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L13 Optimization using Excel See revised schedule read 8(1-4) + Excel “help” for Mar 12 Test Answers Review: Convex Prog. Prob. Worksheet modifications Excel optimization Summary 1
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Trendline in Excel 2 Excel help “trendline” for Wed
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Theorem 4.9 3 Given: S is convex if: 1. h i are linear 2. g j are convex i.e. H g PD or PSD When f(x) and S are convex= “convex programming problem”
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“Sufficient” Theorem 4.10, pg 165 4 The first-order KKT conditions are Necessary and Sufficient for a GLOBAL minimum….if: 1. f(x) is convex H f (x) Positive definite 2. x is defined as a convex feasible set S Equality constraints must be linear Inequality constraints must be convex HINT: linear functions are convex!
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Worksheet Modifications Naming cells Inserting shapes Inserting MS Equation “object” Recording macros Attaching a macro to a shape Creating a SOLVER hot button Visual basic, tools/references/solver 5
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6 Figure 6.1 Excel worksheet for finding roots of 2x/3 – sin x : (a) worksheet; (b) worksheet with formulas showing. Excel Applications
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Solver parameters 7 Figure 6.2 A Solver Parameters dialog box to define the problem.
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8 Figure 6.3 A Solver Results dialog box and the final worksheet.
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9 Figure 6.4 A Solver Answer Report for roots of 2x/3 – sin x = 0.
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10 Figure 6.5 Worksheet and Solver Parameters dialog box for KKT conditions for Example 4.31.
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11 Figure 6.6 Solver Results for KKT conditions for Example 4.31.
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KKT system of NL EQNs Prob 4.59 and 4.122 12
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13 Figure 6.7 Excel worksheet and Solver Parameters dialog box for unconstrained problem.
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Constrained Optimization Prob. 4.69 and 4.122 14
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Graphical Solution 15 2 1
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16 Figure 6.8 Excel worksheet for the linear programming problem.
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17 Figure 6.9 Solver Parameters dialog box for the linear programming problem.
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18 Figure 6.10 Solver Results dialog box for the linear programming problem.
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19 Figure 6.11 Answer Report from Solver for linear programming problem.
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20 Figure 6.12 Sensitivity Report from Solver for the linear programming problem.
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21 Figure 6.13 Excel worksheet for the spring design problem.
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Summary KKT pt from a Convex Prog. Prob. Is a global min! Use modifications for “ease of use” Pay attention to layout – Design variables – Parameters – Analysis/Performance “Variables” – Objective function – Constraints May need multiple starting points 22
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