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Spring 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 11
ELEC 7770ELEC 7770Advanced VLSI DesignAdvanced VLSI Design
Spring 2008Spring 2008 Linear Programming – A Mathematical Linear Programming – A Mathematical
Optimization TechniqueOptimization Technique
Vishwani D. AgrawalVishwani D. AgrawalJames J. Danaher ProfessorJames J. Danaher Professor
ECE Department, Auburn University, Auburn, AL 36849ECE Department, Auburn University, Auburn, AL 36849
[email protected]@eng.auburn.eduhttp://www.eng.auburn.edu/~vagrawal/COURSE/E7770_Spr08/course.htmlhttp://www.eng.auburn.edu/~vagrawal/COURSE/E7770_Spr08/course.html
Spring 08, Feb 14Spring 08, Feb 14 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
method for selecting the best solution from the available solutions of a problem.
Method: State the problem and define variables whose
values will be determined. Develop a linear programming model:
Write the problem as an optimization formula (a linear expression to be minimized or maximized)
Write a set of linear constraints
An available LP solver (computer program) gives the values of variables.
Spring 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 33
Types of LPsTypes of LPs
LP – all variables are real. ILP – all variables are integers. MILP – some variables are integers, others are
real. A reference:
S. I. Gass, An Illustrated Guide to Linear Programming, New York: Dover, 1990.
Spring 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 44
A Single-Variable ProblemA Single-Variable Problem
Consider variable x Problem: find the maximum value of x subject to
constraint, 0 ≤ x ≤ 15. Solution: x = 15.
0 15
Constraint satisfied
x
Solution x = 15
Spring 08, Feb 14Spring 08, Feb 14 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: Maximize x, subject to following constraints:
x ≥ 0 (1) 5x ≤ 75 (2) 6x ≤ 30 (3) x ≤ 10 (4)
0 5 10 15x (1)
(2)(3)
(4)
All constraints satisfied
Solution, x = 5
Spring 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 66
A Two-Variable ProblemA Two-Variable Problem Manufacture of chairs and tables:
Resources available: Material: 400 boards of wood Labor: 450 man-hours
Profit: Chair: $45 Table: $80
Resources needed: Chair
5 boards of wood 10 man-hours
Table 20 boards of wood 15 man-hours
Problem: How many chairs and how many tables should be manufactured to maximize the total profit?
Spring 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 77
Formulating Two-Variable ProblemFormulating Two-Variable Problem
Manufacture x1 chairs and x2 tables to maximize profit:
P = 45x1 + 80x2 dollars
Subject to given resource constraints: 400 boards of wood, 5x1 + 20x2 ≤ 400 (1)
450 man-hours of labor, 10x1 + 15x2 ≤ 450 (2)
x1 ≥ 0 (3)
x2 ≥ 0 (4)
Spring 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 88
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
(3)(4)
Material constraint
Man-power constraint
Spring 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 99
Change Profit of Chair to $64/UnitChange Profit of Chair to $64/Unit
Manufacture x1 chairs and x2 tables to maximize profit:
P = 64x1 + 80x2 dollars
Subject to given resource constraints: 400 boards of wood, 5x1 + 20x2 ≤ 400 (1)
450 man-hours of labor, 10x1 + 15x2 ≤ 450 (2)
x1 ≥ 0 (3)
x2 ≥ 0 (4)
Spring 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1010
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
(3)(4)
Material constraint
Man-power constraint
Spring 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1111
A Dual ProblemA Dual Problem
Explore an alternative. Questions:
Should we make tables and chairs? Or, auction off the available resources?
To answer this question we need to know: What is the minimum price for the resources that will
provide us with same amount of revenue as the profits from tables and chairs?
This is the dual of the original problem.
