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Sub Exponential Randomize Algorithm for Linear Programming. Paper by: Bernd G ä rtner and Emo Welzl Presentation by : Oz Lavee. Linear programming. The linear programming problem is a well known problem in computational geometry - PowerPoint PPT Presentation
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Sub ExponentialSub Exponential Randomize Algorithm for Linear Randomize Algorithm for Linear
ProgrammingProgramming
Paper by:
Bernd Gärtner and Emo Welzl
Presentation by :
Oz Lavee
Linear programmingLinear programming
The linear programming problem is a well known problem in computational geometry
The last decade brought a progress in the efficiency of the linear programming algorithms
Most of the algorithms were exponential in the dimension of the problem
Linear programmingLinear programming
The last progress is a randomized algorithm that solve linear programming problem with n inequalities and d variables (Rd ) in expected time of:
This algorithm that we will see is a combination of Matoušek and Kalai sub exponential bounds and Clarkson algorithms
Definition : Linear programming Definition : Linear programming problemproblem
Find a non negative vector x Rd that minimize cTx subject to n linear inequalities Ax b
where x 0C – d-vector represent directionX – d-vector A[n,d] – n inequalities over d variables
Example over RExample over R22
C
h1
h2
X
h1,h2 - inequalities
DefinitionsDefinitions
• let H be the set of n half spaces defined by Ax b
•Let H+ be the set of d halfspaces defined by X0
•For a G H H+ we define vG as the lexicographically minimal point x minimizing
cTx over hG h
Definitions : basisDefinitions : basis
•A set of halfspaces B H H+ is called a basis
if ,. vB is finite and for any subset B’ of B
vB’ < vB
•A basis of G is a minimal subset B G such that
vB = vG
Definitions : violationsDefinitions : violations
a constraint h H H+ is violated by G if and only if vG < vG {h}
h is violated by vG if h is violated by G
h1
h2
h3
G= {h1,h3}
h = h2
vG
Definitions : extremeDefinitions : extreme
a constraint h G is extreme in G if vG-{h} < vG
h1
h2
h3
G = {h1,h2,h3}
h2 is extreme vG
Lemma 1
1. For F G H H+ , vF < vG
2. vF,vG are finite and vF = vG .
h is violated by F if and only if
h is violated by G.
3. If vG is finite then any basis of G has exactly d constraints , and G has at the most d extreme constraints
The algorithmThe algorithm
Our algorithm is a combination of 3 algorithms that use each other:
1. Clarkson first algorithm (CL1) for n>>d 2. Clarkson second algorithm
(CL2) for 3. Subexponenial
algorithm(subex) for 6d2
CL1
CL2
subex
n>>d
nd
6d2
nd
Clarkson First Algorithm (CL1)Clarkson First Algorithm (CL1) set H of n constraints where n>>d
We choose a random sample R H
of size ,compute vR and the set V of constraints from H that are violated by vR
If V is not too large we add it to initially set G = H+ , choose another sample R and
compute VR G and so on…
nd
Clarkson First Algorithm (CL1)Clarkson First Algorithm (CL1) CL1 (H)
if n < 9d2 then
return CL2(H)
else
r = , G = H+
repeat
choose random
v = CL2 (R)
V = {h H | v violate h}
if then G = GV
until V = Ø
return v
nd
r
HR
nV 2
Two important factsTwo important facts
Fact 1: The expected size of V is no more than
The probability that |V| >
is at the most
The number of attempts to get a small enough V
expected to be at the most 2
n
n2
2
1
Three important factThree important fact
Fact 2:If any constraint is violated by v = vGR
then for any basis B of H there must be a constraint that is violated by v.
The number of expansion of G is at the most d
CL1 Run timeCL1 Run time
CL1 compute vH H+ with expected
o(d2n) operations and at the most 2d
calls to CL2 with constraints)( ndO
Clarkson Second Algorithm CL2Clarkson Second Algorithm CL2
This algorithm is very similar to the CL1
with the main different that instead of forcing V to be in the next iteration
We increase the probability of the elements
in v to be chosen for R in the next iteration
Clarkson Second Algorithm CL2Clarkson Second Algorithm CL2
We will use factor µ for each constraint that will be initialized to 1 .
