vishwani d. agrawal james j. danaher professor ece department, auburn university auburn, al 36849
DESCRIPTION
ELEC 7770 Advanced VLSI Design Spring 2007 Linear Programming – A Mathematical Optimization Technique. Vishwani D. Agrawal James J. Danaher Professor ECE Department, Auburn University Auburn, AL 36849 [email protected] http://www.eng.auburn.edu/~vagrawal/COURSE/E7770_Spr07. - PowerPoint PPT PresentationTRANSCRIPT
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 11
ELEC 7770ELEC 7770Advanced VLSI DesignAdvanced VLSI Design
Spring 2007Spring 2007Linear Programming – A Mathematical Linear Programming – A Mathematical
Optimization TechniqueOptimization TechniqueVishwani D. AgrawalVishwani D. Agrawal
James J. Danaher ProfessorJames J. Danaher Professor
ECE Department, Auburn UniversityECE Department, Auburn University
Auburn, AL 36849Auburn, AL 36849
[email protected]@eng.auburn.edu
http://www.eng.auburn.edu/~vagrawal/COURSE/E7770_Spr07http://www.eng.auburn.edu/~vagrawal/COURSE/E7770_Spr07
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 22
What is Linear ProgrammingWhat is Linear Programming Linear programming (LP) is a mathematical Linear programming (LP) is a mathematical
method for selecting the best solution from the method for selecting the best solution from the available solutions of a problem.available solutions of a problem.
Method:Method: State the problem and define variables whose State the problem and define variables whose
values will be determined.values will be determined. Develop a linear programming model:Develop a linear programming model:
Write the problem as an optimization formula (a Write the problem as an optimization formula (a linear expression to be minimized or maximized)linear expression to be minimized or maximized)
Write a set of linear constraintsWrite a set of linear constraints
An available LP solver (computer program) gives An available LP solver (computer program) gives the values of variables.the values of variables.
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 33
Types of LPsTypes of LPs
LP – all variables are real.LP – all variables are real. ILP – all variables are integers.ILP – all variables are integers. MILP – some variables are integers, others are MILP – some variables are integers, others are
real.real. A reference:A reference:
S. I. Gass, An Illustrated Guide to Linear S. I. Gass, An Illustrated Guide to Linear Programming, New York: Dover, 1990.Programming, New York: Dover, 1990.
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 44
A Single-Variable ProblemA Single-Variable Problem
Consider variable xConsider variable x Problem: find the maximum value of x subject to Problem: find the maximum value of x subject to
constraint, 0 ≤ x ≤ 15.constraint, 0 ≤ x ≤ 15. Solution: x = 15.Solution: x = 15.
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 55
Single Variable Problem (Cont.)Single Variable Problem (Cont.) Consider more complex constraints:Consider more complex constraints: Maximize x, subject to following constraintsMaximize x, subject to following constraints
x ≥ 0x ≥ 0 (1)(1) 5x ≤ 755x ≤ 75 (2)(2) 6x ≤ 306x ≤ 30 (3)(3) x ≤ 10x ≤ 10 (4)(4)
0 5 10 15x (1)
(2)(3)
(4)
All constraints satisfied Solution, x = 5
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 66
A Two-Variable ProblemA Two-Variable Problem
Manufacture of xManufacture of x11 chairs and x chairs and x22 tables: tables:
Maximize profit, P = 45xMaximize profit, P = 45x11 + 80x + 80x22 dollars dollars
Subject to resource constraints:Subject to resource constraints:
