9.6 Systems of Inequalities and Linear Programmingsmiller5
This document provides an overview of systems of inequalities and how to graph and solve them. It discusses representing systems of inequalities symbolically and identifying the solution as the overlapping region of the graphed inequalities. Examples are provided of writing systems of inequalities from word problems and using graphs to find the solutions. Linear programming is also introduced as an application of systems of inequalities to optimize an objective function subject to constraints.
Solving Systems of Equations and Inequalities by Graphingdmidgette
This document discusses how to solve systems of equations and inequalities by graphing. It explains that systems contain two or more equations or inequalities to be solved simultaneously. Graphing the functions allows identification of intersection points, which are the solutions. Systems can have one solution, no solution, or infinitely many solutions depending on whether the graphs intersect, are parallel, or coincide. The steps are to graph each function on the same plane and identify intersection points of solutions. Systems of inequalities are graphed similarly, with the region of overlap indicating the solution set. Examples demonstrate solving various systems of equations and inequalities through graphing.
This document provides instructions for using synthetic division to divide polynomials. It contains the following key points:
1. Synthetic division can be used to divide polynomials when the divisor has a leading coefficient of 1 and there is a coefficient for every power of the variable in the numerator.
2. The procedure involves writing the terms of the numerator in descending order, bringing down the constant of the divisor, multiplying and adding down the columns to obtain the coefficients of the quotient polynomial and the remainder.
3. An example problem walks through each step of synthetic division to divide (5x^4 - 4x^2 + x + 6) / (x - 3), obtaining a quotient of 5x^3 + 15
The document provides an introduction to various MATLAB fundamentals including:
- Modeling the problem of a falling object using differential equations and analytical/numerical solutions.
- Conservation laws that constrain numerical solutions.
- MATLAB commands for defining variables, arrays, matrices, and performing basic operations.
- Plotting the velocity-time solution and customizing graphs.
- Describing algorithms using flowcharts and pseudocode.
- Structured programming in MATLAB using scripts, functions, decisions, and loops.
This document provides an overview of numerical linear algebra concepts including matrix notation, operations, and solving systems of linear equations using Gaussian elimination. It describes the Gaussian elimination process which involves eliminating variables one by one to obtain an upper triangular system that can then be solved using back substitution. The document notes some pitfalls of naive Gaussian elimination such as division by zero, round-off errors, ill-conditioned systems, and singular systems. It introduces pivoting as a technique to avoid division by zero during the elimination process and calculates the determinant as a byproduct of Gaussian elimination.
After the completion of this workshop, you will be able to:
1. Create and working with Arrays of numbers
2. Create simple plots
3. Plot the given function
4. Do Symbolic Computation
5. Understand SIMULINK
The document discusses LU decomposition and its applications in numerical linear algebra. It explains that LU decomposition decomposes a matrix A into lower and upper triangular matrices (L and U) such that A = LU. This decomposition allows the efficient solution of linear systems even when the right hand side vector changes. The document also discusses other related topics like matrix inverse, condition number, special matrices, and iterative refinement. It provides examples to illustrate LU decomposition and its use in calculating the inverse of a matrix.
The document discusses graphing linear inequalities and systems of linear inequalities. It provides steps for graphing a single inequality, which include writing the inequality in slope-intercept form, plotting points, drawing the line, choosing a test point, and shading the correct region. For systems of inequalities, the steps are to graph each inequality individually and then shade where the regions overlap. Worked examples demonstrate how to graph y ≥ -2x + 4 and y < -3x + 6 and find their common region.
9.6 Systems of Inequalities and Linear Programmingsmiller5
This document provides an overview of systems of inequalities and how to graph and solve them. It discusses representing systems of inequalities symbolically and identifying the solution as the overlapping region of the graphed inequalities. Examples are provided of writing systems of inequalities from word problems and using graphs to find the solutions. Linear programming is also introduced as an application of systems of inequalities to optimize an objective function subject to constraints.
