OpenLLM-France/wikimedia huggingface dataset:
# LMIs in Control/KYP Lemmas/KYP Lemma (Bounded Real Lemma)

KYP Lemma (Bounded Real Lemma)
The Kalman–Popov–Yakubovich (KYP) Lemma is a widely used lemma in control theory. It is sometimes also referred to as the Bounded Real Lemma. The KYP lemma can be used to determine the $H_{\infty }$ norm of a system and is also useful for proving many LMI results.

## The System

       ${\begin{aligned}{\dot {x}}(t)&=Ax(t)+Bu(t)\\y(t)&=Cx(t)+Du(t)\\x(0)&=x_{0}\end{aligned}}$
where $x(t)\in \mathbb {R} ^{n}$, $y(t)\in \mathbb {R} ^{m}$, $u(t)\in \mathbb {R} ^{q}$, at any $t\in \mathbb {R}$.

## The Data

The matrices $A,B,C,D$ are known.

## The Optimization Problem

The following optimization problem must be solved.
    ${\begin{aligned}&{\underset {\gamma ,\;X}{\operatorname {minimize} }}\quad \gamma \\&\operatorname {subject\;to} &X>0\\&&{\begin{bmatrix}A^{T}X+XA&XB\\B^{T}X&-\gamma I\end{bmatrix}}+{\frac {1}{\gamma }}{\begin{bmatrix}C^{T}\\D^{T}\end{bmatrix}}{\begin{bmatrix}C&D\end{bmatrix}}<0\\\end{aligned}}$

## The LMI: The KYP or Bounded Real Lemma

Suppose ${\hat {G}}(s)(A,B,C,D)$ is the system. Then the following are equivalent.
       $1)\quad \left\|G\right\|_{H_{\infty }}\leq \gamma$
       $2)\quad {\text{There exists a}}\;X>0\;{\text{such that}}$
             ${\begin{bmatrix}A^{T}X+XA&XB\\B^{T}X&-\gamma I\end{bmatrix}}+{\frac {1}{\gamma }}{\begin{bmatrix}C^{T}\\D^{T}\end{bmatrix}}{\begin{bmatrix}C&D\end{bmatrix}}<0$
       $3)\quad {\text{There exists a}}\;X>0\;{\text{such that}}$
             ${\begin{bmatrix}A^{T}X+XA&XB&C^{T}\\B^{T}X&-\gamma I&D^{T}\\C&D&-\gamma I\end{bmatrix}}<0$

## Conclusion:

The KYP Lemma can be used to find the bound $\gamma$ on the $H_{\infty }$ norm of a system. Note from the (1,1) block of the LMI we know that $A$ is Hurwitz.

## Implementation

A link to CodeOcean or other online implementation of the LMI (in progress)

## Related LMIs

Positive Real Lemma

## External Links

A list of references documenting and validating the LMI.
* LMI Methods in Optimal and Robust Control - A course on LMIs in Control by Matthew Peet.
* LMI Properties and Applications in Systems, Stability, and Control Theory - A List of LMIs by Ryan Caverly and James Forbes.
* LMIs in Systems and Control Theory - A downloadable book on LMIs by Stephen Boyd.
# LMIs in Control/LMI Matrix Properties/Minimization of Maximum Eigenvalue of a MAtrix

LMIs in Control/LMI Matrix Properties/Minimization of Maximum Eigenvalue of a MAtrix

## The System

    ${\begin{aligned}A(x)=A_{0}+A_{1}x_{1}+...+A_{n}x_{n}\end{aligned}}$
Note that ${\begin{aligned}A_{i},\quad i=1,2,...,n\end{aligned}}$ are symmetric matrices.

## The Data

    ${\begin{aligned}A_{0},A_{1},...,A_{n}\quad {\text{are given matrices.}}\end{aligned}}$

## The Optimization Problem

Find
${\begin{aligned}x=[x_{1}\quad x_{2}...x_{n}]\end{aligned}}$
to minimize,
${\begin{aligned}J(x)=\lambda _{max}(A(x))\end{aligned}}$
According to Lemma 1.1 in [1] page 10, the following statements are equivalent
${\begin{aligned}\lambda _{max}(A(x))\leq t\iff A(x)-tI\leq 0\end{aligned}}$