400 boards of wood,400 boards of wood, 5x5x11 + 20x + 20x22 ≤ 400 ≤ 400 (1)(1)
450 man-hours of labor,450 man-hours of labor, 10x10x11 + 15x + 15x22 ≤ 450 ≤ 450 (2)(2)
xx11 ≥ 0 ≥ 0 (3)(3)
xx22 ≥ 0 ≥ 0 (4)(4)
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 77
Solution: Two-Variable ProblemSolution: Two-Variable Problem
Chairs, x1
Tab
les,
x2
(1)
(2)
0 10 20 30 40 50 60 70 80 90
40
30
20
10
0
(24, 14)
Profi
t increasing
decresing
P = 2200
P = 0
Best solution: 24 chairs, 14 tablesProfit = 45×24 + 80×14 = 2200 dollars
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 88
Change Chair Profit, $64/UnitChange Chair Profit, $64/Unit
Manufacture of xManufacture of x11 chairs and x chairs and x22 tables: tables:
Maximize profit, P = 64xMaximize profit, P = 64x11 + 80x + 80x22 dollars dollars
Subject to resource constraints:Subject to resource constraints:
400 boards of wood,400 boards of wood, 5x5x11 + 20x + 20x22 ≤ 400 ≤ 400 (1)(1)
450 man-hours of labor,450 man-hours of labor, 10x10x11 + 15x + 15x22 ≤ 450 ≤ 450 (2)(2)
xx11 ≥ 0 ≥ 0 (3)(3)
xx22 ≥ 0 ≥ 0 (4)(4)
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 99
Solution: $64 Profit/ChairSolution: $64 Profit/Chair
Chairs, x1
Tab
les,
x2
(1)
(2)
Profi
t increasing
decresing
P = 2880
P = 0
Best solution: 45 chairs, 0 tablesProfit = 64×45 + 80×0 = 2880 dollars
0 10 20 30 40 50 60 70 80 90
(24, 14)
40
30
20
10
0
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1010
Primal-Dual ProblemsPrimal-Dual Problems Primal problemPrimal problem
Fixed resourcesFixed resources Maximize profitMaximize profit
Variables:Variables: xx11 (number of chairs) (number of chairs) xx22 (number of tables) (number of tables)
Maximize profit 45xMaximize profit 45x11 + 80x + 80x22
Subject to:Subject to: 5x5x11 + 20x + 20x22 ≤ 400≤ 400 10x10x11 + 15x + 15x22 ≤≤ 450 450 xx11 ≥ 0≥ 0 xx22 ≥ 0≥ 0
Solution:Solution: xx11 = 24 chairs, x = 24 chairs, x22 = 14 tables = 14 tables Profit = $2200Profit = $2200
Dual ProblemDual Problem Fixed profitFixed profit Minimize costMinimize cost
Variables:Variables: ww11 ($ cost/board of wood) ($ cost/board of wood) ww22 ($ cost/man-hour) ($ cost/man-hour)
Minimize cost 400w1 + 450w2Minimize cost 400w1 + 450w2 Subject to:Subject to:
5w5w11 +10w +10w22 ≥ 45≥ 45 20w20w11 + 15w + 15w2 2 ≥ 80≥ 80 ww11 ≥ 0≥ 0 ww22 ≥ 0≥ 0
Solution:Solution: ww11 = $1, w = $1, w22 = $4 = $4 Cost = $2200Cost = $2200
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1111
The Duality TheoremThe Duality Theorem
If the primal has a finite optimal solution, so If the primal has a finite optimal solution, so does the dual, and the optimum values of the does the dual, and the optimum values of the objective functions are equal.objective functions are equal.
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1212
LP for LP for nn Variables Variables
n
minimize Σ cj xj Objective functionj =1 n
subject to Σ aij xj ≤ bi, i = 1, 2, . . ., m j =1
n
Σ cij xj = di, i = 1, 2, . . ., p j =1
Variables: xjConstants: cj, aij, bi, cij, di
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1313
Algorithms for Solving LPAlgorithms for Solving LP
Simplex methodSimplex method G. B. Dantzig, G. B. Dantzig, Linear Programming and ExtensionLinear Programming and Extension, Princeton, New , Princeton, New
Jersey, Princeton University Press, 1963.Jersey, Princeton University Press, 1963. Ellipsoid methodEllipsoid method
L. G. Khachiyan, “A Polynomial Algorithm for Linear Programming,” L. G. Khachiyan, “A Polynomial Algorithm for Linear Programming,” Soviet Math. DoklSoviet Math. Dokl., vol. 20, pp. 191-194, 1984.., vol. 20, pp. 191-194, 1984.
