · · u denotes the r-time Hadamard product of u.
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The Discrete Minimization Problem for m = 1
One-component DNLSEAu− λu+ µu©2 u = 0,
u > 0,(6)
where λ, µ > 0 and A is diagonal dominant with positive
off-diagonal entries.
The minimization problem:
infu≥0
E(u), (7a)
where
E(u) =−u⊤Au+ λu⊤u
(u©2 ⊤u©2 )1/2. (7b)
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The Discrete Minimization Problem for m ≥ 2
The corresponding discrete minimization problem:
infx∈Nm
E(x). (8a)
where
E(x) =m∑
j=1
(−
1
2u⊤
j Auj +λj
2u⊤
j uj −µj
4u©2 ⊤j u
©2j
)−
1
4
m∑
i6=j,i=1
βiju©2 ⊤i u
©2j , (8b)
and
Nm =(u⊤
1 , . . . , u⊤m)⊤ ∈ R
Nm| uj ≥ 0, x ≡/ 0 and
−u⊤j Auj + λju⊤
j uj = µju©2 ⊤j u
©2j +
m∑
i6=j,i=1
βiju©2 ⊤i u
©2j , j = 1, . . . , m
.
(8c)
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Outline
1 Introduction
2 Iterative method for one-component DNLSE
3 Stable continuation method for DNLSE
4 Analysis of 3-component DNLSE
5 Numerical Experiments
6 Rotating Bose-Einstein Condensates
7 Conclusions
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Iterative Method for One-Component DNLSE I
Recall that the one-component DNLSE is
Au− λu+ µu©2 u = 0,
u > 0,(9)
Some notations and facts
Let
A = λI −A. (10)
Since λ > 0, then A is an irreducible M-matrix and A−1
is
positive definite matrix with positive entries.
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Iterative Method for One-Component DNLSE II
Define the set
M =u ∈ R
N | ‖u‖4 = 1,u ≥ 0
, (11)
where ‖u‖4 = (u©2 ⊤u©2 )1/4.
If u ∈M then A−1u = (λI −A)−1u > 0.
Define a mapping f :M→M by
f(u) =A
−1u©3
‖A−1u©3 ‖4
. (12)
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The Fixed Point Iterations
Algorithm 2.1 Fixed Point Iteration.
(i) Given A ∈ RN×N and u0 > 0 with ‖u0‖4 = 1, let i = 0;
(ii) Solve the linear system
Aui+1 = u©3i .
Compute ui+1 = ui+1/‖ui+1‖4.
(iii) If converges, then u∗ ← ui+1, stop; else i← i + 1, go to (ii).
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Convergence Analysis of the Fixed Point Iterations
Existence of fixed point and the resulting solution of the
one-component DNLSE
Globally convergent subsequence
Globally convergent sequence derived from a mild assumption
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Fixed Point and the One-Comp DNLSE Solution
Theorem
The function f :M→M given in (12) has a fixed point u∗ in
M.
Furthermore, the point
u(µ) =1
µ1/2‖A
−1u∗©3 ‖−1/2
4 u∗ ∈ N1, (13)
is the solution of Au− λu+ µu©2 u = 0.
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Globally Convergent Subsequence I
Theorem
(i) If u ∈M and v = f (u), then E(v) ≤ E(u), where E(·) is
defined as E(u) = −u⊤Au+λu⊤u
(u©2 ⊤u©2 )1/2 . The equality holds if and only if u
is a fixed point of f :M→M, i.e., f(u) = u.
(ii) For a sequence ui∞i=0 generated by the Fixed Point Algorithm,
there exists a subsequence uni∞i=0 such that
limi→∞
uni= u∗. (14)
Furthermore, u∗ is a fixed point of the function f (u) = A−1
u©3
‖A−1
u©3 ‖4.
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Globally Convergent Subsequence II
Corollary
If the minimization problem (7) has a unique global minimizer
u∗ ∈M, then there exist a neighborhood Ru∗ of u∗ such that the
fixed point iteration converges to u∗ for any initial vector u0 ∈ Ru∗ .
infu≥0
E(u), (15a)
where
E(u) =−u⊤Au+ λu⊤u
(u©2 ⊤u©2 )1/2. (15b)
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Globally Convergent Sequence
Theorem
If u∗ given in (14) is strictly local minimum of (7), then the
sequence ui converges to u∗ ∈M.
