From yjc at photon.poly.edu Tue Jul 3 18:15:57 2001
From: yjc at photon.poly.edu (Yi-Jen Chiang)
Date: Mon Jan 9 13:41:02 2006
Subject: [DMANET] WADS 2001 Call for Participation
Message-ID:
Please note that the deadlines for early registration and for dorm room
reservation are both July 9.
The registration fees are as follows:
Early Late
Regular Registration $315 $350
Student Registration $195 $220
Early registration fees apply when payment is received by July 9, 2001.
The current fee structure and payment options were finalized on June
2. Differences from their preliminary version include lower student
fees, postponed early registration deadline, and an honor system for
non-US participants who are unable to pay by check in US
dollars. Credit cards are No Longer accepted to avoid processing fees.
-----------------------------------------------------------------------------
Call for Participation
WADS 2001
7th Workshop on Algorithms and Data Structures
August 8-10, 2001
Brown University
Providence, Rhode Island, USA
http://www.wads.org/
Sponsored by the Center for Geometric Computing and by
the Department of Computer Science at Brown University
For details on the conference program, registration and accommodation
information, see the WADS Web site http://www.wads.org/.
Deadlines:
July 9: Early Registration
July 9: Dorm Room Reservation
Invited Lectures:
M. J. Atallah (Purdue):
Secure Multi-Party Computational Geometry
F. T. Leighton (Akamai and MIT):
The Challenges of Delivering Content on the Internet
M. Yannakakis (Bell Laboratories):
Approximation of Multiobjective Optimization Problems
Conference Organization:
R. Tamassia (Brown, conference chair), Y.-J. Chiang (Polytechnic,
publicity chair), G. Shubina (Brown, local arrangements chair)
Program Committee:
F. Dehne (Carleton, co-chair), J.-R. Sack (Carleton, co-chair),
R. Tamassia (Brown, co-chair), A. Apostolico, T. Chan,
B. Codenotti, G. Di Battista, S. Dolev, M. Farach-Colton,
P. Fraigniaud, H. Gabow, S. Goldman, M. Goodrich,
R. Grossi, M. Halldorsson, S. Khuller, R. Klein, J. Kleinberg,
G. Liotta, E. Mayr, J. Mitchell, S. Naeher, T. Nishizeki,
V. Prasanna, E. Puppo, J. Rolim, J. Snoeyink, I. Tollis, I. Vrt'o,
D. Wagner, T. Warnow, S. Whitesides, P. Widmayer
--------------------------------------------------------------------------
**********************************************************
*
* Contributions to be spread via DMANET are submitted to
*
* DMANET@zpr.uni-koeln.de
*
* Replies to a message carried on DMANET should NOT be
* addressed to DMANET but to the original sender. The
* original sender, however, is invited to prepare an
* update of the replies received and to communicate it
* via DMANET.
*
* DISCRETE MATHEMATICS AND ALGORITHMS NETWORK (DMANET)
* http://www.zpr.uni-koeln.de/dmanet
*
**********************************************************
From biedl at math.uwaterloo.ca Mon Jul 9 11:01:45 2001
From: biedl at math.uwaterloo.ca (Therese Biedl)
Date: Mon Jan 9 13:41:02 2006
Subject: [DMANET] CCCG'01: Call for participation
In-Reply-To:
Message-ID:
Early registration deadline is July 16th!
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
Call for Participation
13th Canadian Conference
on Computational Geometry
August 13-15, 2001
University of Waterloo
http://compgeo.math.uwaterloo.ca/~cccg01
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
Scope
CCCG'01 will take place in Waterloo on August 13-15 (with a welcome
reception on August 12). 44 papers will be presented at CCCG'01; a complete
list and the preliminary program is available from the conference web page.
Invited presentations will be given by Neil Sloane, George Hart and Mike
Lazaridis.
Registration
The early registration fee is CAD120 (CAD60 for students). Early
registration deadline is July 16, 2001. To register, please visit the
conference web page.
Accommodation
A block of rooms has been reserved in Hotel Laurier (the student residences
of nearby Wilfrid Laurier University). Please reserve before July 16, 2001.
Various other hotels are available nearby, see the conference web page for
details.
Further Information
Travel directions, local information, and an outline of the program are also
available from the conference web page.
Important dates
Submission of final paper: June 29, 2001
Early registration: July 16, 2001
Accommodation: July 16, 2001
Conference: August 13-15, 2001
Contact Information
Therese Biedl
Dept. of Computer Science
University of Waterloo
Waterloo, ON N2L 3G1
Phone: (519) 888-4567x4721
Fax: (519) 885-1208
Email: biedl@uwaterloo.ca
**********************************************************
*
* Contributions to be spread via DMANET are submitted to
*
* DMANET@zpr.uni-koeln.de
*
* Replies to a message carried on DMANET should NOT be
* addressed to DMANET but to the original sender. The
* original sender, however, is invited to prepare an
* update of the replies received and to communicate it
* via DMANET.
