The document discusses various numerical techniques for solving equations and systems of equations. It covers bisection, regula falsi, Newton-Raphson, and interpolation methods for finding roots of equations. It also covers the Jacobi and Gauss-Seidel methods for solving systems of linear equations iteratively. Numerical differentiation and integration techniques like the trapezoidal, Simpson's, and Runge-Kutta methods are also summarized. Examples are provided to illustrate solving systems of equations using the Jacobi and Gauss-Seidel methods.
Nams- Roots of equations by numerical methodsRuchi Maurya
Bisection method. The simplest root-finding algorithm is the bisection method. ...
False position (regula falsi) ...
Interpolation. ...
Newton's method (and similar derivative-based methods) ...
Secant method. ...
Interpolation. ...
Inverse interpolation. ...
Brent's method.
In mathematics and computing, a root-finding algorithm is an algorithm, for finding values x such that f(x) = 0, for a given continuous function f from the real numbers to real numbers or from the complex numbers to the complex numbers. Such an x is called a root or zero of the function f. As, generally, the roots may not be described exactly, they are approximated as floating point numbers, or isolated in small intervals (or disks for complex roots), an interval or disk output being equivalent to an approximate output together with an error bound.
Solving an equation f(x) = g(x) is the same as finding the roots of the function f – g. Thus root-finding algorithms allows solving any equation.
Numerical root-finding methods use iteration, producing a sequence of numbers that hopefully converge towards the root as a limit. They require one or more initial guesses of the root as starting values, then each iteration of the algorithm produces a successively more accurate approximation to the root. Since the iteration must be stopped at some point these methods produce an approximation to the root, not an exact solution. Many methods compute subsequent values by evaluating an auxiliary function on the preceding values. The limit is thus a fixed point of the auxiliary function, which is chosen for having the roots of the original equation as fixed points.
The behaviour of root-finding algorithms is studied in numerical analysis. Algorithms perform best when they take advantage of known characteristics of the given function, so different algorithms are used to solve different types of equations. Desirable characteristics include a rapid rate of convergence, ability to separate close roots, robustness against failures of differentiability, and low propagation rate of rounding errors.
Nams- Roots of equations by numerical methodsRuchi Maurya
Bisection method. The simplest root-finding algorithm is the bisection method. ...
False position (regula falsi) ...
Interpolation. ...
Newton's method (and similar derivative-based methods) ...
Secant method. ...
Interpolation. ...
Inverse interpolation. ...
Brent's method.
In mathematics and computing, a root-finding algorithm is an algorithm, for finding values x such that f(x) = 0, for a given continuous function f from the real numbers to real numbers or from the complex numbers to the complex numbers. Such an x is called a root or zero of the function f. As, generally, the roots may not be described exactly, they are approximated as floating point numbers, or isolated in small intervals (or disks for complex roots), an interval or disk output being equivalent to an approximate output together with an error bound.
Solving an equation f(x) = g(x) is the same as finding the roots of the function f – g. Thus root-finding algorithms allows solving any equation.
Numerical root-finding methods use iteration, producing a sequence of numbers that hopefully converge towards the root as a limit. They require one or more initial guesses of the root as starting values, then each iteration of the algorithm produces a successively more accurate approximation to the root. Since the iteration must be stopped at some point these methods produce an approximation to the root, not an exact solution. Many methods compute subsequent values by evaluating an auxiliary function on the preceding values. The limit is thus a fixed point of the auxiliary function, which is chosen for having the roots of the original equation as fixed points.
The behaviour of root-finding algorithms is studied in numerical analysis. Algorithms perform best when they take advantage of known characteristics of the given function, so different algorithms are used to solve different types of equations. Desirable characteristics include a rapid rate of convergence, ability to separate close roots, robustness against failures of differentiability, and low propagation rate of rounding errors.
Numerical Method Analysis: Algebraic and Transcendental Equations (Non-Linear)Minhas Kamal
Numerical Method Analysis- Solution of Algebraic and Transcendental Equations (Non-Linear Equation). Algorithms- Bisection Method, False Position Method, Newton-Raphson Method, Secant Method, Successive Approximation Method.
Visit here for getting code implementation- https://github.com/MinhasKamal/AlgorithmImplementations/blob/master/numericalMethods/equationSolving/NonLinearEquationSolvingProcess.c
Created in 2nd year of Bachelor of Science in Software Engineering (BSSE) course at Institute of Information Technology, University of Dhaka (IIT, DU).
Regula Falsi or False Position Method is one of the iterative (bracketing) Method for solving root(s) of nonlinear equation under Numerical Methods or Analysis.
