Parameterized complexity | Analysis of algorithms
In computer science, a kernelization is a technique for designing efficient algorithms that achieve their efficiency by a preprocessing stage in which inputs to the algorithm are replaced by a smaller input, called a "kernel". The result of solving the problem on the kernel should either be the same as on the original input, or it should be easy to transform the output on the kernel to the desired output for the original problem. Kernelization is often achieved by applying a set of reduction rules that cut away parts of the instance that are easy to handle. In parameterized complexity theory, it is often possible to prove that a kernel with guaranteed bounds on the size of a kernel (as a function of some parameter associated to the problem) can be found in polynomial time. When this is possible, it results in a fixed-parameter tractable algorithm whose running time is the sum of the (polynomial time) kernelization step and the (non-polynomial but bounded by the parameter) time to solve the kernel. Indeed, every problem that can be solved by a fixed-parameter tractable algorithm can be solved by a kernelization algorithm of this type. (Wikipedia).
Introduction to the Kernel and Image of a Linear Transformation
This video introduced the topics of kernel and image of a linear transformation.
From playlist Kernel and Image of Linear Transformation
Determine the Kernel of a Linear Transformation Given a Matrix (R3, x to 0)
This video explains how to determine the kernel of a linear transformation.
From playlist Kernel and Image of Linear Transformation
Proof that the Kernel of a Linear Transformation is a Subspace
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Proof that the Kernel of a Linear Transformation is a Subspace
From playlist Proofs
Concept Check: Describe the Kernel of a Linear Transformation (Projection onto y=x)
This video explains how to describe the kernel of a linear transformation that is a projection onto the line y = x.
From playlist Kernel and Image of Linear Transformation
Kernel of a group homomorphism
In this video I introduce the definition of a kernel of a group homomorphism. It is simply the set of all elements in a group that map to the identity element in a second group under the homomorphism. The video also contain the proofs to show that the kernel is a normal subgroup.
From playlist Abstract algebra
Find the Kernel of a Matrix Transformation (Give Direction Vector)
This video explains how to determine direction vector a line that represents for the kernel of a matrix transformation
From playlist Kernel and Image of Linear Transformation
Determine a Basis for the Kernel of a Matrix Transformation (3 by 4)
This video explains how to determine a basis for the kernel of a matrix transformation.
From playlist Kernel and Image of Linear Transformation
Concept Check: Describe the Kernel of a Linear Transformation (Reflection Across y-axis)
This video explains how to describe the kernel of a linear transformation that is a reflection across the y-axis.
From playlist Kernel and Image of Linear Transformation
Live CEOing Ep 690: Language Design in Wolfram Language [Kernel Configuration UI]
In this episode of Live CEOing, Stephen Wolfram discusses upcoming improvements and features of Kernel configuration UI for parallel and remote kernels in the Wolfram Language. If you'd like to contribute to the discussion in future episodes, you can participate through this YouTube channe
From playlist Behind the Scenes in Real-Life Software Design
Configuring and Managing Wolfram Language Kernels
From within a Wolfram Language session, it is possible to connect to other Wolfram Language engines (kernels) both locally and on remote computers using a variety of connection methods. Such kernels can be used for ad-hoc evaluations, as session kernels of a notebook or bundled for paralle
From playlist Wolfram Technology Conference 2022
Kernel Recipes 2016 - The Kernel Report - Jonathan Corbet
The Linux kernel is at the core of any Linux system; the performance and capabilities of the kernel will, in the end, place an upper bound on what he system as a whole can do. This talk will review recent events in the kernel development community, discuss the current state of the kernel a
From playlist Kernel Recipes 2016
Parallelism in the Enterprise Private Cloud
To learn more about Wolfram Technology Conference, please visit: https://www.wolfram.com/events/technology-conference/ Speaker: Roman E Maeder Wolfram developers and colleagues discussed the latest in innovative technologies for cloud computing, interactive deployment, mobile devices, a
From playlist Wolfram Technology Conference 2017
DeepSec 2009: Windows Secure Kernel Development
Thanks to the DeepSec organisation for making these videos available and let me share the videos on YouTube. Speaker: Fermin J. Serna Fermin J. Serna talks about how to securely develop software for the Microsoft Windows platform. For more information visit: http://bit.ly/DeepSec_2009_i
From playlist DeepSec 2009
Kernel Recipes 2017 - Linux Kernel release model - Greg KH
This talk describes how the Linux kernel development model works, what a long term supported kernel is, and why all Linux-based systems devices should be using all of the stable releases and not attempting to pick and choose random patches. It also goes into how the kernel community appro
From playlist Kernel Recipes 2017
24C3: Inside the Mac OS X Kernel
Speaker: Lucy Debunking Mac OS Myths Many buzzwords are associated with Mac OS X: Mach kernel, microkernel, FreeBSD kernel, C++, 64 bit, UNIX... and while all of these apply in some way, "XNU", the Mac OS X kernel is neither Mach, nor FreeBSD-based, it's not a microkernel, it's not writt
From playlist 24C3: Full steam ahead
Parallel Computing in the Wolfram Language
In the first webinar of the Software Development webinar series, you'll learn about the state-of-the-art local and global optimization techniques and parallel programming paradigms integrated into the Wolfram Language, along with parallelization fundamentals.
From playlist Software Development Webinar Series
Kernel Recipes 2022 - Checking your work: validating the kernel by building and testing in CI
The Linux kernel is one of the most complex pieces of software ever written. Being in ring 0, bugs in the kernel are a big problem, so having confidence in the correctness and robustness of the kernel is incredibly important. This is difficult enough for a single version and configuration
From playlist Kernel Recipes 2022
Convolution in the time domain
This video lesson is part of a complete course on neuroscience time series analyses. The full course includes - over 47 hours of video instruction - lots and lots of MATLAB exercises and problem sets - access to a dedicated Q&A forum. You can find out more here: https://www.udemy.
From playlist NEW ANTS #3) Time-frequency analysis