Approximation Algorithms for Circle Packing

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Packing

Flávio K. Miyazawa unicamp

São Paulo School of Advanced Science on Algorithms, Combinatorics and Optimization

São Paulo, SP July, 2016

Flávio K. Miyazawa Approximation Algorithms for Circle Packing July, 2016 1 / 57

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Circle Packing Problems Basic Algorithms

Circle Bin Packing

Bounded Space Online Bin Packing

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Main references

•F. K. Miyazawa, L. L. C. Pedrosa, R. C. S. Schouery, M. Sviridenko, Y. Wakabayashi. Polynomial-Time Approximation Schemes for Circle and Other Packing Problems. Algorithmica. To appear.

•P. H. Hokama, F. K. Miyazawa and R. C. S. Schouery. A bounded space algorithm for online circle packing. Information Processing Letters, 116, p. 337-342, 2016.

This work also obtained collaboration fromLehilton L. C. Pedrosa, Maxim Sviridenko, Rafael C. S. Schouery, Pedro H. Hokamaand Yoshiko Wakabayashi.

My thanks to Lehilton Pedrosa and Rafael Schouery that also contributed to part of these slides. This work obtained support from CNPq, FAPESP, UNICAMP, USP.

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Circle Packing Problems

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Packings

Given:

I A list of geometrical itemsL and bins B

I Obtain agood packing of items inL into bin B ∈ B

I The inner region of two packed items cannot overlap

I Each packed item must be totally contained in the bin

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Problems

Circle Strip Packing

I Input: List of circles L= (c1, . . . ,cn)

I Output: Packing of L into a rectangle of width 1 and minimum height.

Minimize

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Problems

Circle Bin Packing

I Input: List of circles L= (c₁, . . . ,c_n)

I Output: Packing of L into the minimum number of unit bins.

Minimize

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Some Applications

I Cutting and Packing of circular items

I Transportation of tubes, cilinders,...

I Cable assembly/allocations

I Tree plantation

I Origami design

I Marketing

I Cylinder pallet assembly

I . . .

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Marketing

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Computational Complexity

Demaine, Fekete, Lang’10: To decide if a set of circles can be packed into a square is NP-hard.

Approximation Algorithms:

I Efficient Algorithms (polynomial time)

I Analysis: Howfar from the optimum solution value ?

I

Compromise:

Computational Time × Solution Quality

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Computational Complexity

Approximation Algorithms:

I

Compromise:

Computational Time × Solution Quality

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Computational Complexity

Approximation Algorithms:

I

Compromise:

Computational Time × Solution Quality

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Computational Complexity

Approximation Algorithms:

I

Compromise:

Computational Time × Solution Quality

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Approximation Algorithms

I A(I) Value of the solution produced by A for instance I

I OPT(I) Value of an optimum solution of I

I A has approximation factor α if

I A has asymptotic approximation factorα if

for some constantβ

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Approximation Algorithms

A(I)

OPT(I) ≤α for any instanceI OPT(I)

A(I)

Minimize

for some constantβ

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Approximation Algorithms

A(I)

OPT(I) ≤α for any instanceI OPT(I)

A(I)

Minimize

A(I)≤αOPT(I) +β for any instance I, for some constantβ

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Approximation Algorithms

For Minimization Problems

PTAS:Polynomial Time Approximation Scheme

I A family of polynomial time algorithms A_ε, ε >0, is a polynomial time approximation schemeif

A_ε(I)≤(1+ε)OPT(I), for any instance I

APTAS: Asymptotic Polynomial Time Approximation Scheme

I A family of algorithmsA_ε,ε > 0 is an asymptotic polynomial time approximation schemeif

A_ε(I)≤(1+ε)OPT(I) +β_ε, for any instance I where βε is a constant that depends only on ε

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Approximation Algorithms

For Minimization Problems

PTAS:Polynomial Time Approximation Scheme

I A family of polynomial time algorithms A_ε, ε >0, is a polynomial time approximation schemeif

A_ε(I)≤(1+ε)OPT(I), for any instance I

APTAS: Asymptotic Polynomial Time Approximation Scheme

I A family of algorithmsA_ε,ε > 0 is an asymptotic polynomial time approximation schemeif

A_ε(I)≤(1+ε)OPT(I) +β_ε, for any instance I where βε is a constant that depends only on ε

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Online Packing Algorithms

I Incoming items appears one after the other, sequentially

I An incoming item must be packed when it arrives, without the knowledge of further items

I Once an item is packed, it cannot be repacked again.

