Fat object (geometry)

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In geometry, a fat object is an object in two or more dimensions, whose lengths in the different dimensions are similar. For example, a square is fat because its length and width are identical. A 2-by-1 rectangle is thinner than a square, but it is fat relative to a 10-by-1 rectangle. Similarly, a circle is fatter than a 1-by-10 ellipse and an equilateral triangle is fatter than a very obtuse triangle.

Fat objects are especially important in computational geometry. Many algorithms in computational geometry can perform much better if their input consists of only fat objects; see the applications section below.

Global fatness[edit]

Given a constant R≥1, an object o is called R-fat if its "slimness factor" is at most R. The "slimness factor" has different definitions in different papers. A common definition[1] is:

where o and the cubes are d-dimensional. A 2-dimensional cube is a square, so the slimness factor of a square is 1 (since its smallest enclosing square is the same as its largest enclosed disk). The slimness factor of a 10-by-1 rectangle is 10. The slimness factor of a circle is √2. Hence, by this definition, a square is 1-fat but a disk and a 10×1 rectangle are not 1-fat. A square is also 2-fat (since its slimness factor is less than 2), 3-fat, etc. A disk is also 2-fat (and also 3-fat etc.), but a 10×1 rectangle is not 2-fat. Every shape is ∞-fat, since by definition the slimness factor is always at most ∞.

The above definition can be termed two-cubes fatness since it is based on the ratio between the side-lengths of two cubes. Similarly, it is possible to define two-balls fatness, in which a d-dimensional ball is used instead.[2] A 2-dimensional ball is a disk. According to this alternative definition, a disk is 1-fat but a square is not 1-fat, since its two-balls-slimness is √2.

An alternative definition, that can be termed enclosing-ball fatness (also called "thickness"[3]) is based on the following slimness factor:

The exponent 1/d makes this definition a ratio of two lengths, so that it is comparable to the two-balls-fatness.

Here, too, a cube can be used instead of a ball.

Similarly it is possible to define the enclosed-ball fatness based on the following slimness factor:

Enclosing-fatness vs. enclosed-fatness[edit]

The enclosing-ball/cube-slimness might be very different from the enclosed-ball/cube-slimness.

For example, consider a lollipop with a candy in the shape of a 1×1 square and a stick in the shape of a b×(1/b) rectangle (with b>1>(1/b)). As b increases, the area of the enclosing cube (≈b2) increases, but the area of the enclosed cube remains constant (=1) and the total area of the shape also remains constant (=2). Thus the enclosing-cube-slimness can grow arbitrarily while the enclosed-cube-slimness remains constant (=√2). See this GeoGebra page for a demonstration.

On the other hand, consider a rectilinear 'snake' with width 1/b and length b, that is entirely folded within a square of side length 1. As b increases, the area of the enclosed cube(≈1/b2) decreases, but the total areas of the snake and of the enclosing cube remain constant (=1). Thus the enclosed-cube-slimness can grow arbitrarily while the enclosing-cube-slimness remains constant (=1).

With both the lollipop and the snake, the two-cubes-slimness grows arbitrarily, since in general:

enclosing-ball-slimness ⋅ enclosed-ball-slimness = two-balls-slimness
enclosing-cube-slimness ⋅ enclosed-cube-slimness = two-cubes-slimness

Since all slimness factor are at least 1, it follows that if an object o is R-fat according to the two-balls/cubes definition, it is also R-fat according to the enclosing-ball/cube and enclosed-ball/cube definitions (but the opposite is not true, as exemplified above).

Balls vs. cubes[edit]

The volume of a d-dimensional ball of radius r is: , where Vd is a dimension-dependent constant:

A d-dimensional cube with side-length 2a has volume (2a)d. It is enclosed in a d-dimensional ball with radius a√d whose volume is Vd(a√d)d. Hence for every d-dimensional object:

enclosing-ball-slimness ≤ enclosing-cube-slimness ⋅ .