RemarkNumerical experience shows that for any arbitrary initial positive
vector u0 with ‖u0‖4 = 1, the fixed point iteration converges to the
global minimizer of (7).
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Outline
1 Introduction
2 Iterative method for one-component DNLSE
3 Stable continuation method for DNLSE
4 Analysis of 3-component DNLSE
5 Numerical Experiments
6 Rotating Bose-Einstein Condensates
7 Conclusions
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Continuation Method for the DNLSE
Recall that the m-coupled DNLSE isAuj − λjuj + µju
©2j uj +
∑mi6=j βiju
©2i uj = 0,
uj > 0 for j = 1, . . . , m,
where λj > 0, µj > 0 and βij = βji, i 6= j.
Let βij = βδij (β: continuation parameter) and rewrite as
G(x, β) = (G1, . . . ,Gm)(x, β) = 0, (16)
where x = (u⊤1 , . . . ,u⊤
m)⊤ ∈ RNm, G : RNm × R→ RNm and
Gj(x, β) = Auj − λjuj + µju©2j uj + β
m∑
i6=j
δiju©2i uj , j = 1, . . . , m.
(17)
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Standard Continuation Method for the DNLSE
The solution curve C of (16):
C =y(s) = (x(s)⊤, β(s))⊤| G(y(s)) = 0, s ∈ R
. (18)
Assume s is a parametrization via arc length is available on C.
By differentiating with s we have
DG(y(s))y(s) ≡ [Gx,Gβ]y(s) = 0,
where y(s) = (x(s)⊤, β(s))⊤ is a tangent vector to C at y(s).
yi
yi
Cyi+1
yi+1,1
x
β
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Solution Set of DNLSE for n = 1
We consider 2-coupled NLSE with n = 1
φ
′′
1 − λ1φ1 + µ1φ31 + β12φ2
2φ1 = 0,
φ′′
2 − λ2φ2 + µ2φ32 + β12φ2
1φ2 = 0.
By differentiating with x we have
[L1 2β12φ1φ2
2β12φ1φ2 L2
] [φ
′
1
φ′
2
]= 0, (19)
where L1 = d2
dx2 − λ1 + 3µ1φ21 + β12φ
22 and
L2 = d2
dx2 − λ2 + 3µ2φ22 + β12φ
21. From (19) we see that the
matrix[
L1 2β12φ1φ2
2β12φ1φ2 L2
]is singular. It easily seen that the
solution set of (1) is one dimensional.
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1-D
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Numerical Difficulty
yi
yi
C
yi+1
yi+1,1
x
β
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Numerical Difficulty
yi
ax
yi
C
yi+1
yi+1,1
x
β
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Solution Set of DNLSE for n = 2
We consider the m-coupled DNLSE with domain
[−d, d]× [−d, d]. The numerical null space of Gx(x(s), β(s)) is
spanned by
K0 = spanax,ay, (20)
where ax = Dxx(s), ay = Dyx(s) and
Dx = diagDx, . . . , Dx,Dy = diagDy, . . . , Dy ∈ RNm×Nm,
Dx, Dy ∈ RN×N are discretization matrices of the differential
operators ∂∂x
, ∂∂y
, resp..
Let xr be translation by x-axis or y-axis from x with
‖G(xr, β)‖ < ε. Then these solutions are called “ε-solutions”.
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A Comparison
NLSE
Unbounded domain
Solutions are translation invariant
DNLSE
Computational bounded domain
No translation invariant solutions
The ε-solutions (with small residual) exist
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Numerical Challenges Due to the ε-solutions
1. Cannot compute a unique prediction direction
2. Newton’s correction becomes inaccurate and inefficient
(the Jacobian matrix Gx is nearly singular)
3. Detections of bifurcation points are difficult
4. Cannot follow the desired solution curve efficiently
(Computed solutions may be random or trapped in the
multi-dimensional ε-solution set)
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Main Idea
yi
yi
C
yi+1
yi+1,1
x
β
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Main Idea
ax
C
yi+1
yi+1,1
x
β
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Solution Curve
G(x, β) = 0,
a⊤xx = 0,
a⊤y x = 0,
x⊤i x+ βiβ = 0.