## The LMI: The Lyapunov Inequality

Title and mathematical description of the LMI formulation.
       ${\begin{aligned}{\text{min}}\;\quad t:&\\{\text{s.t.}}\quad {\begin{bmatrix}A(x)-tI\end{bmatrix}}&\leq 0\end{aligned}}$

## Conclusion:

${\begin{aligned}x_{i},\quad i=1,2,...,n\quad {\text{and}}\quad t>0\end{aligned}}$ are parameters to be optimized

## Implementation

A link to Matlab codes for this problem in the Github repository:
https://github.com/asalimil/LMI-for-Minimizing-the-Maximum-Eigenvalue-of-Matrix/blob/master/README.md

## Related LMIs

LMI for Matrix Norm Minimization
LMI for Schur Stabilization

## External Links

A list of references documenting and validating the LMI.
* - LMI in Control Systems Analysis, Design and Applications
# LMIs in Control/KYP Lemma (Bounded Real Lemma)

KYP Lemma (Bounded Real Lemma)
The Kalman–Popov–Yakubovich (KYP) Lemma is a widely used lemma in control theory. It is sometimes also referred to as the Bounded Real Lemma. The KYP lemma can be used to determine the $H_{\infty }$ norm of a system and is also useful for proving many LMI results.

## The System

       ${\begin{aligned}{\dot {x}}(t)&=Ax(t)+Bu(t)\\y(t)&=Cx(t)+Du(t)\\x(0)&=x_{0}\end{aligned}}$
where $x(t)\in \mathbb {R} ^{n}$, $y(t)\in \mathbb {R} ^{m}$, $u(t)\in \mathbb {R} ^{q}$, at any $t\in \mathbb {R}$.

## The Data

The matrices $A,B,C,D$ are known.

## The Optimization Problem

The following optimization problem must be solved.
    ${\begin{aligned}&{\underset {\gamma ,\;X}{\operatorname {minimize} }}\quad \gamma \\&\operatorname {subject\;to} &X>0\\&&{\begin{bmatrix}A^{T}X+XA&XB\\B^{T}X&-\gamma I\end{bmatrix}}+{\frac {1}{\gamma }}{\begin{bmatrix}C^{T}\\D^{T}\end{bmatrix}}{\begin{bmatrix}C&D\end{bmatrix}}<0\\\end{aligned}}$

## The LMI: The KYP or Bounded Real Lemma

Suppose ${\hat {G}}(s)(A,B,C,D)$ is the system. Then the following are equivalent.
       $1)\quad \left\|G\right\|_{H_{\infty }}\leq \gamma$
       $2)\quad {\text{There exists a}}\;X>0\;{\text{such that}}$
             ${\begin{bmatrix}A^{T}X+XA&XB\\B^{T}X&-\gamma I\end{bmatrix}}+{\frac {1}{\gamma }}{\begin{bmatrix}C^{T}\\D^{T}\end{bmatrix}}{\begin{bmatrix}C&D\end{bmatrix}}<0$

## Conclusion:

The KYP Lemma can be used to find the bound $\gamma$ on the $H_{\infty }$ norm of a system. Note from the (1,1) block of the LMI we know that $A$ is Hurwitz.

## Implementation

A link to CodeOcean or other online implementation of the LMI (in progress)

## Related LMIs

Positive Real Lemma

## External Links

* LMI Methods in Optimal and Robust Control - A course on LMIs in Control by Matthew Peet.
* LMI Properties and Applications in Systems, Stability, and Control Theory - A List of LMIs by Ryan Caverly and James Forbes.
* LMIs in Systems and Control Theory - A downloadable book on LMIs by Stephen Boyd.
# LMIs in Control/LMI Matrix Properties
## Contents

      * Maximum Singular Value of a Complex Matrix
      * Minimum Singular Value of a Complex Matrix
      * Minimization of Maximum Eigenvalue of a Matrix
# LMIs in Control/LMI Matrix Properties/Minimization of Maximum Eigenvalue of a Matrix

LMIs in Control/LMI Matrix Properties/Minimization of Maximum Eigenvalue of a Matrix

## The System

    ${\begin{aligned}A(x)=A_{0}+A_{1}x_{1}+...+A_{n}x_{n}\end{aligned}}$
Note that ${\begin{aligned}A_{i},\quad i=1,2,...,n\end{aligned}}$ are symmetric matrices.