For even dimensions (d=2k), the factor simplifies to: . In particular, for two-dimensional shapes V2=π and the factor is: √(0.5 π)≈1.25, so:

enclosing-disk-slimness ≤ enclosing-square-slimness ⋅ 1.25

From similar considerations:

enclosed-cube-slimness ≤ enclosed-ball-slimness ⋅
enclosed-square-slimness ≤ enclosed-disk-slimness ⋅ 1.25

A d-dimensional ball with radius a is enclosed in a d-dimensional cube with side-length 2a. Hence for every d-dimensional object:

enclosing-cube-slimness ≤ enclosing-ball-slimness ⋅

For even dimensions (d=2k), the factor simplifies to: . In particular, for two-dimensional shapes the factor is: 2/√π≈1.13, so:

enclosing-square-slimness ≤ enclosing-disk-slimness ⋅ 1.13

From similar considerations:

enclosed-ball-slimness ≤ enclosed-cube-slimness ⋅
enclosed-disk-slimness ≤ enclosed-square-slimness ⋅ 1.13

Multiplying the above relations gives the following simple relations:

two-balls-slimness ≤ two-cubes-slimness ⋅ √d
two-cubes-slimness ≤ two-balls-slimness ⋅ √d

Thus, an R-fat object according to the either the two-balls or the two-cubes definition is at most Rd-fat according to the alternative definition.

Local fatness[edit]

The above definitions are all global in the sense that they don't care about small thin areas that are part of a large fat object.

For example, consider a lollipop with a candy in the shape of a 1×1 square and a stick in the shape of a 1×(1/b) rectangle (with b>1>(1/b)). As b increases, the area of the enclosing cube (=4) and the area of the enclosed cube (=1) remain constant, while the total area of the shape changes only slightly (=1+1/b). Thus all three slimness factors are bounded: enclosing-cube-slimness≤2, enclosed-cube-slimness≤2, two-cube-slimness=2. Thus by all definitions the lollipop is 2-fat. However, the stick-part of the lollipop obviously becomes thinner and thinner.

In some applications, such thin parts are unacceptable, so local fatness, based on a local slimness factor, may be more appropriate. For every global slimness factor, it is possible to define a local version. For example, for the enclosing-ball-slimness, it is possible to define the local-enclosing-ball slimness factor of an object o by considering the set B of all balls whose center is inside o and whose boundary intersects the boundary of o (i.e. not entirely containing o). The local-enclosing-ball-slimness factor is defined as:[3][4]

The 1/2 is a normalization factor that makes the local-enclosing-ball-slimness of a ball equal to 1. The local-enclosing-ball-slimness of the lollipop-shape described above is dominated by the 1×(1/b) stick, and it goes to ∞ as b grows. Thus by the local definition the above lollipop is not 2-fat.

Global vs. local definitions[edit]

Local-fatness implies global-fatness. Here is a proof sketch for fatness based on enclosing balls. By definition, the volume of the smallest enclosing ball is ≤ the volume of any other enclosing ball. In particular, it is ≤ the volume of any enclosing ball whose center is inside o and whose boundary touches the boundary of o. But every such enclosing ball is in the set B considered by the definition of local-enclosing-ball slimness. Hence:

enclosing-ball-slimnessd =
= volume(smallest-enclosing-ball)/volume(o)
≤ volume(enclosing-ball-b-in-B)/volume(o)
= volume(enclosing-ball-b-in-B)/volume(bo)
≤ (2 local-enclosing-ball-slimness)d

Hence:

enclosing-ball-slimness ≤ 2⋅local-enclosing-ball-slimness

For a convex body, the opposite is also true: local-fatness implies global-fatness. The proof[3] is based on the following lemma. Let o be a convex object. Let P be a point in o. Let b and B be two balls centered at P such that b is smaller than B. Then o intersects a larger portion of b than of B, i.e.:

Proof sketch: standing at the point P, we can look at different angles θ and measure the distance to the boundary of o. Because o is convex, this distance is a function, say r(θ). We can calculate the left-hand side of the inequality by integrating the following function (multiplied by some determinant function) over all angles:

Similarly we can calculate the right-hand side of the inequality by integrating the following function:

By checking all 3 possible cases, it is possible to show that always . Thus the integral of f is at least the integral of F, and the lemma follows.