Interior-point methodInterior-point method N. K. Karmarkar, “A New Polynomial-Time Algorithm for Linear N. K. Karmarkar, “A New Polynomial-Time Algorithm for Linear
Programming,” Programming,” CombinatoricaCombinatorica, vol. 4, pp. 373-395, 1984., vol. 4, pp. 373-395, 1984. Course website of Prof. Lieven Vandenberghe (UCLA), Course website of Prof. Lieven Vandenberghe (UCLA),
http://www.ee.ucla.edu/ee236a/ee236a.htmlhttp://www.ee.ucla.edu/ee236a/ee236a.html
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1414
Basic Ideas of Solution methodsBasic Ideas of Solution methods
Constraints
Extreme points
Objective function Constraints
Extreme points
Objective function
Simplex: search on extreme points.Interior-point methods: Successively iterate with interior spaces of analytic convex boundaries.
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1515
Integer Linear Programming (ILP)Integer Linear Programming (ILP)
Variables are integers.Variables are integers. Complexity is exponential – higher than LP.Complexity is exponential – higher than LP. LP relaxationLP relaxation
Convert all variables to real, preserve ranges.Convert all variables to real, preserve ranges. LP solution provides guidance.LP solution provides guidance. Rounding LP solution can provide a non-optimal Rounding LP solution can provide a non-optimal
solution.solution.
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1616
Solving TSP: Five CitiesSolving TSP: Five CitiesDistances (dij) in miles (symmetric TSP, general TSP is asymmetric)
CityCity j=1j=1 j=2j=2 j=3j=3 j=4j=4 j=5j=5
i=1i=1 00 1818 1010 1212 2727
i=2i=2 1818 00 55 1212 2020
i=3i=3 1010 55 00 1515 1919
i=4i=4 1212 1212 1515 00 66
i=5i=5 2727 2020 1919 66 00
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1717
Search Space: No. of ToursSearch Space: No. of Tours
Asymmetric TSP toursAsymmetric TSP tours Five-city problem: 4 Five-city problem: 4 × 3 × 2 × 1 = 24 tours× 3 × 2 × 1 = 24 tours Nine-city problem: 362,880 toursNine-city problem: 362,880 tours 14-city problem: 87,178,291,200 tours14-city problem: 87,178,291,200 tours
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1818
A Greedy Heuristic SolutionA Greedy Heuristic Solution
CityCity j=1j=1 j=2j=2 j=3j=3 j=4j=4 j=5j=5
i=1i=1
(start)(start)00 1818 1010 1212 2727
i=2i=2 1818 00 55 1212 2020
i=3i=3 1010 55 00 1515 1919
i=4i=4 1212 1212 1515 00 66
i=5i=5 2727 2020 1919 66 00
Tour length = 10 + 5 + 12 + 6 + 27 = 60 miles (non-optimal)
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1919
ILP Variables, Constants and ConstraintsILP Variables, Constants and Constraints
1
32
5
4 d14 = 12
d15 = 27
d12 = 18
d13 = 10
x14 ε [0,1]
x15 ε [0,1]
x12 ε [0,1]
x13 ε [0,1]
x12 + x13 + x14 + x15 = 2 four other similar equations
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2020
Objective Function and ILP SolutionObjective Function and ILP Solution 5 i - 1
Minimize ∑ ∑ xij × dij i = 1 j = 1
xijxij j=1j=1 22 33 44 55
i=1i=1 00 00 11 00 00
22 11 00 00 00 00
33 00 11 00 00 00
44 00 00 00 00 11
55 00 00 00 11 00
∑ xij = 2 for all i j ≠ i
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2121
ILP SolutionILP Solution
1
32
5
4
d13 = 10
d45 = 6
Total length = 45 but not a single tour
d54 = 6
d21 = 18
d32 = 5
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2222
Additional Constraints for a Single TourAdditional Constraints for a Single Tour
Following constraints prevent subtours. For any Following constraints prevent subtours. For any subset S of cities, the tour must enter and exit subset S of cities, the tour must enter and exit that subset:that subset:
∑ xij ≥ 2 for all S, |S| < 5i ε S j ε S
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2323
ILP SolutionILP Solution
1
32
5
4
d13 = 10
d41 = 12
Total length = 53
d54 = 6
d25 = 20
d32 = 5
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2424
ILP Example: Test MinimizationILP Example: Test Minimization
A combinational circuit has n test vectors that detect m A combinational circuit has n test vectors that detect m faults. Each test detects a subset of faults. Find the faults. Each test detects a subset of faults. Find the smallest subset of test vectors that detects all m faults.smallest subset of test vectors that detects all m faults.