Solving Systems of Equations and Inequalities by Graphingdmidgette
This document discusses how to solve systems of equations and inequalities by graphing. It explains that systems contain two or more equations or inequalities to be solved simultaneously. Graphing the functions allows identification of intersection points, which are the solutions. Systems can have one solution, no solution, or infinitely many solutions depending on whether the graphs intersect, are parallel, or coincide. The steps are to graph each function on the same plane and identify intersection points of solutions. Systems of inequalities are graphed similarly, with the region of overlap indicating the solution set. Examples demonstrate solving various systems of equations and inequalities through graphing.
This document provides instructions for using synthetic division to divide polynomials. It contains the following key points:
1. Synthetic division can be used to divide polynomials when the divisor has a leading coefficient of 1 and there is a coefficient for every power of the variable in the numerator.
2. The procedure involves writing the terms of the numerator in descending order, bringing down the constant of the divisor, multiplying and adding down the columns to obtain the coefficients of the quotient polynomial and the remainder.
3. An example problem walks through each step of synthetic division to divide (5x^4 - 4x^2 + x + 6) / (x - 3), obtaining a quotient of 5x^3 + 15
The document provides an introduction to various MATLAB fundamentals including:
- Modeling the problem of a falling object using differential equations and analytical/numerical solutions.
- Conservation laws that constrain numerical solutions.
- MATLAB commands for defining variables, arrays, matrices, and performing basic operations.
- Plotting the velocity-time solution and customizing graphs.
- Describing algorithms using flowcharts and pseudocode.
- Structured programming in MATLAB using scripts, functions, decisions, and loops.
This document provides an overview of numerical linear algebra concepts including matrix notation, operations, and solving systems of linear equations using Gaussian elimination. It describes the Gaussian elimination process which involves eliminating variables one by one to obtain an upper triangular system that can then be solved using back substitution. The document notes some pitfalls of naive Gaussian elimination such as division by zero, round-off errors, ill-conditioned systems, and singular systems. It introduces pivoting as a technique to avoid division by zero during the elimination process and calculates the determinant as a byproduct of Gaussian elimination.
After the completion of this workshop, you will be able to:
1. Create and working with Arrays of numbers
2. Create simple plots
3. Plot the given function
4. Do Symbolic Computation
5. Understand SIMULINK
The document discusses LU decomposition and its applications in numerical linear algebra. It explains that LU decomposition decomposes a matrix A into lower and upper triangular matrices (L and U) such that A = LU. This decomposition allows the efficient solution of linear systems even when the right hand side vector changes. The document also discusses other related topics like matrix inverse, condition number, special matrices, and iterative refinement. It provides examples to illustrate LU decomposition and its use in calculating the inverse of a matrix.
The document discusses graphing linear inequalities and systems of linear inequalities. It provides steps for graphing a single inequality, which include writing the inequality in slope-intercept form, plotting points, drawing the line, choosing a test point, and shading the correct region. For systems of inequalities, the steps are to graph each inequality individually and then shade where the regions overlap. Worked examples demonstrate how to graph y ≥ -2x + 4 and y < -3x + 6 and find their common region.
- Müller's method and Bairstow's method are conventional methods for finding both real and complex roots of polynomials.
- Müller's method fits a parabola to three initial guesses to estimate roots, then iteratively refines the estimate.
- Bairstow's method divides the polynomial by a quadratic factor to estimate roots, then iteratively adjusts the factor's coefficients to minimize the remainder using a process similar to Newton-Raphson.
- Both methods can find all roots of a polynomial by sequentially applying the process after removing already located roots from the polynomial.
This document discusses using matrices to represent and solve systems of equations. A matrix is defined as a rectangular array of numbers. Systems of equations can be represented as matrices, with each row representing an equation and each column representing coefficients of a variable. Row operations, such as switching rows, multiplying rows by constants, and adding/subtracting rows, can be used to put the matrix in reduced row echelon form and solve the system. Examples are provided of representing systems as matrices and using matrices and row operations to solve 2-variable and 3-variable systems of equations. Students are assigned practice problems from the worksheet.
