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A Partition-Based Heuristic for Translational Box CoveringPowerPoint Presentation

A Partition-Based Heuristic for Translational Box Covering

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### A Partition-Based Heuristic for Translational Box Covering

### BACKUP SLIDES

Ben England and Karen Daniels

Department of Computer Science

University of Massachusetts Lowell

supported in part by NSF and DARPA under grant DMS-0310589

Repair work: collection of pieces cover a hole

Motivation for 2D Polygonal CoveringNP-hard problem

Supported under NSF/DARPA CARGO program

Box Covering

- Goal: Translate a collection of boxes (orthotopes) Q = {Q1, Q2 , ... , QN} to cover another box P in 2d, 3d, …
- Motivation: Boxes can form enclosures for general shapes.

2d views of 3d covering

1st published results in > 2d

Partial cover (red part uncovered)

Full cover

40 covering shapes

20 covering shapes

d = dimension

NP-hard problem

With Masters student B. England

Supported under NSF/DARPA CARGO program

. . . .

. . .

. . .

- Survey of non-algorithmic results [Tot04]
- Thin coverings of the plane with congruent convex shapes
- Translational covering of arbitrary polygonal shapes [Dan01,Dan03]
- Translational B-spline covering [Nea06]
- Volume condition for translational covering of a cube by a sequence of convex shapes (arbitrary dimension) [Gro85]
- Volume condition for on-line algorithm for translational covering of a cube by a sequence of convex shapes (arbitrary dimension) [Las97]

covering

combinatorial covering

geometric covering

VERTEX-COVER, SET-COVER (including [Gri99]), EDGE-COVER, VLSI logic minimization, facility location

translational covering

P: finite point sets

P: shapes

Q: identical

Q: nonconvex

Q: convex

. . .

decomposition:

1D interval covered by annuli using approximation algorithm [Hoc87]

BOX-COVER [Fow81]

NP-complete

partition:

decomposition with covering

- NP-hardness proofs for 4 polygon covering problems [Cul88]
- Approximation algorithms for some orthogonal covering problems [Ber92]
- Approximation algorithm to cover orthogonal polygon (with holes) with minimum number of rectangles [Kum03]
- Clique-based Integer Programming (IP) model for covering orthogonal polygon with minimum number of rectangles [Hei05]
- Polynomial-time results for restricted orthogonal polygon covering and horizontally convex polygons

Polynomial-time algorithms for triangulation [Cha91] and some tilings

Box Covering Outline

- Set covering approach
- Key volume expressions
- Partition-based heuristic
- Experimental highlights
- Dimension-independent volume test
- Computational considerations
- Execution time dominated by 1-OPT
- Alternatives to 1-OPT
- 1-OPT preprocessing
- Monotonicity across calls to Lagrangian heuristic

- Conclusion and future work

Lagrangian heuristic comes from Lagrangian relaxation of IP model.

1-OPT heuristic swaps groups for cover shape that best improves objective function until no improvement.

g12 = {2,4}

g11 = {1,3}

g22 = {3,4}

g21 = {1,2}

g32 = {2}

g31 = {1}

g34 = {4}

g33 = {3}

C1 = {g11,g12}

C2 = {g21,g22}

C3 = {g31,g32,g33,g34}

Problem: choose just one part group gjk from the set of part groups Cj for each cover shape Qj such that every part of P is in one of the chosen part groups.

Solution: {g11,g21,g34}

Set Covering Approach Applied to Box Coveringparts

IP model, maximizing number of parts covered, treated with Lagrangian Heuristic + 1-OPT

Based on Daniels and Grinde, IIE Transactions, 1999

Key Volume Expressions

quantized volume

effective volume

quantized effective volume

volume

d = a generic part of P

Uniform refinement scheme, unlike general polygonal approach of [Dan03], which subdivides one triangle during each iteration of repeat loop.

d = dimension

j = total number of parts of P

d = a generic part of P

N = number of covering shapes

LGC_Cover( ) = modified Lagrangian Heuristic + 1-OPT

Minkowski sum of two sets A and B is

Experimental Highlights2D Validation Experiment:

- 20 instances with square P and N = 2…6 rectangular covering shapes
- OrthotopeCover( ) outperforms polygonal solver of Daniels, et al. [CCCG2003] by at least 2 orders of magnitude
- Simpler geometric operations (no Minkowski sum)
- Volume tests that do not generalize to arbitrary polygons

Example:

4 identical square covering shapes

Daniels, et al. [CCCG2003]:

Current paper:

167 triangular parts

4 square parts

94 triangle vertices

5 square vertices

888 groups

80 groups

875 seconds

0.2 seconds

450 MHz CPU Sun SPARC Ultra 60TM with 512 MB memory

cover shape volume= = 1.21

Experimental Highlights

Results in 3d and 4d:

d = dimension

N = number of covering shapes

3 GHz 64-bit Intel PentiumTM D CPU with 2 GB memory

csv = cover shape volume

j = total number of parts of P

maximum aspect ratio of a covering shape = 4

2d views of 3d covering using OpenGL

Experimental HighlightsMore results in 3d:

1 GHz Intel PentiumTM 4 CPU with 1/2 GB memory

Problem Instance “Hardness” Characterization

N = number of covering shapes

d = dimension

d = a generic part of P

j = total number of parts of P

Quantized Effective Volume Ratio

Dimension-independent Volume Margin

~10 instances for each parameter combination

tp = total points.

r = correlation coefficient between Y and % coverage

1 GHz Intel PentiumTM 4 CPU with 1/2 GB memory

d # jN #calls %savings

instances saved

47.2

3

17

8192

2.8

12-18

128-4096

2.3

12-16

58.8

3

13

test added

Heuristic:

Effectiveness of Y

# calls saved = average per instance

% savings = average relative % savings of LCG_Cover( ) calls

100% coverage was reached by original & revised heuristic in all these cases.

3 GHz 64-bit Intel PentiumTM D CPU with 2 GB memory

Computational Considerations

- Execution Time
- OrthotopeCover( ) dominated by LGC_Cover( )
- LGC_Cover( ) dominated by deterministic 1-OPT
- 1-OPT attempts to increase lower bound on Lagrangian dual

- Unlike polygonal heuristic in which group maintenance dominates

- Alternatives to 1-OPT
- 2-OPT too expensive
- Randomization:
- Simulated annealing’s random swaps inferior to 1-OPT
- Random group sampling weakens 1-OPT

- 1-OPT Preprocessing
- 1-OPT really behaves like a greedy global improvement strategy
- 1-OPT preprocessing yields improvement in:
- 75% of 2d instances
- 87% of 3d instances
- 64% of 4d instances

Test suite = subset of 30 of our randomly generated instances: 10 2d, 10 3d, 10 4d

3 GHz 64-bit Intel PentiumTM D CPU with 2 GB memory

1-OPT heuristic swaps groups for cover shape that best improves objective function until no improvement.

Computational Considerations

- Monotonicity across calls to LGC_Cover( ) Lagrangian heuristic
- No theoretical guarantee that number of parts covered increases.
- Number of parts doubles before each LGC_Cover( ) call.
- LGC_Cover( ) is only a heuristic.

- Success depends on N, d, thickness of cover, richness of group structure and strength of LGC_Cover( ).
- Group structure is rich.
- 1-OPT helps LGC_Cover( ) cover increasing number of parts.

- Sample progression for 2d, N = 6, csv = 1.25:
- 504 of 512 parts covered (98.4%)
- 1014 of 1024 parts covered (99.%)
- 2039 of 2048 parts covered (99.6%)
- 4096 of 4096 parts covered (100%)

- No theoretical guarantee that number of parts covered increases.

monotonically increasing

Test suite = subset of 30 of our randomly generated instances: 10 2d, 10 3d, 10 4d

3 GHz 64-bit Intel PentiumTM D CPU with 2 GB memory

1-OPT heuristic swaps groups for cover shape that best improves objective function until no improvement.

Conclusion & Future Work

- Partition-based, translational, box covering heuristic has dimension as an input.
- Set covering approach uses uniform refinement.
- First 3d, 4d heuristic results for our translational box covering problem
- Found covers for some instances with as many as 50 covering shapes.

- Box covering heuristic outperforms general, polygonal heuristic in 2d rectangular experiment.
- Dimension-independent volume margin avoids many refinement steps.
- Computational considerations
- Execution time is dominated by deterministic 1-OPT improvement heuristic.
- 1-OPT outperforms 2-OPT, simulated annealing and randomized 1-OPT.
- 1-OPT preprocessing improves results.
- Monotonicity across calls to Lagrangian heuristic occurs often in practice, although not theoretically guaranteed.

- Future work:
- Use boxes as enclosures for more general shapes to:
- improve 2d general covering heuristic
- treat 3d general covering

- Allow rotations

- Use boxes as enclosures for more general shapes to:

References

Acknowledgement: Thanks to Michelle Daniels for comments.

For More Information

- Email [email protected]
- Web sites:
- Thanks for your attention!
- Questions?

http://www.cs.uml.edu/~kdaniels/covering/covering.htm

http://www.cs.uml.edu/~bengland/cg/orthotope_cover/

19

(from CCCG 2003, etc.)