ILP model:ILP model: Assign an integer variable ti Assign an integer variable ti εε [0,1] to ith test vector such that ti = [0,1] to ith test vector such that ti =
1, if we select ti, otherwise ti= 0.1, if we select ti, otherwise ti= 0. Define an integer constant fij Define an integer constant fij εε [0,1] such that fij = 1, if ith vector [0,1] such that fij = 1, if ith vector
detects jth fault, otherwise fij = 0. Values of constants fij are detects jth fault, otherwise fij = 0. Values of constants fij are determined by fault simulation. determined by fault simulation.
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2525
Test Minimization by ILPTest Minimization by ILP
n
minimize Σ ti Objective function i=1 n
subject to Σ fij ti ≥ 1, j = 1, 2, . . ., mi=1
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2626
3V3F: A 3-Vector 3-Fault Example3V3F: A 3-Vector 3-Fault Example
fijfij i=1i=1 i=2i=2 i=3i=3
j=1j=1 11 11 00
j=2j=2 00 11 11
j=3j=3 11 00 11
Test vector i
Fau
lt j
Variables: t1, t2, t3 εε [0,1]
Minimize t1 + t2 + t3
Subject to:
t1 + t2 ≥ 1
t2 + t3 ≥ 1
t1 + t3 ≥ 1
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2727
3V3F: Solution Space3V3F: Solution Space
Non-optimum solution
t1
t2
t3
1
1
1
LP solution(0.5, 0.5, 0.5) and recursive rounding
ILP solutions(optimum)
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2828
Characteristics of ILPCharacteristics of ILP Worst-case complexity is exponential in number of Worst-case complexity is exponential in number of
variables.variables. Linear programming (LP) relaxation, where integer Linear programming (LP) relaxation, where integer
variables are treated as real, gives a lower bound on the variables are treated as real, gives a lower bound on the objective function.objective function.
Recursive roundingRecursive rounding of relaxed LP solution to nearest of relaxed LP solution to nearest integers gives an approximate solution to the ILP integers gives an approximate solution to the ILP problem.problem. K. R. Kantipudi and V. D. Agrawal, “A Reduced Complexity K. R. Kantipudi and V. D. Agrawal, “A Reduced Complexity
Algorithm for Minimizing Algorithm for Minimizing NN-Detect Tests,” -Detect Tests,” Proc. 20Proc. 20thth International International Conf. VLSI DesignConf. VLSI Design, January 2007, pp. 492-497., January 2007, pp. 492-497.
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2929
3V3F: LP Relaxation and Rounding3V3F: LP Relaxation and Rounding
ILP – Variables: t1, t2, t3 εε [0,1]
Minimize t1 + t2 + t3
Subject to:
t1 + t2 ≥ 1
t2 + t3 ≥ 1
t1 + t3 ≥ 1
LP relaxation: t1, t2, t3 εε (0.0, 1.0)
Solution: t1 = t2 = t3 = 0.5
Recursive rounding:
(1) round one variable, t1 = 1.0 Two-variable LP problem: Minimize t2 + t3 subject to t2 + t3 ≥ 1.0 LP solution t2 = t3 = 0.5
(2) round a variable, t2 = 1.0 ILP constraints are satisfied solution is t1 = 1, t2 = 1, t3 = 0
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 3030
Recursive Rounding AlgorithmRecursive Rounding Algorithm
1.1. Obtain a relaxed LP solution. Obtain a relaxed LP solution. StopStop if each if each variable in the solution is an integer.variable in the solution is an integer.