## The Data

    ${\begin{aligned}A_{0},A_{1},...,A_{n}\quad {\text{are given matrices.}}\end{aligned}}$

## The Optimization Problem

Find
${\begin{aligned}x=[x_{1}\quad x_{2}...x_{n}]\end{aligned}}$
to minimize,
${\begin{aligned}J(x)=\lambda _{max}(A(x))\end{aligned}}$
According to Lemma 1.1 in [1] page 10, the following statements are equivalent
${\begin{aligned}\lambda _{max}(A(x))\leq t\iff A(x)-tI\leq 0\end{aligned}}$

## The LMI: The Lyapunov Inequality

Title and mathematical description of the LMI formulation.
       ${\begin{aligned}{\text{min}}\;\quad t:&\\{\text{s.t.}}\quad {\begin{bmatrix}A(x)-tI\end{bmatrix}}&\leq 0\end{aligned}}$

## Conclusion:

${\begin{aligned}x_{i},\quad i=1,2,...,n\quad {\text{and}}\quad t>0\end{aligned}}$ are parameters to be optimized

## Implementation

A link to Matlab codes for this problem in the Github repository:
https://github.com/asalimil/LMI-for-Minimizing-the-Maximum-Eigenvalue-of-Matrix/blob/master/README.md

## Related LMIs

LMI for Matrix Norm Minimization

## External Links

A list of references documenting and validating the LMI.
* - LMI in Control Systems Analysis, Design and Applications
# LMIs in Control/LMI for Polytopic Uncertainity

      * Polytopic Quadratic Stability
      * Quadratic Polytopic Hinf- Optimal State Feedback Control
# LMIs in Control/KYP Lemmas/KYP lemma for continous time QSR dissipative system
## The System

Consider a contiuous-time LTI system, ${\mathcal {G}}:{\mathcal {L}}_{2e}\rightarrow {\mathcal {L}}_{2e}$, with minimal state-space realization (A, B, C, D), where ${\mathcal {A}}\in {\mathcal {R}}^{n\times n},{\mathcal {B}}\in {\mathcal {R}}^{n\times m},{\mathcal {C}}\in {\mathcal {R}}^{p\times n},$ and ${\mathcal {D}}\in {\mathcal {R}}^{p\times m}$.
    ${\begin{aligned}{\dot {x}}(t)&=Ax(t)+Bu(t),\\y(t)&=Cx(t)+Du(t)\\\end{aligned}}$

## The Data

The matrices $A,B,C$ and $D$

## The Optimization Problem

The system ${\mathcal {G}}$ is QSR disipative if
    $\int _{0}^{T}(y^{T}(t)Qy(t)+2y^{T}Su(t)+u^{T}(t)Ru(t))\,dt\geq 0,\forall u\in {\mathcal {L}}_{2e},\forall T\geq 0$
where $u(t)$ is the input to ${\mathcal {G}},y(t)$ is the output of ${\mathcal {G}},Q\in {\mathcal {S}}^{p},S\in {\mathcal {R}}^{p\times m},$ and ${\mathcal {R}}\in {\mathcal {S}}^{m}$.

## LMI : KYP Lemma for QSR Dissipative Systems

The system ${\mathcal {G}}$ is also QSR dissipative if and only if there exists $P\in {\mathcal {S}}^{n},$ where $P>0,$ such that
    ${\begin{bmatrix}PA+A^{T}P-C^{T}QC&PB-C^{T}S-C^{T}QD\\(PB-C^{T}S-C^{T}QD)^{T}&-D^{T}QD-(D^{T}S+S^{T}D)-R\end{bmatrix}}\leq 0.$

## Conclusion:

If there exist a positive definite $P$ for the the selected Q,S and R matrices then the system ${\mathcal {G}}$ is QSR dissipative.

## Implementation

Code for implementation of this LMI using MATLAB. https://github.com/VJanand25/LMI

## Related LMIs

Kalman%E2%80%93Yakubovich%E2%80%93Popov_lemma

## References

1. J. C. Willems, “Dissipative dynamical systems - part I: General theory,” Archive Rational Mechanics and Analysis, vol. 45, no. 5, pp. 321–351, 1972.
2. D. J. Hill and P. J. Moylan, “The stability of nonlinear dissipative systems,” IEEE Transac- tions on Automatic Control, vol. 21, no. 5, pp. 708–711, 1976.
3. LMI Properties and Applications in Systems, Stability, and Control Theory, by Ryan James Caverly1 and James Richard Forbes2
# LMIs in Control/KYP Lemmas/Positive Real Lemma

Positive Real Lemma
The Positive Real Lemma is a variation of the Kalman–Popov–Yakubovich (KYP) Lemma. The Positive Real Lemma can be used to determine if a system is passive (positive real).

