1 / 13

Guide to Excel Sovler

Guide to Excel Sovler. TUTORIAL 1. What can Excel Solver do?. Linear Programming Nonlinear Programming Linear Regression Nonlinear Regression Optimal value of the objective function in a given interval

jake
Télécharger la présentation

Guide to Excel Sovler

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Guide to Excel Sovler TUTORIAL 1

  2. What can Excel Solver do? • Linear Programming • Nonlinear Programming • Linear Regression • Nonlinear Regression • Optimal value of the objective function in a given interval • Remark: Excel Solver can do more than things above, but some times it may not be accurate.

  3. Installing Excel Sovler STEP 1: You need an Microsoft Excel ~~ STEP 2: • Excel 2003 Tool Add-Ins Solver Add-in • Excel 2007/2010 File Options Add-Ins

  4. 1. choose Add-Ins 2. choose solver add-in 3. press go

  5. Newly Added Excel Solver

  6. Numerical Experiments • Step 1: randomly generate a group of linear data. • Step 2: Using three kind of method solving the problem via Excel Solver. 1)Least Square (L2-Min) 2)L1-Minimization 3)Linear Programming(Converted by L1-Min)

  7. Review of the settings • We have already observed a set of (), the assumption we made is, andfollows a certain kind of linear relationship, • Our goal is to find the appropriate which is the most accurate for all of the (). • How to define the accuracy?

  8. Formulations

  9. Formulations • Compare withthe advantage of is obvious. • The objective function of is quadratic form such that it is convex, smooth and differentiable. • From these characteristics, we can easily solve the unconstrained minimization problem by letting the first-order derivative of the objective function equal to zero.

  10. Formulations • The problem then convert to the following equations: Solving it, we can get the exact A and B.

  11. Formulations

  12. Data & Result

  13. L1/L2-Minimization& LP

More Related