The definition of local-enclosing-ball slimness considers all balls that are centered in a point in o and intersect the boundary of o. However, when o is convex, the above lemma allows us to consider, for each point in o, only balls that are maximal in size, i.e., only balls that entirely contain o (and whose boundary intersects the boundary of o). For every such ball b:

where is some dimension-dependent constant.

The diameter of o is at most the diameter of the smallest ball enclosing o, and the volume of that ball is: . Combining all inequalities gives that for every convex object:

local-enclosing-ball-slimness ≤ enclosing-ball-slimness

For non-convex objects, this inequality of course doesn't hold, as exemplified by the lollipop above.

Examples[edit]

The following table shows the slimness factor of various shapes based on the different definitions. The two columns of the local definitions are filled with "*" when the shape is convex (in this case, the value of the local slimness equals the value of the corresponding global slimness):

Shape two-balls two-cubes enclosing-ball enclosing-cube enclosed-ball enclosed-cube local-enclosing-ball local-enclosing-cube
square √2 1 √(π/2)≈1.25 1 √(4/π) ≈ 1.13 1 * *
b×a rectangle with b>a √(1+b^2/a^2) b/a 0.5√π(a/b+b/a)[3] √(b/a) 2√(b/aπ) √(b/a) * *
disk 1 √2 1 √(4/π)≈1.13 1 √(π/2)≈1.25 * *
ellipse with radii b>a b/a >b/a √(b/a) >√(b/2πa) √(b/a) >√(πb/a) * *
semi-ellipse with radii b>a, halved in parallel to b 2b/a >2b/a √(2b/a) >√(4ba) √(2b/a) >√(2πb/a) * *
semidisk 2 √5 √2 √(8/π)≈1.6 √2 √(5π/8)≈1.4 * *
equilateral triangle 1+2/√3≈2.15 √(π/√3)≈1.35 √(4/√3)≈1.52 √√3/2+1/√√3≈1.42 * *
isosceles right-angled triangle 1/(√2-1)≈2.4 2 √2 √2 * *
'lollipop' made of unit square and b×a stick, b>1>a b+1 √((b+1)^2/(ab+1)) √(ab+1) √(b/a)

Fatness of a triangle[edit]

Slimness is invariant to scale, so the slimness factor of a triangle (as of any other polygon) can be presented as a function of its angles only. The three ball-based slimness factors can be calculated using well-known trigonometric identities.

Enclosed-ball slimness[edit]

The largest circle contained in a triangle is called its incircle. It is known that:

where Δ is the area of a triangle and r is the radius of the incircle. Hence, the enclosed-ball slimness of a triangle is:

Enclosing-ball slimness[edit]

The smallest containing circle for an acute triangle is its circumcircle, while for an obtuse triangle it is the circle having the triangle's longest side as a diameter.[5]

It is known that:

where again Δ is the area of a triangle and R is the radius of the circumcircle. Hence, for an acute triangle, the enclosing-ball slimness factor is:

It is also known that:

where c is any side of the triangle and A,B are the adjacent angles. Hence, for an obtuse triangle with acute angles A and B (and longest side c), the enclosing-ball slimness factor is:

Note that in a right triangle, , so the two expressions coincide.

Two-balls slimness[edit]

The inradius r and the circumradius R are connected via a couple of formulae which provide two alternative expressions for the two-balls slimness of an acute triangle:[6]

For an obtuse triangle, c/2 should be used instead of R. By the Law of sines:

Hence the slimness factor of an obtuse triangle with obtuse angle C is:

Note that in a right triangle, , so the two expressions coincide.