Numerical method (curve fitting)
***TOPICS ARE****
Linear Regression
Multiple Linear Regression
Polynomial Regression
Example of Newton’s Interpolation Polynomial And example
Example of Newton’s Interpolation Polynomial And example
* Find zeros of polynomial functions
* Use the Fundamental Theorem of Algebra to find a function that satisfies given conditions
* Find all zeros of a polynomial function
Numerical Method Analysis: Algebraic and Transcendental Equations (Non-Linear)Minhas Kamal
Numerical Method Analysis- Solution of Algebraic and Transcendental Equations (Non-Linear Equation). Algorithms- Bisection Method, False Position Method, Newton-Raphson Method, Secant Method, Successive Approximation Method.
Visit here for getting code implementation- https://github.com/MinhasKamal/AlgorithmImplementations/blob/master/numericalMethods/equationSolving/NonLinearEquationSolvingProcess.c
Created in 2nd year of Bachelor of Science in Software Engineering (BSSE) course at Institute of Information Technology, University of Dhaka (IIT, DU).
Regula Falsi or False Position Method is one of the iterative (bracketing) Method for solving root(s) of nonlinear equation under Numerical Methods or Analysis.
Numerical method (curve fitting)
***TOPICS ARE****
Linear Regression
Multiple Linear Regression
Polynomial Regression
Example of Newton’s Interpolation Polynomial And example
Example of Newton’s Interpolation Polynomial And example
* Find zeros of polynomial functions
* Use the Fundamental Theorem of Algebra to find a function that satisfies given conditions
* Find all zeros of a polynomial function
A detailed description about Cryptography explaining the topic from the very basics. Explaining how it all started, and how is it currently being applied in the real world. Mostly useful for students in engineering and mathematics.
Numerical Methods was a core subject for Electrical & Electronics Engineering, Based On Anna University Syllabus. The Whole Subject was there in this document.
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This is meant for university students taking either information technology or engineering courses, this course of differentiation, Integration and limits helps you to develop your problem solving skills and other benefits that come along with it.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
1. Unit – IV: Numerical Techniques
•Zeroes of transcendental and polynomial equations using
•Bisection method,
•Regula-falsi method
•Newton-Raphson method,
• Rate of convergence.
• Interpolation:
• Finite differences,
•Newton’s forward and backward interpolation,
• Lagrange’s and Newton’s divided difference formula for
unequal intervals.
1
2. A root, r, of function f occurs when f(r) = 0.
For example:
f(x) = x2 – 2x – 3
has two roots at r = -1 and r = 3.
f(-1) = 1 + 2 – 3 = 0
f(3) = 9 – 6 – 3 = 0
We can also look at f in its factored form.
f(x) = x2 – 2x – 3 = (x + 1)(x – 3)
2
3. Bisection Method
Initial two values of x taken: x1=a & x2=b such that if y(a) is
+ve then y(b) is –ve. Then new value c=(a+b)/2
a bc
f(a)>0
f(b)<0
f(c)>0
If c is +ve, then replace the value of a by c. If c is –
ve, then replace the value of b by c.
Then continue to find the next value of c (End of
1st iteration) with c=(a+b)/2
Guaranteed to converge to a root if one exists
within these initial two values.
3
4. Regula Falsi
a bc
( ) ( )
( ) ( )
( ) ( )
( ) 0 ( )
0 ( )
( ) ( )
( )
( ) ( )
f a f b
y x f b x b
a b
f a f b
y c f b c b
a b
a b
f b c b
f a f b
f b a b
c b
f a f b
f(c)<0
4
5. Newton-Raphson Method
• Only one current guess of x .
• Consider some point x0.
– If we approximate f(x) as a line about x0, then we
can again solve for the root of the line.
0 0 0( ) ( )( ) ( )l x f x x x f x
Solving, leads to the following iteration:
0
1 0
0
1
( ) 0
( )
( )
( )
( )
i
i i
i
l x
f x
x x
f x
f x
x x
f x
5
6. Unit – V: Numerical Techniques –II
•Solution of system of linear equations,
•Matrix Decomposition methods,
•Jacobi method,
• Gauss- Seidal method.
• Numerical differentiation,
•Numerical integration,
•Trapezoidal rule,
•Simpson’s one third and three-eight rules,
• Solution of ordinary differential equations (first
order,second order and simultaneous)
•by Euler’s, Picard’s and
• fourth-order Runge- Kutta methods.
6
7. Systems of Non-linear Equations
Consider the set of equations:
1 1 2
2 1 2
1 2
, , , 0
, , , 0
, , , 0
n
n
n n
f x x x
f x x x
f x x x
K
K
M
K
11 1 12 2 1 1
21 1 22 2 2 2
1 1 2 2
The system of equations:
N N
N N
N N NN N N
a T a T a T C
a T a T a T C
a T a T a T C
L
L
M M M M M
L
A total of N algebraic equations for the N nodal points and the system can be
expressed as a matrix formulation: [A][T]=[C]
11 12 1 1 1
21 22 2 2 2
1 2
= , ,
N
N
N N NN N N
a a a T C
a a a T C
where A T C
a a a T C
L
L
M M M M M M
L
7