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Prediction
Let yi = (x⊤i , pi)
⊤ ∈ RM+1 be an approx. point for C. Supposeyi+1,1 = yi + hiyi is used to predict a new yi+1,1, where yi is thetangent vector by solving
Gx Gβ
a⊤x 0
a⊤y 0
c⊤i ci
yi =
0
0
0
1
, (21)
with some constant vector [c⊤i , ci]⊤ ∈ RM+1.
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Correction
G(y) = 0
(a⊤x , 0)y = a⊤
x xi+1,1
(a⊤y , 0)y = a⊤
y xi+1,1
y⊤i y = y⊤
i yi+1,1
Newton’s method is chosen as a corrector,
Gx(yi+1,l) Gβ(yi+1,l)
a⊤x 0
a⊤y 0
x⊤i βi
δl =
−G(yi+1,l)
ρx,l
ρy,l
ρl
, l = 1, 2, . . . , (22)
with ρl = y⊤i (yi+1,l − yi+1,1), ρx,l = a⊤
x (xi+1,l − xi+1,1) and
ρy,l = a⊤y (xi+1,l − xi+1,1), is solved by yi+1,l+1 = yi+1,l + δl. If
yi+1,l converges until l = l∞, we accept yi+1 = yi+1,l∞ as an
approx to C.
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Outline
1 Introduction
2 Iterative method for one-component DNLSE
3 Stable continuation method for DNLSE
4 Analysis of 3-component DNLSE
5 Numerical Experiments
6 Rotating Bose-Einstein Condensates
7 Conclusions
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Some 3-component NLSE Results I
Let
Σ =
1 |βδ12| |βδ13|
|βδ12| 1 |βδ23|
|βδ13| |βδ23| 1
and βij = δijβ, Lin and Wei (2005) show that
Case 1 (all interactions are repulsive). If δ12 < 0, δ13 < 0 and
δ23 < 0, then the ground state solution does not exist.
Case 2 (all interactions are attractive). If δ12 > 0, δ13 > 0, δ23 > 0
and Σ is positive definite, then the ground state solution
exists.
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Some 3-component NLSE Results II
Case 3 (two repulsive and one attractive interactions). If δ12 < 0,
δ13 < 0, δ23 > 0 and Σ is positive definite, then the
ground state solution does not exist.
Case 4 (two attractive and one repulsive interactions). If δ12 > 0,
δ13 > 0, δ23 < 0, β ≪ 1 and the ground state solution
exists, then it must be non-radially symmetric.
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The 3-component DNLSE Setting I
We assume m = 3, λ1 = λ2 = λ3 = µ1 = µ2 = µ3 = 1.
The 3-coupled DNLSE G(x, β, δ) = 0 in (11), where
δ = (δ12, δ13, δ23), can be rewritten by
Au1 − u1 + u©31 + βδ12u
©22 u1 + βδ13u
©23 u1 = 0, (1a)
Au2 − u2 + u©32 + βδ12u
©21 u2 + βδ23u
©23 u2 = 0, (1b)
Au3 − u3 + u©33 + βδ13u
©21 u3 + βδ23u
©22 u3 = 0. (1c)
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The 3-component DNLSE Setting
Case 1 and 2 are straightforward.
Case 3 and 4 can be combined by letting
δ12 = δ13 = −1, δ23 = 1 and β ∈ R
.
1 23
The resulting DNLSE G(x, β) = 0 in (1) can be rewritten as
Au1 − u1 + u©31 − βu©2
2 u1 − βu©23 u1 = 0, (2a)
Au2 − u2 + u©32 − βu©2
1 u2 + βu©23 u2 = 0, (2b)
Au3 − u3 + u©33 − βu©2
1 u3 + βu©22 u3 = 0, (2c)
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Primal Stalk Solution I
Theorem
The primal stalk of 3-coupled DNLSE (2) can be described by
u1 =
√1+3β
1+β−2β2u∗,
u2 = u3 =√
1+β1+β−2β2u∗,
−1
3≤ β < 1 (3)
where u∗ is a solution of Au− u+ u©3 = 0.