*
* DISCRETE MATHEMATICS AND ALGORITHMS NETWORK (DMANET)
* http://www.zpr.uni-koeln.de/dmanet
*
**********************************************************
From rohitsi at CS.Stanford.EDU Mon Jul 9 01:18:06 2001
From: rohitsi at CS.Stanford.EDU (Rohit Singh)
Date: Mon Jan 9 13:41:02 2006
Subject: dist btwn points sbjct to threshold
Message-ID:
Hi,
This is a newbie question, so I don't know if this is a standard problem:
I am doing some protein-modeling work. Given a set of points with 3-D
coordinates, I am interested in finding all pairs of points such that the
distance between the points is less than some threshold distance. This
threshold distance is chosen, independently, on the basis of energy
considerations. For each such pair, I also need the point-point distance.
The brute force way I am using right now is to go through all pairs of
points and calculate their distance and see if it is less than threshold.
Of course, one obvious improvement is to keep track, while calculating the
inter-point distance along each dimension, of the cumulative
square-of-distance (across all the dimensions considered so far) and stop
as soon as you exceed the square of the threshold. But since I only have 3
dimensions, it doesn't buy me much.
Are there more efficient ways of doing this?
Thanks in advance,
Rohit
-------------
The compgeom mailing lists: see
http://netlib.bell-labs.com/netlib/compgeom/readme.html
or send mail to compgeom-request@research.bell-labs.com with the line:
send readme
Now archived at http://www.uiuc.edu/~sariel/CG/compgeom/maillist.html.
From barequet at cs.Technion.AC.IL Wed Jul 11 08:51:18 2001
From: barequet at cs.Technion.AC.IL (Gill Barequet)
Date: Mon Jan 9 13:41:02 2006
Subject: Rectangular packing
Message-ID: <200107110451.HAA00037@cs.Technion.AC.IL>
Dear list,
Can anyone refer me to C/C++ code that implements a heuristic for
packing axis-parallel rectangles in an axis-parallel rectangular container?
Many thanks,
Gill
---------------------------------------------------------------------------
Gill Barequet Phone: +972-4-829-3219
Faculty of Computer Science Fax: +972-4-822-1128
(Rm.: [New] Taub 516) E-mail: barequet@cs.technion.ac.il
The Technion---IIT WWW: http://www.cs.technion.ac.il/~barequet
Haifa 32000 http://myprofile.cos.com/barequet
Israel
"Life is NP-Hard." (-)
-------------
The compgeom mailing lists: see
http://netlib.bell-labs.com/netlib/compgeom/readme.html
or send mail to compgeom-request@research.bell-labs.com with the line:
send readme
Now archived at http://www.uiuc.edu/~sariel/CG/compgeom/maillist.html.
From mdrinto at sandia.gov Tue Jul 10 21:04:59 2001
From: mdrinto at sandia.gov (Rintoul, Mark Daniel)
Date: Mon Jan 9 13:41:02 2006
Subject: dist btwn points sbjct to threshold
Message-ID:
If you have the memory, the easiest way is probably just to
superimpose a grid over the particles, and keep track (via pointers
say) of which particles are in each grid cell. Then, you just have
to look in your own grid cell and neighboring ones that could possibly
have a neighbor within the specified distance. By choosing the
grid size appropriately, you can just look in your cell and the
26 neighbor cells. I think the simulation literature just calls
this the "cell-list" method. It's basically just a hashing approach.
Danny
-----Original Message-----
From: Rohit Singh
To: compgeom-discuss@research.bell-labs.com
Sent: 7/9/2001 1:18 AM
Subject: dist btwn points sbjct to threshold
Hi,
This is a newbie question, so I don't know if this is a standard
problem:
I am doing some protein-modeling work. Given a set of points with 3-D
coordinates, I am interested in finding all pairs of points such that
the
distance between the points is less than some threshold distance. This
threshold distance is chosen, independently, on the basis of energy
considerations. For each such pair, I also need the point-point
distance.
The brute force way I am using right now is to go through all pairs of
points and calculate their distance and see if it is less than
threshold.
Of course, one obvious improvement is to keep track, while calculating
the
inter-point distance along each dimension, of the cumulative
square-of-distance (across all the dimensions considered so far) and
stop
as soon as you exceed the square of the threshold. But since I only have
3
dimensions, it doesn't buy me much.
