I love this book. The attraction comes, I think, partly from the beauty of the subject, but also partly from the clean way in which Strang packages all of his mathematical expositions. I spent many a nerdy weekend morning with this book and the OCW lectures. These follow the book very closely and are indispensable—Strang is an even better lecturer than he is a writer.
Introduction to Linear Programming. You may recall unconstrained optimization from your high school years: Introduction to Linear Programming You may recall unconstrained optimization from your high school years: For optimization to be required, there must be more than one solution available. Some kind of optimization minimum process is then required in order to choose point the very best solution from among those available.
What is meant by best depends on the problem at hand: Simple unconstrained delivery route design. Linear programming LP is the most commonly applied form of constrained optimization. Constrained optimization is much harder than unconstrained optimization: For example, you must guarantee that the optimum point does not have a value above or below a prespecified limit when substituted into a given constraint function.
The constraints usually relate to limited resources. The best solution might occur half way up a peak when a constraint prohibits movement farther up. The main elements of any constrained optimization problem are: The values of the variables are not known when you start the problem.
The variables usually represent things that you can adjust or control, for example the rate at which to manufacture items.
The goal is to find values of the variables that provide the best value of the objective function. This is a mathematical expression that combines the variables to express your goal. It may represent profit, for example. You will be required to either maximize or minimize the objective function.
These are mathematical expressions that combine the variables to express limits on the possible solutions. For example, they may express the idea that the number of workers available to operate a particular machine is limited, or that only a certain amount of steel is available per day.
Only rarely are the variables in an optimization problem permitted to take on any value from minus infinity to plus infinity. Instead, the variables usually have bounds. For example, zero and might bound the production rate of widgets on a particular machine.
In linear programming LPall of the mathematical expressions for the objective function and the constraints are linear. An amazing range of problems can be modeled using linear programming, everything from airline scheduling to least-cost petroleum processing and distribution.
LP is very widely used. Linear programming is by far the most widely used method of constrained optimization. The largest optimization problems in the world are LPs having millions of variables and hundreds of thousands of constraints.
With recent advances in both solution algorithms and computer power, these large problems can be solved in practical amounts of time. Of course, there are also many problems for which LP is not appropriate, and part of the job for this textbook is to help you decide when to use LP and the other techniques covered here, and when not to use them.
The Acme Bicycle Company produces two kinds of bicycles by hand: Acme wishes to determine the rate at which each type of bicycle should be produced in order to maximize the profits on the sales of the bicycles.
Acme assumes that it can sell all of the bicycles produced. The physical data on the production process is available from the company engineer.
A different team produces each kind of bicycle, and each team has a different maximum production rate: Producing a bicycle of either type requires the same amount of time on the metal finishing machine a production bottleneckand this machine can process at most a total of 4 bicycles per day, of either type.Chapter 6 Introduction to Linear models A statistical model is an expression that attempts to explain patterns in the observed values of a response variable by relating the response variable to a set of predictor variables and parameters.
Chapter 2: Introduction to Linear Programming You may recall unconstrained optimization from your high school years: the idea is to find the highest point (or perhaps the .
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