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Online Packing Algorithms

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Online Packing Algorithms

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Online Circle Bin Packing

Example

9

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Online Circle Bin Packing

Example

9

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Online Circle Bin Packing

Example

9

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Online Circle Bin Packing

Example

9

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Online Circle Bin Packing

Example

9

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Online Circle Bin Packing

Example

9

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Online Circle Bin Packing

Example

9

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Online Circle Bin Packing

Example

9

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Online Circle Bin Packing

Example

9

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Online Circle Bin Packing

Example

9

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Preliminaries

Some Notation

I Iff :D →R is a numerical function, we may write

I f_e and f(e), indistictly

I f(S) as the valueP

e∈Df(e), there is no explicit definition

I Ifc is a circle and L= (c₁, . . . ,c_n) a list of circles, then

I c is also used to denote its radius

I ^c is the square with side lengths 2c

I L^ is the list L^ = (^c₁, . . . ,^c_n)

I max(L)is the maximum radiusof a circle in L

I Area(L) is the total areaof the circles inL

I Lis the list with |L| equal circles with radius max(L)

I Cis the set containingall lists of circlesfor the input problem

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Preliminaries

Some Notation

I Iff :D →R is a numerical function, we may write

I f_e and f(e), indistictly

I f(S) as the valueP

e∈Df(e), there is no explicit definition

I Ifc is a circle and L= (c₁, . . . ,c_n) a list of circles, then

I c is also used to denote its radius

I ^c is the square with side lengths 2c

I L^ is the list L^ = (^c₁, . . . ,^c_n)

I max(L)is the maximum radiusof a circle in L

I Area(L) is the total areaof the circles inL

I Lis the list with |L| equal circles with radius max(L)

I Cis the set containingall lists of circlesfor the input problem

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Preliminaries

IfE is a packing or other geometrical composition, E may be considered as the solid structure

I width(E) is the width of E

I height(E) is the height of E First considerations

I We consider a more general computational model

I Possible to operate over polynomial solutions

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Preliminaries

IfE is a packing or other geometrical composition, E may be considered as the solid structure

I width(E) is the width of E

I height(E) is the height of E First considerations

I We consider a more general computational model

I Possible to operate over polynomial solutions

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Basic Algorithms

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Area Lower Bound

I Circle Strip Packing with bin width 1

Opt

Area bound

I Circle Bin Packing with unit square bins

Optimum

Area Lower Bound

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Area based algorithms

Let

C the set containing all lists of circles, and Qthe set containing all lists of squares

next algorithm is a circle versionCA from square packing algorithmA

CA(L)

1. Let P^ ←A(^L).

2. Let P the packing P^ replacing c^_i by c_i. 3. Return P.

Lemma. If A is a square packing algorithm and α, β are constants, st. A(S)≤αArea(S)+β, for any S ∈ Q

CA(L)≤α4

πArea(L) +β, for any L∈ C

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Area based algorithms

Let

CA(L)

1. Let P^ ←A(^L).

CA(L)≤α4

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Area based algorithms

Let

CA(L)

1. Let P^ ←A(^L).

CA(L)≤α4

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Area based algorithms

Best possible density

Hexagonal Packing

I Density ^√^π

12 ≈0.9069

Lemma. There is no algorithm with approximation factor, based only on area arguments, better than

√12

π ≈1.10266

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Using square packing algorithms

I Round each circleci to a square c^i:

I Use square packing algorithms

I Area increasing: ^{Area(^}_Area(c^cⁱ⁾

i) = _π⁴ ≈1.27324

I Let L^ = (^c1, . . . ,^cn) the list L rounding each circle to a square

I Bounding the optimum with the area:

Area(^L) = 4

πArea(L)≤ 4

πOPT(L)

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Using square packing algorithms

i) = _π⁴ ≈1.27324

Area(^L) = 4

πArea(L)≤ 4

πOPT(L)

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Using square packing algorithms

i) = _π⁴ ≈1.27324

Area(^L) = 4

πArea(L)≤ 4

πOPT(L)

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Using square packing algorithms

i) = _π⁴ ≈1.27324

Area(^L) = 4

πArea(L)≤ 4

πOPT(L)

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Using square packing algorithms

i) = _π⁴ ≈1.27324

Area(^L) = 4

πArea(L)≤ 4

πOPT(L)

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Using square packing algorithms

Shelf Packing:

I Items are packed over shelves (of zero thickness)

I side by side in a leftmost way

I Items in a same shelf are packed at the same height.

