Finding geodesics joining given points
Finding a geodesic joining two given points in a complete path-connected Riemannian manifold requires much more effort than determining a geodesic from initial data. This is because it is much harder to solve boundary value problems than initial value problems. Shooting methods attempt to solve boun...
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description | Finding a geodesic joining two given points in a complete path-connected Riemannian manifold requires much more effort than determining a geodesic from initial data. This is because it is much harder to solve boundary value problems than initial value problems. Shooting methods attempt to solve boundary value problems by solving a sequence of initial value problems, and usually need a good initial guess to succeed. The present paper finds a geodesic
γ
:
[
0
,
1
]
→
M
on the Riemannian manifold
M
with
γ
(0) =
x
0
and
γ
(1) =
x
1
by dividing the interval [0,1] into several sub-intervals, preferably just enough to enable a good initial guess for the boundary value problem on each subinterval. Then a geodesic joining consecutive endpoints (local junctions) is found by single shooting. Our algorithm then adjusts the junctions, either (1) by minimizing the total squared norm of the differences between associated geodesic velocities using Riemannian gradient descent, or (2) by solving a nonlinear system of equations using Newton’s method. Our algorithm is compared with the known
leapfrog algorithm
by numerical experiments on a 2-dimensional ellipsoid Ell(2) and on a left-invariant 3-dimensional special orthogonal group
S
O
(3). We find Newton’s method (2) converges much faster than leapfrog when more junctions are needed, and that a good initial guess can be found for (2) by starting with Riemannian gradient descent method (1). |
doi_str_mv | 10.1007/s10444-022-09966-y |
format | Article |
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γ
:
[
0
,
1
]
→
M
on the Riemannian manifold
M
with
γ
(0) =
x
0
and
γ
(1) =
x
1
by dividing the interval [0,1] into several sub-intervals, preferably just enough to enable a good initial guess for the boundary value problem on each subinterval. Then a geodesic joining consecutive endpoints (local junctions) is found by single shooting. Our algorithm then adjusts the junctions, either (1) by minimizing the total squared norm of the differences between associated geodesic velocities using Riemannian gradient descent, or (2) by solving a nonlinear system of equations using Newton’s method. Our algorithm is compared with the known
leapfrog algorithm
by numerical experiments on a 2-dimensional ellipsoid Ell(2) and on a left-invariant 3-dimensional special orthogonal group
S
O
(3). We find Newton’s method (2) converges much faster than leapfrog when more junctions are needed, and that a good initial guess can be found for (2) by starting with Riemannian gradient descent method (1).</description><identifier>ISSN: 1019-7168</identifier><identifier>EISSN: 1572-9044</identifier><identifier>DOI: 10.1007/s10444-022-09966-y</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Algorithms ; Boundary value problems ; Computational mathematics ; Computational Mathematics and Numerical Analysis ; Computational Science and Engineering ; Geodesy ; Mathematical and Computational Biology ; Mathematical Modeling and Industrial Mathematics ; Mathematics ; Mathematics and Statistics ; Mathematics of Computation and Optimisation ; Nonlinear systems ; Riemann manifold ; Visualization</subject><ispartof>Advances in computational mathematics, 2022-08, Vol.48 (4), Article 50</ispartof><rights>The Author(s) 2022</rights><rights>The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c363t-b7ce697194bf9dcadabb85f10dafded8a571f23d0950ff3d64eec7f7b82b840e3</citedby><cites>FETCH-LOGICAL-c363t-b7ce697194bf9dcadabb85f10dafded8a571f23d0950ff3d64eec7f7b82b840e3</cites><orcidid>0000-0002-4005-5431</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10444-022-09966-y$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10444-022-09966-y$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Noakes, Lyle</creatorcontrib><creatorcontrib>Zhang, Erchuan</creatorcontrib><title>Finding geodesics joining given points</title><title>Advances in computational mathematics</title><addtitle>Adv Comput Math</addtitle><description>Finding a geodesic joining two given points in a complete path-connected Riemannian manifold requires much more effort than determining a geodesic from initial data. This is because it is much harder to solve boundary value problems than initial value problems. Shooting methods attempt to solve boundary value problems by solving a sequence of initial value problems, and usually need a good initial guess to succeed. The present paper finds a geodesic
γ
:
[
0
,
1
]
→
M
on the Riemannian manifold
M
with
γ
(0) =
x
0
and
γ
(1) =
x
1
by dividing the interval [0,1] into several sub-intervals, preferably just enough to enable a good initial guess for the boundary value problem on each subinterval. Then a geodesic joining consecutive endpoints (local junctions) is found by single shooting. Our algorithm then adjusts the junctions, either (1) by minimizing the total squared norm of the differences between associated geodesic velocities using Riemannian gradient descent, or (2) by solving a nonlinear system of equations using Newton’s method. Our algorithm is compared with the known
