Markov decision process: Difference between revisions
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Optimizating of a quadratic function.12 | Optimizating of a quadratic function.12 | ||
Quadratic programming (QP) is the problem of optimizing a quadratic objective function and is one of the simplests form of non-linear programming.1 The objective function can contain bilinear or up to second order polynomial terms,2 and the constraints are linear and can be both equalities and inequalities. QP is widely used in image and signal processing, to optimize financial portfolios, to perform the least-squares method of regression, to control scheduling in chemical plants, and in sequential quadratic programming, a technique for solving more complex non-linear programming problems.3,4 The problem was first explored in the early 1950s, most notably by Princeton University's Wolfe and Frank, who developed its theoretical background,1 and by Harry Markowitz, who applied it to portfolio optimization, a subfield of finance.<references /> | Quadratic programming (QP) is the problem of optimizing a quadratic objective function and is one of the simplests form of non-linear programming.1 The objective function can contain bilinear or up to second order polynomial terms,2 and the constraints are linear and can be both equalities and inequalities. QP is widely used in image and signal processing, to optimize financial portfolios, to perform the least-squares method of regression, to control scheduling in chemical plants, and in sequential quadratic programming, a technique for solving more complex non-linear programming problems.3,4 The problem was first explored in the early 1950s, most notably by Princeton University's Wolfe and Frank, who developed its theoretical background,1 and by Harry Markowitz, who applied it to portfolio optimization, a subfield of finance. | ||
= Theory and Methodology = | |||
Optimizating of a quadratic function.12 | |||
= Numerical Example = | |||
Optimizating of a quadratic function.12 | |||
= Applications = | |||
Optimizating of a quadratic function.12 | |||
= Conclusion = | |||
Optimizating of a quadratic function.12 | |||
= References = | |||
Optimizating of a quadratic function.12 | |||
<references /> | |||
Revision as of 02:53, 25 November 2020
Author: Eric Berg (eb645)
Requirements:
- An introduction of the topic
- Theory, methodology, and/or algorithmic discussions
- At least one numerical example (step-by-step solution process, like
what you did in the HWs)
- A section to discuss and/or illustrate the applications
- A conclusion section
- References
Introduction
Optimizating of a quadratic function.12
Quadratic programming (QP) is the problem of optimizing a quadratic objective function and is one of the simplests form of non-linear programming.1 The objective function can contain bilinear or up to second order polynomial terms,2 and the constraints are linear and can be both equalities and inequalities. QP is widely used in image and signal processing, to optimize financial portfolios, to perform the least-squares method of regression, to control scheduling in chemical plants, and in sequential quadratic programming, a technique for solving more complex non-linear programming problems.3,4 The problem was first explored in the early 1950s, most notably by Princeton University's Wolfe and Frank, who developed its theoretical background,1 and by Harry Markowitz, who applied it to portfolio optimization, a subfield of finance.
Theory and Methodology
Optimizating of a quadratic function.12
Numerical Example
Optimizating of a quadratic function.12
Applications
Optimizating of a quadratic function.12
Conclusion
Optimizating of a quadratic function.12
References
Optimizating of a quadratic function.12