I Items can be packed in a shelf S ifwidth(s) +width(S)≤1

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Using square packing algorithms

Shelf Packing:

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Using square packing algorithms

Shelf Packing:

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Using square packing algorithms

Shelf Packing:

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Using square packing algorithms

NFDH^s(L) #for Strip Packing

1. Sort L= (s₁, . . . ,s_n) st. s₁ ≥ · · · ≥s_n 2. For i ←1 to n:

3. Pack si into the last shelf,if possible 4. otherwise, packs_i in a new shelf on top of

the previous shelf or bin’s bottom (in case

there is no previous shelf) m^m+1

L_t L_k

L₁

1 L₂

s

Lemma. If L has only squares with side lengths at most 1/m NFDH^s(L)≤ ^m+1_m Area(L) +_m¹

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Using square packing algorithms

NFDH^s(L) #for Strip Packing

1. Sort L= (s₁, . . . ,s_n) st. s₁ ≥ · · · ≥s_n 2. For i ←1 to n:

3. Pack si into the last shelf,if possible 4. otherwise, packs_i in a new shelf on top of

the previous shelf or bin’s bottom (in case

there is no previous shelf) m^m+1

L_t L_k

L₁

1 L₂

s

Lemma. If L has only squares with side lengths at most 1/m NFDH^s(L)≤ ^m+1_m Area(L) +_m¹

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Using square packing algorithms

Sketch.

Each of the firstk−1 shelves have width filled by at least _m+1^m LetLt the first shelf having square with side≤1/(m +1)

m m+1 L_t

L_k

L₁

1 L₂

s

• L1,. . .,Lt−1: m squares of side > _m+1¹ , each.

• Lt, . . . ,Lk: width filled > 1−_m+1¹ = _m+1^m , otherwise receive another item.

Sliding up squares inL_i can cover all rectangular region of shelfLi+1, up to width _m+1^m .

(NFDH^s(L) −height(L1)) m

m +1 ≤Area(L) So,

NFDH^s(L)≤ m +1

m Area(L) +height(L1)≤ m +1

m Area(L) + 1 m

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Using square packing algorithms

Sketch.

m m+1 L_t

L_k

L₁

1 L₂

s

m +1 ≤Area(L) So,

NFDH^s(L)≤ m +1

m Area(L) + 1 m

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Using square packing algorithms

Sketch.

m m+1 L_t

L_k

L₁

1 L₂

s

m +1 ≤Area(L) So,

NFDH^s(L)≤ m +1

m Area(L) + 1 m

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Using square packing algorithms

Sketch.

m m+1 L_t

L_k

L₁

1 L₂

s

m +1 ≤Area(L) So,

NFDH^s(L)≤ m +1

m Area(L) + 1 m

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Using square packing algorithms

Sketch.

m m+1 L_t

L_k

L₁

1 L₂

s

m +1 ≤Area(L) So,

NFDH^s(L)≤ m +1

m Area(L) + 1 m

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Using square packing algorithms

Sketch.

m m+1 L_t

L_k

L₁

1 L₂

s

m +1 ≤Area(L) So,

NFDH^s(L)≤ m +1

m Area(L) + 1 m

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Using square packing algorithms

Let

C_m the set of lists with small circles (diam. ≤1/m, m integer) Corollary. If L∈ C_m, then

CNFDH^s(L)≤ m+1 m

4

πOPT(L) + 1 m ∀L Corollary. If L is a list of circles then

CNFDH^s(L)≤2.548 OPT(L) +1

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Using square packing algorithms

Let

C_m the set of lists with small circles (diam. ≤1/m, m integer) Corollary. If L∈ C_m, then

CNFDH^s(L)≤ m+1 m

4

πOPT(L) + 1 m ∀L Corollary. If L is a list of circles then

CNFDH^s(L)≤2.548 OPT(L) +1

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Rounding circles to circles

Algorithm EqualCircles(L) # all circles in Lhave a same size 1. Let P⁰ and P⁰⁰ packings of L as below

2. Return packing P ∈{P⁰,P⁰⁰}with minimum height.

Lemma. If all circles of L have radius r, then EqualCircles(L)≤1.654Area(L) +2r.