The two expressions can be combined in the following way to get a single expression for the two-balls slimness of any triangle with smaller angles A and B:

To get a feeling of the rate of change in fatness, consider what this formula gives for an isosceles triangle with head angle θ when θ is small:


The following graphs show the 2-balls slimness factor of a triangle:

Fatness of circles, ellipses and their parts[edit]

The ball-based slimness of a circle is of course 1 - the smallest possible value.

For a circular segment with central angle θ, the circumcircle diameter is the length of the chord and the incircle diameter is the height of the segment, so the two-balls slimness (and its approximation when θ is small) is:

For a circular sector with central angle θ (when θ is small), the circumcircle diameter is the radius of the circle and the incircle diameter is the chord length, so the two-balls slimness is:

For an ellipse, the slimness factors are different in different locations. For example, consider an ellipse with short axis a and long axis b. the length of a chord ranges between at the narrow side of the ellipse and at its wide side; similarly, the height of the segment ranges between at the narrow side and at its wide side. So the two-balls slimness ranges between:

and:

In general, when the secant starts at angle Θ the slimness factor can be approximated by:[7]

Fatness of a convex polygon[edit]

A convex polygon is called r-separated if the angle between each pair of edges (not necessarily adjacent) is at least r.

Lemma: The enclosing-ball-slimness of an r-separated convex polygon is at most .[8]: 7–8 

A convex polygon is called k,r-separated if:

  1. It does not have parallel edges, except maybe two horizontal and two vertical.
  2. Each non-axis-parallel edge makes an angle of at least r with any other edge, and with the x and y axes.
  3. If there are two horizontal edges, then diameter/height is at most k.
  4. If there are two vertical edges, then diameter/width is at most k.

Lemma: The enclosing-ball-slimness of a k,r-separated convex polygon is at most .[9] improve the upper bound to .

Counting fat objects[edit]

If an object o has diameter 2a, then every ball enclosing o must have radius at least a and volume at least Vdad. Hence, by definition of enclosing-ball-fatness, the volume of an R-fat object with diameter 2a must be at least: Vdad/Rd. Hence:

Lemma 1: Let R≥1 and C≥0 be two constants. Consider a collection of non-overlapping d-dimensional objects that are all globally R-fat (i.e. with enclosing-ball-slimness ≤ R). The number of such objects of diameter at least 2a, contained in a ball of radius C⋅a, is at most:

For example (taking d=2, R=1 and C=3): The number of non-overlapping disks with radius at least 1 contained in a circle of radius 3 is at most 32=9. (Actually, it is at most 7).

If we consider local-fatness instead of global-fatness, we can get a stronger lemma:[3]

Lemma 2: Let R≥1 and C≥0 be two constants. Consider a collection of non-overlapping d-dimensional objects that are all locally R-fat (i.e. with local-enclosing-ball-slimness ≤ R). Let o be a single object in that collection with diameter 2a. Then the number of objects in the collection with diameter larger than 2a that lie within distance 2C⋅a from object o is at most:

For example (taking d=2, R=1 and C=0): the number of non-overlapping disks with radius larger than 1 that touch a given unit disk is at most 42=16 (this is not a tight bound since in this case it is easy to prove an upper bound of 5).

Generalizations[edit]

The following generalization of fatness were studied by [2] for 2-dimensional objects.

A triangle ∆ is a (β, δ)-triangle of a planar object o (0<β≤π/3, 0<δ< 1), if ∆ ⊆ o, each of the angles of ∆ is at least β, and the length of each of its edges is at least δ·diameter(o). An object o in the plane is (β,δ)-covered if for each point P ∈ o there exists a (β, δ)-triangle ∆ of o that contains P.

For convex objects, the two definitions are equivalent, in the sense that if o is α-fat, for some constant α, then it is also (β,δ)-covered, for appropriate constants β and δ, and vice versa. However, for non-convex objects the definition of being fat is more general than the definition of being (β, δ)-covered.[2]

Applications[edit]

Fat objects are used in various problems, for example:

  • Motion planning - planning a path for a robot moving amidst obstacles becomes easier when the obstacles are fat objects.[3]
  • Fair cake-cutting - dividing a cake becomes more difficult when the pieces have to be fat objects. This requirement is common, for example, when the "cake" to be divided is a land-estate.[10]
  • More applications can be found in the references below.