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Primal Stalk Solution II
RemarkIf β → 1− then
u2 = u3 =
√1 + β
1 + 3βu1 →∞.
If β → −13
+then
u1 → 0, u2 = u3 →
√3
2u∗.
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Bifurcation Analysis
The solution curve C of (2):
C = y(s) = (x⊤(s), β(s))⊤|G(y(s)) = 0 is given in (2) (4)
Theorem
The primal stalk of C in (4) given by (3), undergoes at least N − p
bifurcation points at 0 < β = β∗q < 1, q = 1, . . . , N − p, where p is
the number of nonnegative eigenvalues of A− I + 3[[u©2∗ ]].
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Remark
In [2] show that the number of nonnegative eigenvalues of
∆φ − φ + 3ω2
∗φ = λφ,
φ ∈ H2(Rn),
is n + 1, where ω∗ is the unique solution of
∆φ − φ + φ3 = 0,
φ > 0 in Rn,
ω(x) → 0 as |x| → ∞.
In square domain (n = 2), it seem that the number of nonnegative
eigenvalues of A− I + 3[[u©2∗ ]] is 3. In numerical test, the number of
nonnegative eigenvalues of A− I + 3[[u©2∗ ]] (the eigenvalue bigger
than −10−3) is 3.
[2]C.-S. Lin and W.-M. Ni, On the diffusion coefficient of a semilinear Neumann problem, In
Lecture Notes in Mathematics, 1340(1988) 160-174.W. Wang (NTU) HCCM for NLSE Workshop on BEC 46 / 77
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Outline
1 Introduction
2 Iterative method for one-component DNLSE
3 Stable continuation method for DNLSE
4 Analysis of 3-component DNLSE
5 Numerical Experiments
6 Rotating Bose-Einstein Condensates
7 Conclusions
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Implementation
Fortran 95 codes
Hyperplane-constrained continuation method
Interfaces with the packages
Eigenvalue solver (ARPACK)
Linear system solver (GMRES by CERFACS)
Linux based workstation
Automatic bifurcation points detection
Automatic path following with user defined path following policy
Restarting from intermediate solutions
Batch runs
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Implementation
User defined PDEs
User defined program parameters
User defined initial solution
On-going work
Structure aware and efficient eigenvalue solver
Structure aware and efficient linear system solver
Parallel version (distributed memory or multi-cores)
Object version
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Simulation 1
Example
m = 3; Ω = [−5, 5]× [−5, 5]; λj = µj = 1,
The mesh size h of the grid domain Ωh is 0.2
(2 repulsive and 1 attractive) δ12 = δ13 = −1, δ23 = 1.
The solution curve
C+ =(x⊤, β)⊤| G(x, β) = 0 for β ∈ R+
. (1)
1 23
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0.2370.253
0.3850.387 1
R3N
β
0 0.2 0.4 0.6 0.8 115
20
25
30
35
40
E(x
)
β
Figure 1. Bifurcation curves and energy curves of DNLSE.
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Simulation 2
Example
m = 3; Ω = [−5, 5]× [−5, 5]; λj = µj = 1,
The mesh size h of the grid domain Ωh is 0.2
(2 attractive and 1 repulsive) δ12 = δ13 = 1, δ23 = −1.
The solution curve
C− =(x⊤, β)⊤| G(x, β) = 0 for β ∈ R−
. (1)
1 23
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0.333
R3N
β
0 0.1 0.2 0.3 0.415.5
16
16.5
17
17.5
E(x
)
β
Figure 2. Bifurcation curves and energy curves of DNLSE.
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Observations
The theoretical applicable range of β is −13≤ β < 1.
Computed β’s keep approaching, but never reaching 1.
A turning point is found at β = −0.3333.
