cvxgen.com
CVXGEN: Code Generation for Convex Optimization
http://www.cvxgen.com/docs/matlab_interface.html
CVXGEN: Code Generation for Convex Optimization. Using the Matlab interface. CVXGEN creates a Matlab MEX interface for use with each custom solver, making it easy to test and use high-speed solvers in simulations and data analysis. The easiest way to use this interface is via the ‘Matlab’ screen in CVXGEN's online interface. Just copy and paste the given two lines of code into Matlab. This will perform the following steps, which you can also do manually. Folder in Matlab, call. This will use the. Nonnega...
cvxgen.com
CVXGEN: Code Generation for Convex Optimization
http://www.cvxgen.com/docs/c_interface.html
CVXGEN: Code Generation for Convex Optimization. Using the C interface. For the most flexible and best performing solver, use C directly. Once you have generated code online, do the following. Download and extract the ‘cvxgen.zip’ archive for your problem. This will create a subdirectory called. CVXGEN automatically creates random example data, (which may not be a feasible, unbounded problem instance). Use this to test compilation and basic operation, by typing. Check several iterations occur. No other s...
cvxgen.com
CVXGEN: Code Generation for Convex Optimization
http://www.cvxgen.com/docs/dimensions.html
CVXGEN: Code Generation for Convex Optimization. Dimensions may be used anywhere in the problem specification in place of integers. This removes ‘magic numbers’ from the dimensions of variables and parameters (for example), and makes it easy to adjust the size of a problem. The left-hand side must consist only of numbers, letters and underscore, and must start with a letter or an underscore. The right-hand side must be an integer. Dimensions can be used anywhere else in the problem specification.
cvxgen.com
CVXGEN: Code Generation for Convex Optimization
http://www.cvxgen.com/docs/screenshots.html
CVXGEN: Code Generation for Convex Optimization. These screenshots demonstrate the basic functionality available through CVXGEN's online interface. Middot; next →. Specify your problem in a natural, direct way. Page generated 2013-12-04 20:44:02 PST, by jemdoc.
cvxgen.com
CVXGEN: Code Generation for Convex Optimization
http://www.cvxgen.com/docs/constraints.html
CVXGEN: Code Generation for Convex Optimization. You can create constraints in two ways. Or in a dedicated. Block Dimensions and convexity will be automatically checked. A constraint is an expression. A relation sign (. And another expression. You can also index constraints. Valid constraints have one of three forms:. Constraint on sum of indexed variables. Constraint on fourth and fifth elements of z:. For convenience, you can also use two-sided constraints of the following form:.
cvxgen.com
CVXGEN: Code Generation for Convex Optimization
http://www.cvxgen.com/docs/speed.html
CVXGEN: Code Generation for Convex Optimization. CVXGEN is designed to let you solve convex optimization problems particularly fast. Here are some tips that may help you improve performance if solve time is critical. Reduce the size of your problem. Where possible, reduce the number of variables, constraints or objective terms. With model predictive control problems, for example, see the paper by Wang and Boyd. Lower the fixed iteration limit. (Again, see the settings.) This is particularly impor...The C...
richardkwo.net
Curriculum Vitae
http://www.richardkwo.net/CV.html
Katherine A. Heller. My full CV in PDF format. Last update: December, 2015. Page generated 2015-12-05 22:32:03 EST, by jemdoc.
richardkwo.net
Power-law Constrains Memory?
http://www.richardkwo.net/powermemproject.html
Katherine A. Heller. Is ubiquitous in data from complex systems, such as traffic, social networks and human activities. Meanwhile, most of the inter-event time series from such real systems are also found to be positively autocorrelated (positive memory). In this study, we analyze the hidden relation between memory and power-law distribution. We want to know whether such a positive tendency in memory is due to the constraints imposed by the power-law marginal. And its lag-one counterpart. Two notable sta...