2.2. Round the variable closest to an integer.Round the variable closest to an integer.
3.3. Remove any constraints that are now Remove any constraints that are now unconditionally satisfied.unconditionally satisfied.
4.4. Go to step 1.Go to step 1.
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 3131
Recursive RoundingRecursive Rounding
ILP has exponential complexity.ILP has exponential complexity. Recursive rounding:Recursive rounding:
ILP is transformed into k LPs with progressively ILP is transformed into k LPs with progressively reducing number of variables.reducing number of variables.
Number of LPs, k, is the size of the final solution, i.e., Number of LPs, k, is the size of the final solution, i.e., the number of non-zero variables in the test the number of non-zero variables in the test minimization problem.minimization problem.
Recursive rounding complexity is k × O(nRecursive rounding complexity is k × O(npp), where k ≤ ), where k ≤ n, n is number of variables.n, n is number of variables.
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 3232
Four-Bit ALU CircuitFour-Bit ALU Circuit
Initial Initial vectorsvectors
ILPILP Recursive roundingRecursive rounding
VectorsVectors CPU sCPU s VectorsVectors CPU sCPU s
285285 1414 0.650.65 1414 0.420.42
400400 1313 1.071.07 1313 1.001.00
500500 1212 4.384.38 1313 3.003.00
1,0001,000 1212 4.174.17 1212 3.003.00
5,0005,000 1212 12.9512.95 1212 9.009.00
10,00010,000 1212 34.6134.61 1212 17.017.0
16,38416,384 1212 87.4787.47 1212 37.037.0
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 3333
CPU Time: ILP vs. Recursive RoundingCPU Time: ILP vs. Recursive Rounding
0 5,000 10,000 15,000 Vectors
100
75
50
25
0
ILP
Recursive Rounding
CPU
s
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 3434
N-Detect Tests (N = 5)N-Detect Tests (N = 5)
CircuitCircuit Unoptimized Unoptimized vectorsvectors
Relaxed LP/Recur. roundingRelaxed LP/Recur. rounding ILP (exact)ILP (exact)
Lower Lower boundbound
Min. Min. vectorsvectors CPU sCPU s Min. Min.
vectorsvectors CPU sCPU s
c432c432 608608 196.38196.38 197197 1.01.0 197197 1.01.0
c499c499 379379 260.00260.00 260260 1.21.2 260260 2.32.3
c880c880 1,0231,023 125.97125.97 128128 14.014.0 127127 881.8881.8
c1355c1355 755755 420.00420.00 420420 3.23.2 420420 4.44.4
c1908c1908 1,0551,055 543.00543.00 543543 4.64.6 543543 6.96.9
c2670c2670 959959 477.00477.00 477477 4.74.7 477477 7.27.2
c3540c3540 1,9711,971 467.25467.25 477477 72.072.0 471471 20008.520008.5
c5315c5315 1,0791,079 374.33374.33 377377 18.018.0 376376 40.740.7
c6288c6288 243243 52.5252.52 5757 39.039.0 5757 34740.034740.0
c7552c7552 2,1652,165 841.00841.00 841841 52.052.0 841841 114.3114.3
Spring 07, Mar 13, 15Spring 07, Mar 13, 15 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 3535
Finding LP/ILP SolversFinding LP/ILP Solvers
R. Fourer, D. M. Gay and B. W. Kernighan, R. Fourer, D. M. Gay and B. W. Kernighan, AMPL: A AMPL: A Modeling Language for Mathematical ProgrammingModeling Language for Mathematical Programming, , South San Francisco, California: Scientific Press, 1993. South San Francisco, California: Scientific Press, 1993. Several of programs described in this book are available Several of programs described in this book are available to Auburn users.to Auburn users.
MATLAB?MATLAB? Search the web. Many programs with small number of Search the web. Many programs with small number of
variables can be downloaded free.variables can be downloaded free.