In Simulation 1, computed energy keeps raising as β increases to
1. This is consistent to the result that if β → 1− then
u2 = u3 =
√1 + β
1 + 3βu1 →∞.
Computed solution profiles in Simulation 2 is in line with the
result that if β → −13
+then
u1 → 0, u2 = u3 →
√3
2u∗.
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Simulation 3
Example
m = 3; Ω = [−5, 5]× [−5, 5]; λj = µj = 1,
The mesh size h of the grid domain Ωh is 0.2
To search for the non-radially solution whose energy is less than
the radially symmetric solutions for small β, where
δ12 = δ13 = 1, δ23 = −1 (Simulation 2).
Only radially symmetric solutions are found in Simulation 2.
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Procedure
Step 1. We trace the solution curve
C1 =(x⊤, β)⊤| G(x, β) = 0 with δ12 = δ13 = δ23 = 1, for 0 ≤ β ≤ 0.2
. (2)
Step 2. Fix β = 0.2, then trace the solution curve
C2 =(x⊤, δ23)⊤|G(x, δ23)=0 with β=0.2, δ12 =δ13 = 1, for − 1≤δ23 ≤ 1
. (3)
Step 3. Fix δ23 = −1, then trace the solution curve
C3 =
(x⊤, β)⊤| G(x, β) = 0 with δ12 = δ13 = 1 and δ23 = −1, for β ∈ R
. (4)
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0 1-1 -0.314
δ23
R3N
-1 -0.5 0 0.5 1
14.5
15
15.5
16
16.5
17
δ23
E(x
)
Figure 3. Bifurcation curves and energy curves of DNLSE.
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0.969β
R3N
0 0.2 0.4 0.6 0.8 111
12
14
16
18
β
E(x
)
Figure 4. Bifurcation curves and energy curves of DNLSE.
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0 0.2 0.4 0.6 0.8 111
13
15
17
β
E(x
)
Figure 5. Energy curves of DNLSE.
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Motivation and Observations
Solutions on C1 are all ground state solutions.
Interactions are all attractive(δ12 = δ13 = δ23 = 1). Solutions
tends to gather together.
No bifurcation found.
Initial solution for β = 0 is ground state.
C2 is a “bridge” connecting C1 (three attractive) and C3 (two
attractive one repulsive).
One bifurcation occurs in C2. Primal stalk solutions lead to the
results in Simulation 2. The bifurcation branch leads to lower
energy solutions.
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Motivation and Observations
We let β decreases to zero in C3 to find the target solutions.
Another type non-radially symmetric solution is found for β
increase to 1 in C3.
The non-radially symmetric solutions are expected to be ground
state, as we start from ground state (β = 0) and follow the
lower energy path whenever bifurcation occurs.
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Outline
1 Introduction
2 Iterative method for one-component DNLSE
3 Stable continuation method for DNLSE
4 Analysis of 3-component DNLSE
5 Numerical Experiments
6 Rotating Bose-Einstein Condensates
7 Conclusions
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One Component Rotating BEC Model
−1
2∇2φ(x) + V (x)φ(x) + α|φ|2φ(x) + ωι∂θφ(x) = λφ(x), (5)
for x ∈ Ω ⊆ R2 with∫
Ω
|φ(x)|2dx = 1, (6)
Ω is a smooth bounded domain
V (x) ≥ 0 is the magnetic trapping potentials
∂θ = x∂y − y∂x is the z-component of the angular momentum
ω is the angular velocity of the rotating laser beam
λ is the chemical potential
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Induced Nonlinear Algebraic Eigenvalue Problem
Au+ αuH u©2 + ωιSu = λu, (7)
u⊤u = 1. (8)
A: standard central finite difference discretization of
−12∇2 + V (x)
S: discretization matrix corresponding to ∂θ
u©2 = u u, and denotes the Hadamard product
Dirichlet boundary condition
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Corresponding Energy Functional
Energy functional
E(φ) =
∫
Ω
(1
2|∇φ|2 +
1
2Vj|φ|
2 +α
4|φ|4 +
ωι
2φ∗∂θφ
), (9)
where φ∗ denotes the complex conjugate of φ
Finite dimensional case
E(u) =
(1
2uHAu+
α
4u©2 Hu©2
)+
ωι
2uH
Su. (10)
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NAEP rewritten
The new form
Au1 + α([[u©21 ]] + [[u©2
2 ]])u1 − ωSu2 = λu1, (11)
Au2 + α([[u©21 ]] + [[u©2
2 ]])u2 + ωSu1 = λu2, (12)
with
u⊤1 u1 + u⊤
2 u2 = 1, (13)
where u = u1 + ιu2 ∈ CN and u1,u2 ∈ RN
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The Parameter Dependent Polynomial System
Let ω = ω0 + ν0p (ω0, ν0 are given and p is the continuation
parameter), u = (u⊤1 ,u⊤
2 )⊤ ∈ R2N , and z = (u⊤, λ)⊤ ∈ R2N+1.