## The System

       ${\begin{aligned}{\dot {x}}(t)&=Ax(t)+Bu(t)\\y(t)&=Cx(t)+Du(t)\\x(0)&=x_{0}\end{aligned}}$
where $x(t)\in \mathbb {R} ^{n}$, $y(t)\in \mathbb {R} ^{m}$, $u(t)\in \mathbb {R} ^{q}$, at any $t\in \mathbb {R}$.

## The Data

The matrices $A,B,C,D$ are known.

## The LMI: The Positive Real Lemma

Suppose ${\hat {G}}(s)(A,B,C,D)$ is the system. Then the following are equivalent.
       $1)\quad G\;{\text{is passive, i.e.}}\;\left\langle u,Gu\right\rangle _{L_{2}}\geq 0\;({\hat {G}}(s)+{\hat {G}}(s)^{*}\geq 0)$
       $2)\quad {\text{There exists a}}\;X>0\;{\text{such that}}$
             ${\begin{bmatrix}A^{T}X+XA&XB-C^{T}\\B^{T}X-C&-D^{T}-D\end{bmatrix}}\leq 0$

## Conclusion:

The Positive Real Lemma can be used to determine if the system $G$ is passive. Note from the (1,1) block of the LMI we know that $A$ is Hurwitz.

## Implementation

A link to CodeOcean or other online implementation of the LMI (in progress)

## Related LMIs

KYP Lemma (Bounded Real Lemma)

## External Links

A list of references documenting and validating the LMI.
* LMI Methods in Optimal and Robust Control - A course on LMIs in Control by Matthew Peet.
* LMI Properties and Applications in Systems, Stability, and Control Theory - A List of LMIs by Ryan Caverly and James Forbes.
* LMIs in Systems and Control Theory - A downloadable book on LMIs by Stephen Boyd.
# LMIs in Control/LMI Matrix Properties/Maximum Singular Value of a Complex Matrix

LMIs in Control/LMI Matrix Properties/Maximum Singular Value of a Complex Matrix
Maximum Singular Value of a Complex Matrix

## The System

Consider $A\in \mathbb {R} ^{n\times m}$ as well as $\gamma$. A maximum singular value of a matrix $A$ is less than $\gamma$ if and only if $AA^{H}<\gamma ^{2}I$, where $A^{H}$ is the conjugate transpose or Hermitian transpose of the matrix $A$.

## The Data

The matrix $A$ is the only data required.

## The LMI: Maximum Singular Value of a Complex Matrix

Using the Shur complement procedure, the following LMIs can be constructed:
       ${\begin{aligned}{\bar {\sigma }}(A)<\gamma \\{\begin{bmatrix}\gamma I&A\\A^{H}&\gamma I\end{bmatrix}}&>0\\\end{aligned}}$
The following LMI is also equivalent:
       ${\begin{aligned}{\bar {\sigma }}(A)<\gamma \\{\begin{bmatrix}\gamma I&A^{H}\\A&\gamma I\end{bmatrix}}&>0\\\end{aligned}}$

## Conclusion:

The results from this LMI will give the maximum complex value of the matrix $A$:
       ${\begin{aligned}{\bar {\sigma }}(A)<\gamma \end{aligned}}$

## Implementation

A link to CodeOcean or other online implementation of the LMI

## Related LMIs

* [\\ Minimum singular value of a complex matrix]

## External Links

* LMI Methods in Optimal and Robust Control - A course on LMIs in Control by Matthew Peet.
* LMI Properties and Applications in Systems, Stability, and Control Theory - A List of LMIs by Ryan Caverly and James Forbes.
* LMIs in Systems and Control Theory - A downloadable book on LMIs by Stephen Boyd.
# LMIs in Control/KYP Lemmas

      * KYP Lemma (Bounded Real Lemma)
      * Positive Real Lemma
      * KYP lemma for continous time QSR dissipative system
# Human Physiology/Further Reading

This page lists some resources to further the knowledge you gained from this introductory book, or clarify something you may not understand.
Don't be afraid to post any resources that helped you, or you think may help others!

