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This website is designed to teach disciplined convex programming (DCP). DCP is a system for constructing mathematical expressions with known curvature from a given library of base functions. DCP is used by the convex optimization. To ensure that the specified optimization problems are convex. After reading about the DCP rules. Visit the DCP Quiz. To test your understanding, or type expressions into the DCP Analyzer. The following links offer more information on convex optimization and DCP:.

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This website is designed to teach disciplined convex programming (DCP). DCP is a system for constructing mathematical expressions with known curvature from a given library of base functions. DCP is used by the convex optimization. To ensure that the specified optimization problems are convex. After reading about the DCP rules. Visit the DCP Quiz. To test your understanding, or type expressions into the DCP Analyzer. The following links offer more information on convex optimization and DCP:.

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1 feedback

2 rules

3 functions

4 analyzer

5 quiz

6 disciplined convex programming

7 modeling languages cvx

8 and cvxpy

9 related links

10 convex optimization

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https://dcp.stanford.edu

This website is designed to teach disciplined convex programming (DCP). DCP is a system for constructing mathematical expressions with known curvature from a given library of base functions. DCP is used by the convex optimization. To ensure that the specified optimization problems are convex. After reading about the DCP rules. Visit the DCP Quiz. To test your understanding, or type expressions into the DCP Analyzer. The following links offer more information on convex optimization and DCP:.

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dcp.stanford.edu

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Home

http://dcp.stanford.edu/home

This website is designed to teach disciplined convex programming (DCP). DCP is a system for constructing mathematical expressions with known curvature from a given library of base functions. DCP is used by the convex optimization. To ensure that the specified optimization problems are convex. After reading about the DCP rules. Visit the DCP Quiz. To test your understanding, or type expressions into the DCP Analyzer. The following links offer more information on convex optimization and DCP:.

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About

http://dcp.stanford.edu/about

This website was created by Steven Diamond in 2013 with help from Eric Chu and Stephen Boyd. The source code for the website can be viewed at the links below:. Let us know how to improve the site!

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DCP Analyzer

http://dcp.stanford.edu/analyzer

Let us know how to improve the site! Follow the prompt in the box to see DCP analysis visualized as a tree. To modify the tree, click on any box containing an expression and edit the text.

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Functions

http://dcp.stanford.edu/functions

X in mathbf{R} $. Incr for $ x geq 0 $. Decr for $ x leq 0 $. Begin{cases} -x log (x) and x 0 0 and x = 0 end{cases} $. X geq 0 $. X in mathbf{R} $. Geo mean(x1,.,xk). X {1} cdots x {k}) {1/k} $. X {i} geq 0 $. Begin{cases} 2 x -1 and x ge 1 x {2} and x 1 end{cases} $. X in mathbf{R} $. Incr for $ x geq 0 $. Decr for $ x leq 0 $. X log (x/y)-x y $. Log sum exp(x1,.,xk). Log left(e {x {1} cdots e {x {k} right) $. X in mathbf{R} {k} $. Max(x1,.,xk). Max left { x {1}, ldots , x {k} right } $. Max {x,0 } $.

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DCP Quiz

http://dcp.stanford.edu/quiz

Let us know how to improve the site! Choose the button that matches the expression's curvature. The quiz difficulty will adjust to match your skill.

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julialang.org

Summer of Code Ideas Page

http://julialang.org/jsoc

Held on June 21. Google Summer of Code. Julia gets 12 slots in the Google Summer of Code! GSoC Project Ideas Page. Thanks for your interest in Summer of Code! We encourage you to send us a proposal through the GSoC website. You’re welcome to flesh out one of these projects with your own ideas, or to come up with something completely different. We also have some application guidelines. For more suggestions on writing a proposal. You can edit this page. If there’s no directly relevant package, you could al...

cvxpy.org

FAQ — CVXPY 0.4.3 documentation

http://www.cvxpy.org/en/latest/faq/index.html

Where can I get help with CVXPY? Where can I learn more about convex optimization? How do I know which version of CVXPY I’m using? What do I do if I get a. How do I find DCP errors? What do I do if I get a. What solvers does CVXPY support? What are the differences between CVXPY’s solvers? What do I do if I get “Exception: Cannot evaluate the truth value of a constraint”? What do I do if I get “RuntimeError: maximum recursion depth exceeded”? Can I use NumPy functions on CVXPY objects? How does CVXPY work?

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dcp.stanford.edu

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This website is designed to teach disciplined convex programming (DCP). DCP is a system for constructing mathematical expressions with known curvature from a given library of base functions. DCP is used by the convex optimization. To ensure that the specified optimization problems are convex. After reading about the DCP rules. Visit the DCP Quiz. To test your understanding, or type expressions into the DCP Analyzer. The following links offer more information on convex optimization and DCP:.

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