G(z, p) = 0, (14)
where G ≡ (G1,G2, g) : R2N+1 × R→ R2N+1 is given by
G1(z, p) = Au1 + α([[u©21 ]] + [[u©2
2 ]])u1 − ωSu2 − λu1, (15a)
G2(z, p) = Au2 + α([[u©21 ]] + [[u©2
2 ]])u2 + ωSu1 − λu2, (15b)
g(z, p) =1
2(u⊤
1 u1 + u⊤2 u2 − 1). (15c)
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Transformation Invariant Solutions
Define u(θ) : [0, 2π]→ R2N by
u(θ) =
[cos θu1 + sin θu2
− sin θu1 + cos θu2
]. (16)
Solution set C is a two dimensional manifold on R2N+2
G(u(θ), λ, p) = 0 for all θ ∈ [0, 2π]
Same energy: E(u(0)) = E(u(θ))
Same shape: |u(0)|2 = |u(θ)|2
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Hyperplane-Constrained Continuation Method
Consider the quotient solution set
C/θ = y(s) = (z(s)⊤, p(s))⊤| G(y(s)) = 0, s ∈ R. (17)
Compute the tangent vector of u(θ) at θ = 0,
∂u
∂θ(0) = (u⊤
2 ,−u⊤1 )⊤. (18)
Prediction vector yi = (z⊤i , p⊤i )⊤ satisfies DG(y(s))y(s) = 0
and is orthogonal to(
∂u∂θ
(0)⊤, 0)⊤
In correction, add an additional hyperplane constraint, with
normal vector(
∂u∂θ
(0)⊤, 0)⊤
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Visualization of the Idea
Predict Vector
Tangent vector
Transformation invariant solution
The "pipeline−like" soultion set
Computed solution curve
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An Analysis of Bifurcation
Theorem
Suppose 0 < α <∞ and p = ω in (8). Then the solution curve of
ground states undergoes at least n (= N − dim N (S)) bifurcation
points at finite value ω = ω∗i , i = 1, . . . n. That is, the Jacobian
matrix Gz(z, ω) is singular on Cθ at ω∗i .
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An Experimental Result
Madison, Chevy, Wohlleben, and Dalibard, PRL (84)5,
pp. 806–809, 2000
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Preliminary Computational Results
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Initial Solutions
Let ω = 0
Solve the linear eigenvalue problem Au = λu for α = 0
Take the ith smallest eigenvalue and the corresponding
eigenfunction as initial of continuation method
Follow the solution curve by increasing α = 0 to α = 100.
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Outline
1 Introduction
2 Iterative method for one-component DNLSE
3 Stable continuation method for DNLSE
4 Analysis of 3-component DNLSE
5 Numerical Experiments
6 Rotating Bose-Einstein Condensates
7 Conclusions
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Conclusions
m-coupled DNLSE
Nonlinear optics for Kerr-like photorefractive media
A hyperplane-constrained continuation method for ε-solutions
Analysis and numerical experiments for 3-coupled DNLSE
Bifurcation diagrams and non-radially symmetric ground states
Single component DNLSE
Single component rotating Bose-Einstein condensate
A hyperplane-constrained continuation method for
transformation invariant solutions
Bistability of solution curves
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Thank you.
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