## Homeostasis

* Wikipedia - Homeostasis
* biology-online.org - Physiological Homeostasis
* optimalhealth.cia.com.au - Homeostasis and the human body

## Cell physiology

* howstuffworks.com - cells
* people.eku.edu - Cell Physiology
* wikipedia - Cells

## Integumentary System

* wikipedia - the integumentary system
* wikipedia - category integumentary system
* besthealth.com - skin system

## The Nervous System

* erowid.org - extensive, both subjective and objective recreational drug information
* wikipedia - category nervous system
* Neuroscience for kids - Basic information, but could be helpful

## Senses

* wikipedia - senses
* wikipedia - category perception

## The Muscular System

* wikipedia - category muscular system

## Blood Physiology

* wikipedia - category blood

## The cardiovascular system

* emc.maricopa.edu - Circulatory Systems
* wikipedia - category cardiovascular system

## The Immune System

* howstuffworks.com - immune system
* wikipedia - category immune system

## Urinary System

* wikipedia - category urinary system

## The respiratory system

* wikipedia - category respiratory system

## The gastrointestinal system

* wikipedia - category digestive system

## Nutrition

* wikipedia - category nutrition

## The Endocrine System

* wikipedia - endocrine system

## The male/female reproductive system

* wikipedia - category reproductive system
# Human Physiology/Human physiology authors
## Authors

* Kevin Young, U.S. Citizen and resident thereof. I was teaching pre-nursing students of Provo College through distance education technology, while working at the Utah State University Brigham City campus (From 2010-2020 I taught at Snow College, Arizona Western College, and Sul Ross State University, then returned to USU Brigham City). This textbook was created as a class project. I created the chapter pages, but the students are the primary authors (hopefully some of them will list themselves here). The fall 2006 class created the initial structure and content, while the spring 2007 students went through each chapter making improvements. Starting spring 2021, USU students will make contributions to this book.
* Nursing student, Provo College Stephanie Greenwood 18:13, 30 April 2007 (UTC)
* Nursing Student, Provo College Danette Seyffert 16:07, 1 May 2007 (UTC)
* Dr.Elvis Ali, Board Certified Licensed Naturopath and Licensed Acupuncturist. Staff member of CCNM. August 29th, 2016.
Hopefully this project expands far beyond our classroom. If you wish to be listed specifically as a contributing author, then please include your name above this paragraph. We also thank all those who wrote Wikipedia articles that we borrowed from extensively, and all anonymous contributors.

## Content and Contributions

Don P. Demyers, Ph.D
To the best of our knowledge, this is the world's first and only open content human physiology textbook. Not only will this be the most affordable introductory physiology textbook available, but we anticipate that it will become the best one available. We will focus all our efforts for two months on providing valuable content and organizing and explaining information clearly. We anticipate the book becoming increasingly better as worldwide awareness and participation spread.
The original text of this work, and the home of the project, can be found at:
    Wikibooks: Human Physiology
    http://en.wikibooks.org/wiki/Human_Physiology
Notes on the preferred style and formatting of each page can be at:
    Wikibooks: Human Physiology/Formatting
    http://en.wikibooks.org/wiki/Human_Physiology/Formatting

## License

All content contained herein is available under the GNU free documentation license. You can copy it, print it, sell it, and create derivative works from it, in accordance with that license.
This book links to several outside sources, and these links are designed to be illustrative learning tools. Links to outside content do not represent an endorsement of that content. Any of the content not found under the Wikimedia site and subsites is not guaranteed to be under the GDFL; they are most likely not.
# Learning Talmud/rabbi
## Literal translation

Rabbi

## Further reading

* Rabbi
# Learning Talmud/havey
## Qal Form: "To be"
### Perfect

Masculine Singular הוי
# Learning Theories

| A printable version of Learning Theories is available. | A PDF version is available. (info) | Wikipedia has more about this subject: Philosophy of Education |

## Contents

| Theories Behavioralist Constructivist Social Learning Post-Modern Adult Learning | Organizational Learning Contributions by Discipline Triggers Influencing Factors Agents Processes Interorganizational Practice | Knowledge Management Challenges Processes Leadership Change |
| References |||

## See also

* Legal and Regulatory Issues in the Information Economy
* Instructional Technology
* Wikiversity Education Portal
* Contemporary Educational Psychology – The Learning Process
# Learning Talmud/ban'ah(rabbi)
## Literal translation

Ban'ah.