For any constraint in V we will double his factor
For any basis B the elements of B increase so quickly that after a logarithmic time they will be chosen with high probability
Clarkson Second Algorithm CL2Clarkson Second Algorithm CL2
CL2 (H) if n 6d2 then
return subex(H) else
r = 6d2 repeat choose random v = subex (R)
V = {h H | v violate h}
if then for all h V do µh = 2µh until V = Ø return v
r
HR
)(3
1)( H
dV
Lemma 4Lemma 4
Let k be a positive integer then after kd
Successful Iterations we have
For any basis B of H
nekk
B3/
)(2
Run time CL2Run time CL2
Since and since 2 > e1/3
after big enough k 2k > nek/3
let k = 3ln(n)
2 k = n3ln2 > n2 = ne3ln(n)/3
There will be at the most 3dln(n) successful iterations
There will be at the most 6dln(n) iterations
nekk
B3/
)(2
Run time CL2Run time CL2
In each iteration there are O(dn) arithmetic
operations and one call to subex
Totally O(d2nlogn) operations and 6dln(n) calls to subex
The Sub Exponential Algorithm The Sub Exponential Algorithm (SUBEX)(SUBEX)
The idea :– H a group of n constraints .– Remove a random constraint h– Compute recursively vH-{h} – If h is not violated then done.– Else try again by removing
(hopefully different) constraintThe probability that h is violated is d/n
The Subex AlgorithmThe Subex Algorithm
In order to get efficiency we will pass to the subex procedure in addition to the set G of constraints a candidate basis
We assume that we have the following primitive procedures:– Basis(B,h) : calculate the basis of B {h}– Violation test– Calculate VB of basis B
The Subex AlgorithmThe Subex Algorithm
Subex(G,B)if G=B
return (VB,B) else choose random hG-B (v,B’) = Subex(G- {h},B)
if h violates v return Subex(G,basis(B’,h))
else return (v,B’)
The Subex AlgorithmThe Subex Algorithm
The number of steps is finite since:– The first recursive call decrease the number of
constraints– The second recursive call increase the value of
the temporal solution
Inductively it can be shown that the step keeps the correctness of the temporal solution
The Subex Algorithm Run TimeThe Subex Algorithm Run Time
The subex algorithm is computing vH H+
with We called the subex algorithm with 6d2
constraints
so the run time for each call to subex is
))(( )ln((2 ndOenddO
)ln()ln(3 )( ddOddO eedO
Total Run TimeTotal Run TimeThe runtime of cl2 =
O(d2nlogn + 6dln(n)Tsubex(6d2) =
The runtime of cl1 =
)loglog( )ln(2 nenndO ddO
))(2( 22 nddTndO CL
)()log(
)loglog()ln(2)ln(2
)ln(22
ddOddO
ddO
endOnendO
nenndndO
The Abstract FrameworkThe Abstract Framework
We can expand this algorithm to be used on larger range of problems.
Let H be a finite set
let (W{-},) linear ordered set of values
Let w : 2H W{-} value function
The Abstract FrameworkThe Abstract Framework
Our goal :
to find a minimal subset BH
where w(B) = w(H)
We can use the algorithm that we saw
in order to solve this problem if 2 axiom are satisfied.
The axiomsThe axioms
1. For any F ,G such that FG H
we have w(F)w(G) .(monotonicity)
2. For any FG H with w(F) = w(G)
if hH w(G) < w(G {h}) than
w(F) < w(F {h})
LP-type problemsLP-type problems
If those axioms hold for a definition of a problem we will call it
LP-type problem
From lemma 1 we can see that the linear programming problem is LP-type.
LP-type problemsLP-type problems
For the efficiency of the algorithm we need one more parameter for (H,w):
the maximum size of any basis of H which is referred to as the combinatorial dimension of (H,w) denote as
LP-type problemsLP-type problems
Any LP-type problem can be solved using the above algorithm but it is not necessarily
be in sub exponential time In order to have sub exponential time the
problem should have the property of basis regularity
Basis regularity – all the basis has exactly constraints
LP-type problems - ExamplesLP-type problems - Examples
Smallest closing ball- given a set of n points in Rd find the smallest closing ball (combinatorial dimension- d+1)
Polytope distance – given two polytopes P,Q.
compute pP q Q minimizing dist (p,q)(combinatorial dimension- d+2)
SummerySummery
We have seen a randomized sub exponential in d-space algorithm for the linear programming problem
We have seen the family of LP-type problems
ReferencesReferences
Linear-programming – Randomization and abstract frame work /
Bernd Gärtner and Emo welzl