leapfrog algorithm
by numerical experiments on a 2-dimensional ellipsoid Ell(2) and on a left-invariant 3-dimensional special orthogonal group
S
O
(3). We find Newton’s method (2) converges much faster than leapfrog when more junctions are needed, and that a good initial guess can be found for (2) by starting with Riemannian gradient descent method (1).</description><subject>Algorithms</subject><subject>Boundary value problems</subject><subject>Computational mathematics</subject><subject>Computational Mathematics and Numerical Analysis</subject><subject>Computational Science and Engineering</subject><subject>Geodesy</subject><subject>Mathematical and Computational Biology</subject><subject>Mathematical Modeling and Industrial Mathematics</subject><subject>Mathematics</subject><subject>Mathematics and Statistics</subject><subject>Mathematics of Computation and Optimisation</subject><subject>Nonlinear systems</subject><subject>Riemann manifold</subject><subject>Visualization</subject><issn>1019-7168</issn><issn>1572-9044</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><recordid>eNp9kEtLxDAUhYMoOI7-AVcFwV305t0sZXBGYcCNrkObR2nRtiYdof_eOBXcubqvc86FD6FrAncEQN0nApxzDJRi0FpKPJ-gFRGKYp0Pp7kHorEisjxHFyl1AKClEit0u2171_ZN0fjB-dTaVHRD2x837ZfvizFPU7pEZ6F6T_7qt67R2_bxdfOE9y-7583DHlsm2YRrZb3UimheB-1s5aq6LkUg4KrgvCsroUigzIEWEAJzkntvVVB1SeuSg2drdLPkjnH4PPg0mW44xD6_NFRqIYATpbOKLiobh5SiD2aM7UcVZ0PA_PAwCw-TeZgjDzNnE1tMKYv7xse_6H9c3-DdY9w</recordid><startdate>20220801</startdate><enddate>20220801</enddate><creator>Noakes, Lyle</creator><creator>Zhang, Erchuan</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>C6C</scope><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0002-4005-5431</orcidid></search><sort><creationdate>20220801</creationdate><title>Finding geodesics joining given points</title><author>Noakes, Lyle ; Zhang, Erchuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c363t-b7ce697194bf9dcadabb85f10dafded8a571f23d0950ff3d64eec7f7b82b840e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Boundary value problems</topic><topic>Computational mathematics</topic><topic>Computational Mathematics and Numerical Analysis</topic><topic>Computational Science and Engineering</topic><topic>Geodesy</topic><topic>Mathematical and Computational Biology</topic><topic>Mathematical Modeling and Industrial Mathematics</topic><topic>Mathematics</topic><topic>Mathematics and Statistics</topic><topic>Mathematics of Computation and Optimisation</topic><topic>Nonlinear systems</topic><topic>Riemann manifold</topic><topic>Visualization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Noakes, Lyle</creatorcontrib><creatorcontrib>Zhang, Erchuan</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>CrossRef</collection><jtitle>Advances in computational mathematics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Noakes, Lyle</au><au>Zhang, Erchuan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Finding geodesics joining given points</atitle><jtitle>Advances in computational mathematics</jtitle><stitle>Adv Comput Math</stitle><date>2022-08-01</date><risdate>2022</risdate><volume>48</volume><issue>4</issue><artnum>50</artnum><issn>1019-7168</issn><eissn>1572-9044</eissn><abstract>Finding a geodesic joining two given points in a complete path-connected Riemannian manifold requires much more effort than determining a geodesic from initial data. This is because it is much harder to solve boundary value problems than initial value problems. Shooting methods attempt to solve boundary value problems by solving a sequence of initial value problems, and usually need a good initial guess to succeed. The present paper finds a geodesic
γ
:
[
0
,
1
]
→
M
on the Riemannian manifold
M
with
γ
(0) =
x
0
and
γ
(1) =
x
1
by dividing the interval [0,1] into several sub-intervals, preferably just enough to enable a good initial guess for the boundary value problem on each subinterval. Then a geodesic joining consecutive endpoints (local junctions) is found by single shooting. Our algorithm then adjusts the junctions, either (1) by minimizing the total squared norm of the differences between associated geodesic velocities using Riemannian gradient descent, or (2) by solving a nonlinear system of equations using Newton’s method. Our algorithm is compared with the known
leapfrog algorithm
by numerical experiments on a 2-dimensional ellipsoid Ell(2) and on a left-invariant 3-dimensional special orthogonal group
S
O
(3). We find Newton’s method (2) converges much faster than leapfrog when more junctions are needed, and that a good initial guess can be found for (2) by starting with Riemannian gradient descent method (1).</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10444-022-09966-y</doi><orcidid>https://orcid.org/0000-0002-4005-5431</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Boundary value problems Computational mathematics Computational Mathematics and Numerical Analysis Computational Science and Engineering Geodesy Mathematical and Computational Biology Mathematical Modeling and Industrial Mathematics Mathematics Mathematics and Statistics Mathematics of Computation and Optimisation Nonlinear systems Riemann manifold Visualization |
title | Finding geodesics joining given points |
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