Are there more efficient ways of doing this?
Thanks in advance,
Rohit
-------------
The compgeom mailing lists: see
http://netlib.bell-labs.com/netlib/compgeom/readme.html
or send mail to compgeom-request@research.bell-labs.com with the line:
send readme
Now archived at http://www.uiuc.edu/~sariel/CG/compgeom/maillist.html.
-------------
The compgeom mailing lists: see
http://netlib.bell-labs.com/netlib/compgeom/readme.html
or send mail to compgeom-request@research.bell-labs.com with the line:
send readme
Now archived at http://www.uiuc.edu/~sariel/CG/compgeom/maillist.html.
From ph+ at cs.cmu.edu Tue Jul 10 22:19:04 2001
From: ph+ at cs.cmu.edu (Paul Heckbert)
Date: Mon Jan 9 13:41:02 2006
Subject: dist btwn points sbjct to threshold
In-Reply-To:
Message-ID: <000601c109a7$79c88f60$7cd50280@graphics.cs.cmu.edu>
The algorithm you describe takes O(n^2) time, where n is the number of
points, and that is of course the best you can do for arbitrary point sets,
since the output set could be that large, but when you have fairly well
distributed points, as you probably do for protein work, you should be able
to get O(n) performance in practice.
A simple algorithm I've used for related problems is to create a k*k*k grid
of cubes, each of which contains a list of points within it. You could
choose k such that your cube size is proportional to the threshold distance
you're interested in, or if your points are uniformly distributed through
space (probably not true for proteins) you could try k proportional to n^.33
. Using that simple data structure, it is a simple matter of checking a
cube and perhaps some of the neighboring cubes to find all points within a
given distance of some query point. If your points are well-distributed and
you've chosen k appropriately, the average work required for each query will
be O(1), so finding all pairs with distance below threshold will be O(n).
See the paper "Fast Surface Particle Repulsion" at http://www.cs.cmu.edu/~ph
for more details on the cubical bucketing scheme.
Paul Heckbert
Computer Science Dept
Carnegie Mellon University
> -----Original Message-----
> From: Rohit Singh [mailto:rohitsi@CS.Stanford.EDU]
> Sent: Monday, July 09, 2001 3:18 AM
> To: compgeom-discuss@research.bell-labs.com
> Subject: dist btwn points sbjct to threshold
>
> I am doing some protein-modeling work. Given a set of points with 3-D
> coordinates, I am interested in finding all pairs of points such that the
> distance between the points is less than some threshold distance. This
> threshold distance is chosen, independently, on the basis of energy
> considerations. For each such pair, I also need the point-point distance.
> The brute force way I am using right now is to go through all pairs of
> points and calculate their distance and see if it is less than threshold.
> Of course, one obvious improvement is to keep track, while calculating the
> inter-point distance along each dimension, of the cumulative
> square-of-distance (across all the dimensions considered so far) and stop
> as soon as you exceed the square of the threshold. But since I only have 3
> dimensions, it doesn't buy me much.
>
> Are there more efficient ways of doing this?
-------------
The compgeom mailing lists: see
http://netlib.bell-labs.com/netlib/compgeom/readme.html
or send mail to compgeom-request@research.bell-labs.com with the line:
send readme
Now archived at http://www.uiuc.edu/~sariel/CG/compgeom/maillist.html.
From Frederic.Cazals at sophia.inria.fr Thu Jul 12 11:34:23 2001
From: Frederic.Cazals at sophia.inria.fr (Frederic Cazals)
Date: Mon Jan 9 13:41:02 2006
Subject: dist btwn points sbjct to threshold
In-Reply-To: <000601c109a7$79c88f60$7cd50280@graphics.cs.cmu.edu>; from ph+@cs.cmu.edu on Tue, Jul 10, 2001 at 09:19:04PM -0400
References: <000601c109a7$79c88f60$7cd50280@graphics.cs.cmu.edu>
Message-ID: <20010712103423.A11064@neiges.inria.fr>
Dear All,
as a follow-up to the discussion regarding bucket-based strategies to
retrieve the pairs of points whose distance is less than a given
threshold, some variants of uniform grids deserve a quote. uniform
grids indeed solve the problem efficiently (theoretically and
practically) as long as the points' distribution is uniform. if not,
variants (in particular recursive grids) are a better choice. the
following bibliography discusses some of these issues:
@book{d-lnba-86
, author = "L. Devroye"
, title = "Lecture Notes on Bucket Algorithms"
, publisher = "Birkh{\"a}user Verlag"
, address = "Boston, MA"
, year = 1986
, keywords = "book"
}
@inproceedings{cp-blspd-97
, author = "F. Cazals and C. Puech"
, title = "Bucket-like space partitioning data structures with applications to ra
y tracing"
, booktitle = "Proc. 13th Annu. ACM Sympos. Comput. Geom."