Spring 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1212
Formulating the Dual ProblemFormulating the Dual Problem
Revenue received by selling off resources: For each board, w1
For each man-hour, w2
Minimize 400w1 + 450w2
Subject to constraints: 5w1 + 10w2 ≥ 45
20w1 + 15w2 ≥ 80
w1 ≥ 0
w2 ≥ 0
Spring 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1313
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 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1414
Primal-Dual ProblemsPrimal-Dual Problems Primal problem
Fixed resources Maximize profit
Variables: x1 (number of chairs) x2 (number of tables)
Maximize profit 45x1+80x2
Subject to: 5x1 + 20x2 ≤ 400 10x1 + 15x2 ≤ 450 x1 ≥ 0 x2 ≥ 0
Solution: x1 = 24 chairs, x2 = 14 tables Profit = $2200
Dual Problem Fixed profit Minimize value
Variables: w1 ($ value/board of wood) w2 ($ value/man-hour)
Minimize value 400w1+450w2
Subject to: 5w1 + 10w2 ≥ 45 20w1 + 15w2 ≥ 80 w1 ≥ 0 w2 ≥ 0
Solution: w1 = $1, w2 = $4 value = $2200
Spring 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1515
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 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1616
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 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1717
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 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1818
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 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1919
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 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2020
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 50-city problem: 49! = 6.08×1050-city problem: 49! = 6.08×1062 tours tours
Time for enumerative search assuming 1 Time for enumerative search assuming 1 μμs per tour s per tour evaluationevaluation == 1.93×101.93×1055 years years
Spring 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2121
A Greedy Heuristic SolutionA Greedy Heuristic Solution
City j = 1 j = 2 j = 3 j = 4 j = 5
i = 1
(start)0 18 10 12 27
i = 2 18 0 5 12 20
i = 3 10 5 0 15 19
i = 4 12 12 15 0 6
i = 5 27 20 19 6 0
Tour length = 10 + 5 + 12 + 6 + 27 = 60 miles (non-optimal)
Spring 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2222
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
Integer variables:xij = 1, travel i to jxij = 0, do not travel i to j
Real variables:dij = distance from i to j
Spring 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2323
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 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2424
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 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2525
Additional Constraints for Single TourAdditional Constraints for Single Tour
Following constraints prevent split tours. For any Following constraints prevent split tours. 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
Any subset
Remaining set At least two
arrows must crossthis boundary.
Spring 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2626
ILP SolutionILP Solution
1
32
5
4
d13 = 10
d41 = 12
Total length = 53
d54 = 6
d25 = 20
d32 = 5
Spring 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2727
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 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2828
Why ILP Solution is Exponential?Why ILP Solution is Exponential?
LP solutionfound inpolynomial time(bound on ILPsolution)
Must try all2n roundoffpoints
First variable
Se
con
d va
ria
ble
Constraints
Objective(maximize)
Spring 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2929
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 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 3030
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 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 3131
3V3F: A 3-Vector 3-Fault Example3V3F: A 3-Vector 3-Fault Example
fij i=1 i=2 i=3
j=1 1 1 0
j=2 0 1 1
j=3 1 0 1
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 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 3232
3V3F: Solution Space3V3F: Solution Space
Non-optimum solution
t1
t2
t3
1
1
1
1st LP solution(0.5, 0.5, 0.5)
ILP solutions(optimum)
Rounding and2nd ILP solution(1.0, 0.5, 0.5)
Rounding and3rd LP solution(1.0, 1.0, 0.0)
Spring 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 3333
3V3F: LP Relaxation and Rounding3V3F: LP Relaxation and RoundingILP – 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 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 3434
Recursive Rounding AlgorithmRecursive Rounding Algorithm
1.1. Obtain a relaxed LP solution. Obtain a relaxed LP solution. Stop 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 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 3535
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.
A solution that satisfies all constraints is A solution that satisfies all constraints is guaranteed; this solution is often close to optimal.guaranteed; this solution is often close to optimal.
Number of LPs, k, is the size of the final solution, Number of LPs, k, is the size of the final solution, i.e., the number of non-zero variables in the test i.e., 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 ), where k ≤ n, n is number of variables.k ≤ n, n is number of variables.
Spring 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 3636
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 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 3737
ILP vs. Recursive RoundingILP vs. Recursive Rounding
0 5,000 10,000 15,000 Vectors
100
75
50
25
0
ILP
Recursive Rounding
CPU
s
Spring 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 3838
N-Detect Tests (N = 5)N-Detect Tests (N = 5)
CircuitCircuit Unoptimized Unoptimized vectorsvectors
Relaxed LP/Recur. Relaxed LP/Recur. roundingrounding 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 08, Feb 14Spring 08, Feb 14 ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 3939
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 Modeling AMPL: A Modeling Language for Mathematical ProgrammingLanguage for Mathematical Programming, South San Francisco, , South San Francisco, California: Scientific Press, 1993. Several of programs described in California: Scientific Press, 1993. Several of programs described in this book are available to Auburn users.this book are available to Auburn users.
B. R. Hunt, R. L. Lipsman, J. M. Rosenberg, K. R. Coombes, J. E. B. R. Hunt, R. L. Lipsman, J. M. Rosenberg, K. R. Coombes, J. E. Osborn and G. J. Stuck, Osborn and G. J. Stuck, A Guide to MATLAB for Beginners and A Guide to MATLAB for Beginners and Experienced UsersExperienced Users, Cambridge University Press, 2006., Cambridge University Press, 2006.
Search the web. Many programs with small number of variables can Search the web. Many programs with small number of variables can be downloaded free.be downloaded free.