# Difference between revisions of "Garbage 3"

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=== Prerequisites === | === Prerequisites === | ||

+ | [[Math 290]], [[Math 313]], [[Math 314]]; concurrent with [[Math 334]], [[Math 344]], [[Math 321]] | ||

=== Minimal learning outcomes === | === Minimal learning outcomes === | ||

+ | Students will have a solid understanding of the concepts listed below. They will be able to prove many of the theorems that are central to this material. They will understand the model specifications for the optimization algorithms, and be able to recognize whether they apply to a given application or not. They will be able to perform the relevant computations on small, simple problems. They will be able to describe the optimization and approximation algorithms well enough that they could program simple versions of them, and will have a basic knowledge of the computational strengths and weaknesses of the algorithms covered. | ||

+ | |||

+ | # Complexity and Data | ||

+ | #* Asymptotic Analysis | ||

+ | #* Combinatorics | ||

+ | #* Graphs and Trees | ||

+ | #* Complexity (P, NP, NP Complete) | ||

+ | # Approximation Theory | ||

+ | #* Interpolation and Splines | ||

+ | #* Stone-Weierstrass Theorem | ||

+ | #* Bezier Curves | ||

+ | #* B-Splines | ||

+ | # Recursive Algorithms | ||

+ | #* Difference Calculus, including Summation by Parts | ||

+ | #* Simple linear recurrences | ||

+ | #* General linear recurrences | ||

+ | #* Generating functions | ||

+ | # Linear Optimization | ||

+ | #* Problem Formulation | ||

+ | #* Simplex Method | ||

+ | #* Duality | ||

+ | #* Applications | ||

+ | # Unconstrained Optimization | ||

+ | #* Steepest Descent | ||

+ | #* Newton | ||

+ | #* Broyden | ||

+ | #* Conjugate Gradient | ||

+ | #* Applications | ||

+ | # Constrained Optimization | ||

+ | #* Equality Constrained, Lagrange Multipliers | ||

+ | #* Inequality Constrained, KKT Condition | ||

+ | #* Applications | ||

+ | # Global Optimization | ||

+ | #* Interior Point Methods | ||

+ | #* Genetic Algorithms | ||

+ | #* Simulated Annealing | ||

+ | |||

=== Textbooks === | === Textbooks === |

## Revision as of 13:48, 6 June 2012

## Contents

## Catalog Information

### Title

Computation and Optimization 1

### (Credit Hours:Lecture Hours:Lab Hours)

(3:3:1)

### Offered

F

### Prerequisite

Math 290, Math 313, Math 314; concurrent with Math 334, Math 344, Math 321

### Description

A treatment of algorithms used to solve these problems. Specific topics include Complexity and Data, Approximation Theory, Recursive Algorithms, Linear Optimization, Unconstrained Optimization, Constrained Optimization, Global Optimization.

## Desired Learning Outcomes

### Prerequisites

Math 290, Math 313, Math 314; concurrent with Math 334, Math 344, Math 321

### Minimal learning outcomes

Students will have a solid understanding of the concepts listed below. They will be able to prove many of the theorems that are central to this material. They will understand the model specifications for the optimization algorithms, and be able to recognize whether they apply to a given application or not. They will be able to perform the relevant computations on small, simple problems. They will be able to describe the optimization and approximation algorithms well enough that they could program simple versions of them, and will have a basic knowledge of the computational strengths and weaknesses of the algorithms covered.

- Complexity and Data
- Asymptotic Analysis
- Combinatorics
- Graphs and Trees
- Complexity (P, NP, NP Complete)

- Approximation Theory
- Interpolation and Splines
- Stone-Weierstrass Theorem
- Bezier Curves
- B-Splines

- Recursive Algorithms
- Difference Calculus, including Summation by Parts
- Simple linear recurrences
- General linear recurrences
- Generating functions

- Linear Optimization
- Problem Formulation
- Simplex Method
- Duality
- Applications

- Unconstrained Optimization
- Steepest Descent
- Newton
- Broyden
- Conjugate Gradient
- Applications

- Constrained Optimization
- Equality Constrained, Lagrange Multipliers
- Inequality Constrained, KKT Condition
- Applications

- Global Optimization
- Interior Point Methods
- Genetic Algorithms
- Simulated Annealing

### Textbooks

Possible textbooks for this course include (but are not limited to):