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Warm-up: What to eat?

Warm-up: What to eat?. We are trying healthy by finding the optimal amount of food to purchase. We can choose the amount of stir-fry (ounce) and boba (fluid ounces). Healthy Squad Goals 2000 Calories 2500 Sugar 100 g Calcium 700 mg.

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Warm-up: What to eat?

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  1. Warm-up: What to eat? We are trying healthy by finding the optimal amount of food to purchase. We can choose the amount of stir-fry (ounce) and boba (fluid ounces). Healthy Squad Goals 2000 Calories 2500 Sugar 100 g Calcium 700 mg What is the cheapest way to stay “healthy” with this menu? How much stir-fry (ounce) and boba (fluid ounces) should we buy?

  2. Announcements Assignments: HW3 (online) • Due Wed 2/6, 10 pm P1: Search & Games • Due Thu 2/7, 10 pm HW4 (written) • Released Wed 2/6 • Due Tue 2/12, 10 pm P2: Optimization • Released later this week • Due Thu 2/21, 10 pm

  3. Announcements Midterm 1 Exam Mon 2/18, in class

  4. AAAI Conference Honolulu, HI

  5. AI: Representation and Problem Solving Linear Programming Instructors: Pat Virtue & Stephanie Rosenthal Slide credits: CMU AI, http://ai.berkeley.edu

  6. Warm-up: What to eat? We are trying healthy by finding the optimal amount of food to purchase. We can choose the amount of stir-fry (ounce) and boba (fluid ounces). Healthy Squad Goals 2000 Calories 2500 Sugar 100 g Calcium 700 mg What is the cheapest way to stay “healthy” with this menu? How much stir-fry (ounce) and boba (fluid ounces) should we buy?

  7. Optimization Graphical Representation Problem Description Optimization Representation s.t.

  8. Warm-up: What to eat? We are trying healthy by finding the optimal amount of food to purchase. We can choose the amount of stir-fry (ounce) and boba (fluid ounces). Healthy Squad Goals 2000 Calories 2500 Sugar 100 g Calcium 700 mg What is the cheapest way to stay “healthy” with this menu? How much stir-fry (ounce) and boba (fluid ounces) should we buy?

  9. Constraint Satisfaction Problems Map coloring s.t.

  10. Constraint Satisfaction Problems Map coloring s.t. Notation Alert!

  11. What to eat? We are trying healthy by finding the optimal amount of food to purchase. We can choose the amount of stir-fry (ounce) and boba (fluid ounces). Healthy Squad Goals 2000 Calories 2500 Sugar 100 g Calcium 700 mg What is the cheapest way to stay “healthy” with this menu? How much stir-fry (ounce) and boba (fluid ounces) should we buy?

  12. Optimization Formulation Diet Problem s.t. Healthy Squad Goals 2000 Calories 2500 Sugar 100 g Calcium 700 mg Notation Alert!

  13. Optimization Formulation Diet Problem Objective s.t. Healthy Squad Goals 2000 Calories 2500 Sugar 100 g Calcium 700 mg Notation Alert!

  14. Optimization Formulation Diet Problem s.t. calcium Healthy Squad Goals 2000 Calories 2500 Sugar 100 g Calcium 700 mg

  15. Optimization Formulation Diet Problem s.t. Healthy Squad Goals 2000 Calories 2500 Sugar 100 g Calcium 700 mg Notation Alert!

  16. Optimization Formulation Diet Problem s.t. Cost Stir-fry Boba Limit Calorie min Calorie max Sugar Notation Alert! Calcium

  17. Optimization Formulation Diet Problem s.t. Cost Stir-fry Boba Limit Calorie min Calorie max Sugar Notation Alert! Calcium

  18. Optimization Formulation Diet Problem s.t. Cost Stir-fry Boba Limit Calorie min Calorie max Sugar Calcium

  19. Optimization Formulation Diet Problem s.t. Cost Stir-fry Boba Limit Calorie min Calorie max Sugar Calcium

  20. Optimization Formulation Diet Problem s.t. Cost Stir-fry Boba Limit Calorie min Calorie max Sugar Notation Alert! Calcium

  21. Piazza Poll 1 What has to increase to add more nutrition constraints? s.t. Select all that apply length length height width length

  22. Piazza Poll 1 What has to increase to add more nutrition constraints? s.t.

  23. Piazza Poll 2 What has to increase to add more menu items? s.t. Select all that apply length length height width length

  24. Piazza Poll 2 What has to increase to add more nutrition constraints? s.t.

  25. Question If , which of the following also equals ? s.t. Select all that apply length length length Notation Alert!

  26. Linear Programming Linear objective with linear constraints s.t. As opposed to general optimization s.t.

  27. Linear Programming Different formulations Inequality form General form Standard form s.t. s.t. s.t. A Important to pay attention to form!

  28. Linear Programming Different formulations Inequality form General form Standard form s.t. s.t. s.t. A Can switch between formulations!

  29. Optimization Graphical Representation Problem Description Optimization Representation s.t.

  30. Graphics Representation Geometry / Algebra I Quiz What shape does this inequality represent?

  31. Graphics Representation Geometry / Algebra I Quiz What shape does this inequality represent?

  32. Piazza Poll 3 What is the relationship between the half plane: and the vector:

  33. Piazza Poll 4 Given the cost vector and initial point , Which unit vector step will cause to have the lowest cost ? Notation Alert!

  34. Cost Contours Given the cost vector where will = 0 ?

  35. Cost Contours Given the cost vector where will = 0 ? = 1 ? = 2 ? = -1 ? = -2 ?

  36. LP Graphical Representation Inequality form s.t.

  37. LP Graphical Representation Inequality form, with no constraints

  38. LP Graphical Representation Inequality form, with no constraints s.t.

  39. Piazza Poll 5 True or False: An minimizing LP with exactly on constraint, will always have a minimum objective at . s.t.

  40. Optimization Graphical Representation Problem Description Optimization Representation s.t.

  41. Warm-up: What to eat? We are trying healthy by finding the optimal amount of food to purchase. We can choose the amount of stir-fry (ounce) and boba (fluid ounces). Healthy Squad Goals 2000 Calories 2500 Sugar 100 g Calcium 700 mg What is the cheapest way to stay “healthy” with this menu? How much stir-fry (ounce) and boba (fluid ounces) should we buy?

  42. Optimization Formulation Diet Problem s.t. Cost Stir-fry Boba Limit Calorie min Calorie max Sugar Calcium

  43. Solving an LP Solutions are at feasible intersections of constraint boundaries!!

  44. Solving an LP Solutions are at feasible intersections of constraint boundaries!!

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