## ME 575 Optimization Basics

## Main.OptimizationBasics History

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- What resources (at least 3) did you find particularly useful or informative as you reviewed the tutorials, documentation, videos, or other online material?

- What resources (at least 3) did you find particularly useful or informative as you reviewed the tutorials, documentation, videos, or other online material? Please enter any that you'd like to share below in the comments section.

- Review information on optimization platforms such as (2 or 3 is fine):

- Review information on optimization platforms (2 or 3 is fine) such as:

- Complete the information sheet to tell me a little more about your background and interests

- Complete the information sheet to tell me a little more about your background and interests

- Think of an optimization problem. Define the following for this optimization problem of your choice:
- Objective
- Analysis variables (Parameters)
- Design variables (Variables)
- Equality constraints
- Inequality constraints
- Continuous variables
- Discrete variables

- Optimizers sometimes report that the optimization problem is infeasible. What does infeasible mean and how can it generally be corrected?
- Give the general form of the following types of problems and list at least one method that is used to solve them.
- Linear programming (LP)
- Quadratic programming (QP)
- Nonlinear programming (NLP)
- Mixed integer linear programming (MILP)
- Mixed integer nonlinear programming (MINLP)

- Think of an optimization problem. Define the following for this optimization problem of your choice:
- Objective
- Analysis variables (Parameters)
- Design variables (Variables)
- Equality constraints
- Inequality constraints
- Continuous variables
- Discrete variables

- Optimizers sometimes report that the optimization problem is infeasible. What does infeasible mean and how can it generally be corrected?
- Give the general form of the following types of problems and list at least one method that is used to solve them.
- Linear programming (LP)
- Quadratic programming (QP)
- Nonlinear programming (NLP)
- Mixed integer linear programming (MILP)
- Mixed integer nonlinear programming (MINLP)

- Define the following and tell how it is relevant to optimization:
- Equation residuals
- Global (vs. local) optimization techniques
- Lagrange multiplier
- Jacobian matrix
- Hessian matrix
- Sensitivity analysis
- Optimization under uncertainty

- Review information on optimization platforms such as (2 or 3 is fine):
- AIMMS
- AMPL
- APMonitor
- Frontline Excel Solver
- GAMS
- MATLAB Optimization Toolbox
- OptdesX - see OptdesX section
- Python Optimization Packages
- Additional tutorials available for the Two Bar Truss Problem

- What optimization software tutorials did you review?
- What resources (at least 3) did you find particularly useful or informative as you reviewed the tutorials, documentation, videos, or other online material?

- Define the following and tell how it is relevant to optimization:
- Equation residuals
- Global (vs. local) optimization techniques
- Lagrange multiplier
- Jacobian matrix
- Hessian matrix
- Sensitivity analysis
- Optimization under uncertainty

- Review information on optimization platforms such as (2 or 3 is fine):
- AIMMS
- AMPL
- APMonitor
- Frontline Excel Solver
- GAMS
- MATLAB Optimization Toolbox
- OptdesX - see OptdesX section
- Python Optimization Packages
- Additional tutorials available for the Two Bar Truss Problem

- What optimization software tutorials did you review?
- What resources (at least 3) did you find particularly useful or informative as you reviewed the tutorials, documentation, videos, or other online material?

This assignment can be completed as a collaborative assignment. Additional guidelines on individual, collaborative, and group assignments are provided under the Expectations link.

This assignment can be completed as a collaborative assignment. Additional guidelines on individual, collaborative, and group assignments are provided under the Expectations link.

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- Review information on optimization platforms such as:

- Review information on optimization platforms such as (2 or 3 is fine):

Example problems for each can be solved at the following links by selecting the run button (green arrow) and viewing the results in the window below.

Example problems for each can be solved at the following links by selecting the run button (green arrow) and viewing the results in the window below.