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Rounding circles to circles

Algorithm EqualCircles(L) # all circles in Lhave a same size 1. Let P⁰ and P⁰⁰ packings of L as below

2. Return packing P ∈{P⁰,P⁰⁰}with minimum height.

Lemma. If all circles of L have radius r, then EqualCircles(L)≤1.654Area(L) +2r.

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Rounding circles to circles

Idea: Circles with close radius are rounded up to the same radius Given L∈ C, L is the list with |L| circles with radius max(L) Algorithm A_ε(L)

1. δ← ^ε₆. 2. For i ≥0 do

3. L_i ←{r ∈L: _(1+δ)^1/2i+1 <r ≤ _(1+δ)^1/2 i}. 4. P_i ←EqualCircles(Li).

5. P ←P₀kP₁kP₂k. . . # concatenation of packings 6. Return P.

Theorem. Given ε >0, we have

A_ε(L)≤(1.654+ε)Area(L) +C_ε, for any L∈ C

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Rounding circles to circles

Idea: Circles with close radius are rounded up to the same radius Given L∈ C, L is the list with |L| circles with radius max(L) Algorithm A_ε(L)

1. δ← ^ε₆. 2. For i ≥0 do

3. L_i ←{r ∈L: _(1+δ)^1/2i+1 <r ≤ _(1+δ)^1/2 i}. 4. P_i ←EqualCircles(Li).

5. P ←P₀kP₁kP₂k. . . # concatenation of packings 6. Return P.

Theorem. Given ε >0, we have

A_ε(L)≤(1.654+ε)Area(L) +C_ε, for any L∈ C

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Online Circle Strip Packing

Online Packing

Baker, Schwarz’83: For 0<p <1, there exists online algorithm CNFS_p s.t.,

CNFS_p(L)≤ 2.548

p OPT(L) + 1

p(1−p), for any L∈ C

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Online Circle Strip Packing

Online Packing

CNFS_p(L)≤ 2.548

p OPT(L) + 1

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Online Circle Strip Packing

Online Packing

CNFS_p(L)≤ 2.548

p OPT(L) + 1

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Online Circle Strip Packing

Online Packing

CNFS_p(L)≤ 2.548

p OPT(L) + 1

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Online Circle Strip Packing

Online Packing

CNFS_p(L)≤ 2.548

p OPT(L) + 1

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Circle Bin Packing

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Rounding to squares

Adaptation of the strip packing version NFDH^s. NFDH^b(L) # For the bin packing version

1. SortL= (s₁, . . . ,s_n) st. s₁ ≥ · · · ≥s_n 2. For i ←1 to n:

3. Pack s_i in the last shelf (of the last bin), if possible 4. otherwise, packs_i in a new shelf at the top of the

previous shelf, if possible

5. otherwise, packs_i in a new shelf of a new bin.

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Rounding to squares

Meir, Moser’68. If all squares of L have side lengths at most _m¹ NFDH^b(L)≤ ^m+1_m 2

Area(L) +^m+2_m Proof: Exercise (analogous to the proof of NFDH^s)

Corollary. For any list L∈ C with diameters at most _m¹ CNFDH^b(L)≤ 4

π

m+1

m 2

Area(L)+m +2 m Corollary. For any list L∈ C

CNFDH^b(L)≤5.1OPT(L)+3

Corollary. As radius of circles decrease, the density of the packing is improved andCNFDH^b goes to 4/π≈1.27324.

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Rounding to squares

π

m+1

m 2

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Rounding to squares

π

m+1

m 2

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Rounding to squares

π

m+1

m 2

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Bounded Space Online Bin Packing

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Bounded Space Online Bin Packing

I Algorithms must be online

I At any moment, bins are classified as open or closed

I Only open bins can receive new items

I A bin starts open and once it became closed, it cannot be open again.

I The number of open bins is bounded by a constant

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Bounded Space Online Bin Packing

Related results with asymptotic approximation:

I Lee and Lee: Algorithm with factor 1.69103for 1-dimensional items and

showed that no algorithm can have better performance

I Epstein, van Stee’07: Algorithms with factors 2.3722for packing squares and

3.0672for packing cubes.

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Bounded Space Online Bin Packing

Related results with asymptotic approximation:

I Lee and Lee: Algorithm with factor 1.69103for 1-dimensional items and

showed that no algorithm can have better performance

I Epstein, van Stee’07: Algorithms with factors 2.3722for packing squares and

3.0672for packing cubes.