, year = 1997
, pages = "11--20"
, cites = "a-fcagi-94, aeiim-pubtc-85, c-cpoda-97, cdp-fchcn-95, cs-sigte-97, bko
s-cge-95, d-lnba-86, d-mddtd-88, glm-othsr-96, jl-rrt-92, ks-frtua-97, bwy-oetac-80, mm
s-qsrs-94, nhs-gfasm-84, o-cgc-94, ps-cgi-85, s-iggp-76, s-igbmf-93, sd-fbcve-95, sd-cs
dta-95, w-ltsbv-92, ZZZ"
, update = "98.07 bibrelex, 97.07 efrat"
}
@inproceedings{ho-smhsr-94
, author = "D. Halperin and M. H. Overmars"
, title = "Spheres, Molecules, and Hidden Surface Removal"
, booktitle = "Proc. 10th Annu. ACM Sympos. Comput. Geom."
, year = 1994
, pages = "113--122"
, cites = "abbkw-pdb-87, bkwmbrkst-pdbcb-77, cegs-sessr-91, cegsw-ccbac-90, c-sas
pn-83, b-rsdoh-93, dkmmrt-dphul-88, fp-eagcs-93, fo-fdmm-, gs-pmgsc-85, s-hb-70, kos-eh
sro-92, klps-ujrcf-86, lr-ipses-71, mmpssw-ftdlm-91, m-ms-90, o-plfs-92, ps-cgi-85, s-a
tubl-93, sho-cfsrm-93, vb-facrs-93, ZZZ"
, update = "98.03 bibrelex, 94.09 jones, 94.01 jones"
}
Frederic Cazals.
On Tue, Jul 10, 2001 at 09:19:04PM -0400, Paul Heckbert wrote:
> The algorithm you describe takes O(n^2) time, where n is the number of
> points, and that is of course the best you can do for arbitrary point sets,
> since the output set could be that large, but when you have fairly well
> distributed points, as you probably do for protein work, you should be able
> to get O(n) performance in practice.
>
> A simple algorithm I've used for related problems is to create a k*k*k grid
> of cubes, each of which contains a list of points within it. You could
> choose k such that your cube size is proportional to the threshold distance
> you're interested in, or if your points are uniformly distributed through
> space (probably not true for proteins) you could try k proportional to n^.33
> . Using that simple data structure, it is a simple matter of checking a
> cube and perhaps some of the neighboring cubes to find all points within a
> given distance of some query point. If your points are well-distributed and
> you've chosen k appropriately, the average work required for each query will
> be O(1), so finding all pairs with distance below threshold will be O(n).
>
> See the paper "Fast Surface Particle Repulsion" at http://www.cs.cmu.edu/~ph
> for more details on the cubical bucketing scheme.
>
> Paul Heckbert
> Computer Science Dept
> Carnegie Mellon University
--
------------------------------------ ---------------- -------- ---- -- -
-- Project PRISME, INRIA Sophia-Antipolis, 2004 route des Lucioles,
-- BP 93, F-06902 Sophia-Antipolis,
-- Tel: 33 (0)4 92 38 71 88, Fax: 33 (0)4 92 38 76 43
-- Frederic.Cazals@sophia.inria.fr, http://www.inria.fr/prisme
-------------
The compgeom mailing lists: see
http://netlib.bell-labs.com/netlib/compgeom/readme.html
or send mail to compgeom-request@research.bell-labs.com with the line:
send readme
Now archived at http://www.uiuc.edu/~sariel/CG/compgeom/maillist.html.
From eppstein at ics.uci.edu Wed Jul 11 16:48:36 2001
From: eppstein at ics.uci.edu (David Eppstein)
Date: Mon Jan 9 13:41:02 2006
Subject: dist btwn points sbjct to threshold
In-Reply-To:
Message-ID: <1865525.3203855316@hyperbolic.ics.uci.edu>
On 7/9/01 12:18 AM -0700, Rohit Singh wrote:
> I am doing some protein-modeling work. Given a set of points with 3-D
> coordinates, I am interested in finding all pairs of points such that the
> distance between the points is less than some threshold distance.
This can be solved in time O(n log n + k) where k is the number of output
points. I think the first paper to do this may be Bentley, Stanat, and
Williams, "The complexity of finding fixed radius near neighbors", Inf.
Proc. Lett.