References[edit]

  1. ^ Katz, M. J. (1997). "3-D vertical ray shooting and 2-D point enclosure, range searching, and arc shooting amidst convex fat objects" (PDF). Computational Geometry. 8 (6): 299–316. doi:10.1016/s0925-7721(96)00027-2. S2CID 122806176., Agarwal, P. K.; Katz, M. J.; Sharir, M. (1995). "Computing depth orders for fat objects and related problems". Computational Geometry. 5 (4): 187. doi:10.1016/0925-7721(95)00005-8.
  2. ^ a b c Efrat, A.; Katz, M. J.; Nielsen, F.; Sharir, M. (2000). "Dynamic data structures for fat objects and their applications". Computational Geometry. 15 (4): 215. doi:10.1016/s0925-7721(99)00059-0.
  3. ^ a b c d e f Van Der Stappen, A. F.; Halperin, D.; Overmars, M. H. (1993). "The complexity of the free space for a robot moving amidst fat obstacles". Computational Geometry. 3 (6): 353. doi:10.1016/0925-7721(93)90007-s. hdl:1874/16650.
  4. ^ Berg, M.; Groot, M.; Overmars, M. (1994). "New results on binary space partitions in the plane (extended abstract)". Algorithm Theory — SWAT '94. Lecture Notes in Computer Science. Vol. 824. p. 61. doi:10.1007/3-540-58218-5_6. ISBN 978-3-540-58218-2., Van Der Stappen, A. F.; Overmars, M. H. (1994). "Motion planning amidst fat obstacles (extended abstract)". Proceedings of the tenth annual symposium on Computational geometry - SCG '94. p. 31. doi:10.1145/177424.177453. ISBN 978-0897916486. S2CID 152761., Overmars, M. H. (1992). "Point location in fat subdivisions". Information Processing Letters (Submitted manuscript). 44 (5): 261–265. doi:10.1016/0020-0190(92)90211-d. hdl:1874/17965., Overmars, M. H.; Van Der Stappen, F. A. (1996). "Range Searching and Point Location among Fat Objects". Journal of Algorithms. 21 (3): 629. doi:10.1006/jagm.1996.0063. hdl:1874/17327.
  5. ^ Rajan, V. T. (1994). "Optimality of the Delaunay triangulation in ". Discrete & Computational Geometry. 12 (2): 189–202. doi:10.1007/BF02574375. MR 1283887.
  6. ^ Weisstein, Eric W. "Inradius". MathWorld. Retrieved 28 September 2014.
  7. ^ See graph at: https://www.desmos.com/calculator/fhfqju02sn
  8. ^ Mark de Berg; Onak, Krzysztof; Sidiropoulos, Anastasios (2010). "Fat Polygonal Partitions with Applications to Visualization and Embeddings". Journal of Computational Geometry. 4. arXiv:1009.1866. doi:10.20382/jocg.v4i1a9. S2CID 15245776.
  9. ^ De Berg, Mark; Speckmann, Bettina; Van Der Weele, Vincent (2014). "Treemaps with bounded aspect ratio". Computational Geometry. 47 (6): 683. arXiv:1012.1749. doi:10.1016/j.comgeo.2013.12.008. S2CID 12973376.. Conference version: Convex Treemaps with Bounded Aspect Ratio (PDF). EuroCG. 2011.
  10. ^ Segal-Halevi, Erel; Nitzan, Shmuel; Hassidim, Avinatan; Aumann, Yonatan (2017). "Fair and square: Cake-cutting in two dimensions". Journal of Mathematical Economics. 70: 1–28. arXiv:1409.4511. doi:10.1016/j.jmateco.2017.01.007. S2CID 1278209.