Translational 2D Polygon Covering

P2

P2

P1

Q3

P1

Q2

Q2

Q1

Sample P and Q

Translated Q Covers P

Q1

Q3

2D Polygonal Covering [CCCG 2001,CCCG2003]Supported under NSF/DARPA CARGO program

- Input:
- Covering polygons Q = {Q1, Q2 , ... , Qm}
- Target polygons (or point-sets) P = {P1, P2 , ... , Pn}

- Output:
- Translations g = {g1, g2 , ... , gm} such that

With graduate students R. Inkulu, A. Mathur, C.Neacsu, & UNH professor R. Grinde

2D B-Spline Covering [CORS/INFORMS2004, UMass Lowell Student Research Symposium 2004, Computers Graphics Forum, 2006]

Supported under NSF/DARPA CARGO program

motivated by 3D CAD

NP-hard problem

With graduate student C. Neacsu

Covering Web Sitehttp://www.cs.uml.edu/~kdaniels/covering/covering.htm

With graduate student C. Neacsu and undergraduate A. Hussin

Combinatorial Covering Procedure: LAGRANGIAN-COVER IP Model

exactly 1 group chosen for each Qj

value of 1 contributed to objective function for each triangle covered by a Qj, where that triangle is in a group chosen for that Qj

Variables:

Parameters:

T2

T3

T4

T5

Combinatorial Covering Procedure: LAGRANGIAN-COVER IP ParametersTriangles:

Groups:

Qj’s:

b11=1 b12=0

b21=0b22=1

b31=1b32=1

G1

Q1

G2

Q2

a11=1 a12=1 a13=1

a21=1a22=1a23=1

a31=1a32=0a33=0

a41=1a42=0a43=0

a51=0a52=1a53=0

G3

G3

Combinatorial Covering Procedure: LAGRANGIAN-COVER IP Constraints

k=2

k=1

k=3

b11=1 b12=0

b21=0b22=1

b31=1b32=1

j=1

j=2

Variables:

Parameters:

j=1

j=1

j=1

j=1

j=2

j=2

j=2

j=2

j=2

Combinatorial Covering Procedure: LAGRANGIAN-COVER IP Constraintsvalue of 1 contributed to objective function for each triangle covered by a Qj, where that triangle is in a group chosen for that Qj

k=3

k=1

k=2

b11=1 b12=0

b21=0b22=1

b31=1b32=1

a11=1 a12=1 a13=1

a21=1a22=1a23=1

a31=1a32=0a33=0

a41=1a42=0a43=0

a51=0a52=1a53=0

Variables:

Parameters:

T2

T3

T4

T5

Combinatorial Covering Procedure: LAGRANGIAN-COVER IP VariablesTriangles:

Groups:

Qj’s:

Group choices:

G1 for Q1

G2 for Q2

G1

Q1

g11=1 g12=0g21=0g22=1g31=0g32=0

G2

Q2

t1 , t2=1multiply covered

G3

t1=1 t2=1t3=1t4=1 t5=1

Lagrangian Relaxation

exactly 1 group chosen for each Qj

value of 1 contributed to objective function for each triangle covered by a Qj, where that triangle is in a group chosen for that Qj

bring into objective function

Variables:

Parameters:

Lagrangian Relaxation

maximize

Lagrange Multipliers

2

3

4

removing constraints

minimize

2

l>=0 and subtracting term < 0

3

Lagrangian Relaxation LR(l)

1

Lower bounds come from any feasible solution to

1

4

Lagrangian Dual: min LR(l), subject to l >= 0

Lagrangian Relaxation

Lagrangian Relaxation LR(l)

LR(l) is separable

SP1

SP2

Solve: if (1-li) >=0

then set ti=1

else set ti=0

Solve: Redistribute:

Solve j sub-subproblems

- compute gkj coefficients

- set to 1 gkjwith largest coefficient

For candidate l values, solve SP1, SP2

Lagrangian Relaxation

1

- Generating lower bound for :
- SP2 solution yields gkj values feasible for
- Modify ti values accordingly
- Result is feasible for

1

1

1

Lagrangian Relaxation

SP2

SP1

- SP1, SP2 have integrality property
- Solutions unchanged when variable integrality not enforced
- Optimal value of Lagrangian Dual no better than Linear Programming relaxation of
- Use as a heuristic:
- Upper bound for
- Lower bound for by generating feasible solution to

- Fast, predictable execution time
- Optimization software libraries not required

1

1

1

1

Lagrangian Relaxation

- Search l space using subgradient optimization
- Initialize lis (e.g. 0)
- Solve SP1 and SP2
- Update upper bound using sum of SP1, SP2 solutions
- Generate feasible solution
- Improve feasible solution using local exchange heuristic
- Update lower bound using feasible solution
- Calculate subgradients
- Calculate step size
- Take a step in subgradient direction
- Update lis

Iterate until stopping criteria satisfied

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