- Global (vs. local) optimization techniques
- Lagrange multiplier
- Jacobian matrix
- Hessian matrix
- Sensitivity analysis
- Optimization under uncertainty

- Global (vs. local) optimization techniques
- Lagrange multiplier
- Jacobian matrix
- Hessian matrix
- Sensitivity analysis
- Optimization under uncertainty

- Linear programming (LP)
- Quadratic programming (QP)
- Nonlinear programming (NLP)
- Mixed integer linear programming (MILP)
- Mixed integer nonlinear programming (MINLP)

- Linear programming (LP)
- Quadratic programming (QP)
- Nonlinear programming (NLP)
- Mixed integer linear programming (MILP)
- Mixed integer nonlinear programming (MINLP)

- Additional tutorial available under the next homework assignment for the Two Bar Truss

- Additional tutorials available for the Two Bar Truss Problem

- Additional tutorial available under the next homework assignment for the Two Bar Truss

- Define the following for an optimization problem of your choice:

- Think of an optimization problem. Define the following for this optimization problem of your choice:
- Objective

- Complete the [Main/InfoSheet | information sheet] to tell me a little more about your background and interests

- Complete the information sheet to tell me a little more about your background and interests

- Complete the [Main/InfoSheet | information sheet] to tell me a little more about your background and interests

- Review tutorials on optimization platforms such as:

- Review information on optimization platforms such as:

- How much time did you spend reviewing tutorials? _________ Hours

#### Optimization Basics

Review tutorials on optimization platforms such as:

- AIMMS
- AMPL
- APMonitor
- Frontline Excel Solver
- GAMS
- MATLAB Optimization Toolbox
- OptdesX - see OptdesX section
- Python Optimization Packages

Once you have done this, answer the following questions.

- Define the following types of parameters, variables, and equations
- Analysis variables
- Design variables

### Optimization Basics

- Define the following for an optimization problem of your choice:
- Analysis variables (Parameters)
- Design variables (Variables)

- Review tutorials on optimization platforms such as:
- AIMMS
- AMPL
- APMonitor
- Frontline Excel Solver
- GAMS
- MATLAB Optimization Toolbox
- OptdesX - see OptdesX section
- Python Optimization Packages

This assignment can be completed as a collaborative assignment. Additional guidelines on individual, collaborative, and group assignments are provided under the Expectations link.

- How much time did you spend reviewing tutorials? _________ Hours
- What optimization software tutorials did you review?

- How much time did you spend reviewing tutorials? _________ Hours
- What optimization software tutorials did you review?

Note: Each of the homework assignments are listed as either:

- Group
- Collaborative:
- Individual:

- Optimization under uncertainty

- Optimization under uncertainty

Note: Each of the homework assignments are listed as either:

- Group
- Collaborative:
- Individual:

This assignment can be completed as a collaborative assignment. Addition guidelines on individual, collaborative, and group assignments are provided under the Expectations link.

Note: Each of the assignments are listed as either:

Note: Each of the homework assignments are listed as either:

This assignment can be completed as a collaborative assignment.

This assignment can be completed as a collaborative assignment. Addition guidelines on individual, collaborative, and group assignments are provided under the Expectations link.

- Optimization under uncertainty

- Optimization under uncertainty

#### Optimization Basics

Note: Each of the assignments are listed as either:

- Group
- Collaborative:
- Individual:

This assignment can be completed as a collaborative assignment.

Attach:

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- How much time did you spend reviewing tutorials? _________ Hours
- What optimization software tutorials did you review?
- Define the following types of parameters, variables, and equations
- Analysis variables
- Design variables
- Equality constraints
- Inequality constraints
- Continuous variables
- Discrete variables

- Optimizers sometimes report that the optimization problem is infeasible. What does infeasible mean and how can it generally be corrected?
- Give the general form of the following types of problems and list at least one method that is used to solve them.
- Linear programming (LP)
- Quadratic programming (QP)
- Nonlinear programming (NLP)
- Mixed integer linear programming (MILP)
- Mixed integer nonlinear programming (MINLP)

- Define the following and tell how it is relevant to optimization:
- Equation residuals
- Global (vs. local) optimization techniques
- Lagrange multiplier
- Jacobian matrix
- Hessian matrix
- Sensitivity analysis
- Optimization under uncertainty

- See OptdesX Section

- OptdesX - see OptdesX section

(:title ME 575 Optimization Basics:) (:keywords nonlinear, optimization, engineering optimization, interior point, active set, differential, algebraic, modeling language, university course:) (:description Introductory assignment on Optimization Techniques in Engineering at Brigham Young University:)

Review tutorials on optimization platforms such as:

- AIMMS
- AMPL
- APMonitor
- Frontline Excel Solver
- GAMS
- MATLAB Optimization Toolbox
- See OptdesX Section
- Python Optimization Packages

Once you have done this, answer the following questions.

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