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Bounded Space Online Bin Packing

We will see

I Algorithm withasymptotic approximation factor 2.44

I Lower bound of 2.29

Techniques

I Weighting system to obtain approximation factors

I Specific algorithms to deal with big and small circles

I Grouping circles to consider as equal circles

I Geometric Partitionto combine items of the same type

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Bounded Space Online Bin Packing

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Packing equal circles

Find the largestρ^∗ st. k circles of radius ρ^∗ can be packed in a unit square

ρ^∗₁ =0.5 ρ^∗₂ =0.2928 ρ^∗₃ =0.2543 ρ^∗₄ =0.1963

ρ^∗₅ =0.2071 ρ^∗₆ =0.1876 ρ^∗₇ =0.1744 ρ^∗₈ =0.1705

Previous results: It is known the exact values ofρ^∗_n, for n ≤30 and good lower bounds for many.

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Packing equal circles

Find the largestρ^∗ st. k circles of radius ρ^∗ can be packed in a unit square

ρ^∗₁ =0.5 ρ^∗₂ =0.2928 ρ^∗₃ =0.2543 ρ^∗₄ =0.1963

ρ^∗₅ =0.2071 ρ^∗₆ =0.1876 ρ^∗₇ =0.1744 ρ^∗₈ =0.1705

Previous results: It is known the exact values ofρ^∗_n, for n ≤30 and good lower bounds for many.

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Packing big circles

Round up big circles to the nearest value of ρ (to bound the number of different circles)

I A circle is big if its radius is larger than 1/M

I Let ρi be the value of ρ^∗_i, when it is known, otherwise, the best known lower bound.

I Let K be such that ρ_K+1 ≤1/M < ρ_K

A circle r is of typei if:

I ρ_i₊₁ <r ≤ρ_i (for 1≤i <K)

I 1/M <r ≤ρK (for i =K)

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Packing big circles

I ρ_i+1 <r ≤ρ_i (for 1≤i <K)

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Packing big circles

I ρ_i+1 <r ≤ρ_i (for 1≤i <K)

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Packing big circles

I ρ_i+1 <r ≤ρ_i (for 1≤i <K)

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Packing big circles

I Let K be such that ρ_K+1 ≤1/M < ρ_K A circle r is of typei if:

I ρ_i+1 <r ≤ρ_i (for 1≤i <K)

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Packing big circles

I ρ_i+1 <r ≤ρ_i (for 1≤i <K)

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Packing big circles

I ρ_i+1 <r ≤ρ_i (for 1≤i <K)

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Packing big circles

I ρ_i+1 <r ≤ρ_i (for 1≤i <K)

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Packing big circles

I ρ_i+1 <r ≤ρ_i (for 1≤i <K)

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Packing big circles

I For1≤i ≤K, a c-bin of typei is a circular bin of radiusρ_i

I Circles of type i are packed in a c-bin of type i

I Packing in a c-bins of type 2

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Packing big circles

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Packing big circles

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Packing big circles

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Packing big circles

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Algorithm - Part 1

To pack a big circlec of type i : if there is no emptyc-bin of typei

close the current bin of type i (if any)

open a new bin of typei containing i c-binsof type i Packc into a emptyc-binof type i

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Small circles

Let C >0be an integer multiple of 3

A small circle of radiusr is of type i, subtype k if

I 1/(i+1)<C^kr ≤1/i

I where k is the largest integer such that C^kr ≤1/M

I and the circle is said to be of type (i,k)

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Small circles

1 M

1 MC

1 M+1

1 (M+1)C

1 M+2

1 (M+2)C

1 (MC−1)C

1 MC−1

1

MC2 1

(M+1)C2 1

(M+2)C3 1

(M−1)C3 1 MC

1 MC2

1 MC3

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Small circles

Subtype 1

Subtype 2

Subtype 3 Subtype 4 Subtype 0

Unit Bin

Subdivisions within a same type

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Sub-bins (h-bins and t-bins)

Idea: Round/Pack small circles into hexagonal bins h-bin of type (i,k):

I hexagonal bin of side length 2/(√

3C^ki)

I Receives a small circle of type (i,k) t-bin of type (i,k):

I trapezoidal bin obtained by the subdivision of a h-bin of type(i,k) in the center

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Sub-bins (h-bins and t-bins)

Idea: Round/Pack small circles into hexagonal bins h-bin of type (i,k):

I hexagonal bin of side length 2/(√

3C^ki)

I Receives a small circle of type (i,k) t-bin of type (i,k):

I trapezoidal bin obtained by the subdivision of a h-bin of type(i,k) in the center