## Explanation

Rabbi Ban'ah (also Bana, Bana'ah, etc.) was a sage during the time of the composition of the Mishnah, and is in between the categories of Tanna and Amora.

## Further information

* Tannaim
* Amoraim
* Jewish Encyclopedia entry
# Knowledge Management Cases in a University in Asia

This e-book is created by a group of Knowledge Management (KM) students. It contains actual cases of KM application in various departments in a university in Asia.
In this e-book, the reason for KM implementation in the departments, strategies employed, problems encountered and some suggestions for future development are recorded. Some of the names and facts pertaining to the organizations may have been modified in order to protect the privacy of the people involved.
    Samuel Kai Wah Chu, Ph.D.
    Associate Professor
    Division of Information & Technology Studies
    Deputy Director, Centre for Information Technology in Education
    Faculty of Education, The University of Hong Kong
    Pokfulam Road, Hong Kong
    E-mail: samchuhku.hk
    Homepage: http://www.edu.hku.hk/samchu/

## Table of contents

* Knowledge Management Practice of a University Library in Asia
* Applying Knowledge Management in Career Resources for Higher Education Institution in Asia
* Knowledge Management Study in a University Counseling Unit
* Knowledge Management Cases in a University General Education Department
# Introduction to Programming Languages/Introduction Cost Models

Programming language constructs might have particular asymptotic complexities, which programmers should know, to produce efficient algorithms. In other words, even though an algorithm has some specific, well-known, computational cost, its implementation can have higher complexity, due to an implicit cost model that is part of that languages runtime environment. Furthermore, some programming languages miss features that might complicate the implementation of efficient algorithms. For instance, it will be hard to implement Bucket Sort without random access data structures. These minutia concern an aspect of programming languages' implementation that we call cost models.
In this chapter we will look into four cost models. The first concerns the use of lists in programming languages that follow the Lisp model. The second cost model concerns the invocation of functions in languages that support recursion. After that, we look into Prolog's unification model, trying to reason about the factors that cause unification to be more or less efficient. Finally, we discuss languages that support arrays, which are random access data structures.
← Unification · List Cost Model →
# Introduction to Programming Languages/Noticeable High-Order Functions
### Noticeable High-Order Functions

Some high-order functions are very common programming idioms. Three of the most famous examples are map, reduce and filter. The first example in this chapter is an implementation of the map function. It takes two arguments, a data-structure t and a mapping function f, and returns a new data-structure in which every element has been transformed by f. Usually map works on lists. Map does not add nor remove elements from the input list; thus, the input and output lists will have always the same size.
Reduce is used to transform a data-structure into a single value, given continuous applications of a binary operator. In SML reduce comes in two flavours: foldr and foldl. The first function, foldr, takes a binary function of type 'a * 'b -> 'b, a seed of type 'b, plus a list of type 'a list, and returns an element of type 'b that is called the reduction. The function foldr starts applying the binary operator from the right-side of the list towards its left side. Some examples are given below:
The function foldl is very similar to foldr; however, it starts evaluating the binary operation from the left side of the list towards its right side. A few examples of use are given below:
Sometimes foldl and foldr produce the same results when given the same operands. This outcome depends on the binary operator that these operations use. If the operator is both associative and commutative, then these functions will produce the same results for the same inputs. Otherwise, their results might be different.
These three functions, map, foldr and foldl are very useful parallel skeletons. Map, for instance, can be completely parallelized, given a number of processors proportional to the size of the input list, as in the PRAM model. In other words, this function would execute in O(f) asymptotic time in this scenario, where O(f) is the complexity of applying the mapping function. Given a very large number of processors, i.e., proportional to the size of the input list, both foldr and foldl could have their complexity reduced to a logarithmic factor of the size of the input list. This observation has motivated the design of several high-performance frameworks, such as map reduce and radoop.
Our last high order function is filter. This function has the type ('a -> bool) -> 'a list -> 'a list. This function takes two arguments: a predicate, that is, a function that returns true or false to a given input, and a list. Filter returns a new list that contains only those elements that cause the predicate to be true. Some examples in SML can be seen below:
← Partial Application · Template Oriented Programming →