This document discusses three methods for solving simultaneous linear algebraic equations: graphical method, Cramer's rule, and elimination of unknowns. The graphical method involves plotting the equations on a graph and finding their intersection point. Cramer's rule uses determinants to solve for one variable at a time. Elimination of unknowns treats the equations similarly to how single equations are solved, by adding or subtracting equations to eliminate variables until one is isolated. Examples are provided for each method.
This document provides an overview of least-squares regression techniques including:
- Simple linear regression to fit a line to data
- Polynomial regression to fit higher order curves
- Multiple regression to fit surfaces using two or more variables
It discusses calculating regression coefficients, quantifying errors, and performing statistical analysis of the regression results including determining confidence intervals. Examples are provided to demonstrate applying these techniques.
The document provides examples of solving systems of linear equations by substitution and elimination. It also demonstrates how to graph systems of linear inequalities by writing each inequality in slope-intercept form, determining whether the line is solid or dashed, and shading the appropriate regions. Finally, it shows how to apply systems of equations to word problems by setting up the corresponding equations and solving.
Powerpoint on adding and subtracting decimals notesrazipacibe
The document provides instructions for adding and subtracting decimals. It explains that to add decimals, you should line up the decimal points and add the columns from right to left, placing the decimal in the answer below the other decimals. Two examples of decimal addition are shown. It also explains that to subtract decimals, you should line up the decimal points, subtract the columns from right to left while regrouping if needed, and place the decimal in the answer below the other decimals. One example of decimal subtraction is provided.
A linear inequality is similar to a linear equation but uses inequality symbols like < or > instead of =. A solution to a linear inequality is any coordinate pair that makes the inequality true. A linear inequality describes a half-plane region on a coordinate plane where all points in the region satisfy the inequality, with the boundary line given by the related equation. To graph a linear inequality, you solve it for y, graph the boundary line as solid or dotted, and shade the correct half-plane above or below the line.
Synthetic division is a method for dividing polynomials that can be used when the divisor is of the form x - r or x + r, where r is a constant. It involves setting up a table with boxes and lines and systematically filling in numbers from the dividend polynomial and performing operations to arrive at the coefficients of the quotient and the remainder. The resulting expression provides the quotient polynomial and remainder over the divisor from the original problem.
Gaussian Elimination is a variation of the Gauss elimination method that can solve up to 15-20 simultaneous equations with 8-10 significant digits of precision on a computer. It differs from Gaussian elimination by normalizing all rows when using them as the pivot equation, resulting in an identity matrix rather than a triangular matrix. This avoids needing to perform back substitution. The method is demonstrated through solving a system of 3 equations with 3 unknowns via Gaussian elimination, resulting in values for the 3 unknowns. Advantages of the Gaussian-Jordan method include requiring approximately 50% fewer operations than Gaussian elimination and providing a direct method for obtaining the inverse matrix.
This document discusses various matrix decompositions including LU, QR, Cholesky, and singular value decompositions. It provides details on LU decomposition, including that an LU decomposition of a matrix A exists such that A = LU where L is a lower triangular matrix and U is an upper triangular matrix. The document also discusses that LU decomposition can be used to solve systems of linear equations by applying forward and back substitution on the decomposed matrices. Further, it introduces QR decomposition, where a matrix A can be decomposed as A = QR, where Q is an orthonormal matrix and R is an upper triangular matrix.
This document discusses methods for solving systems of linear equations, including Gauss elimination and iterative Gauss methods like the Gauss-Jacobi and Gauss-Seidel methods. It provides explanations of row-echelon form and reduced row-echelon form. Examples are given to demonstrate using elementary row operations to solve systems of linear equations and the Gauss-Jacobi iterative method.
Bresenham's line algorithm is an efficient method for drawing lines on a digital display. It works by calculating the next pixel coordinate along the line using integer math only. This avoids complex floating point calculations. It starts at the initial coordinate and iteratively calculates the next x,y coordinate using integer addition and comparisons until it reaches the final endpoint.
This document provides instructions for adding and subtracting decimals. It explains that decimals should be aligned by place value, with zeros added to make the columns even. It also notes that when whole numbers are used in calculations, an "understood" decimal point and zeros are present but not written. Examples are provided demonstrating how to correctly add, subtract, and align decimals by placing them in columns and only combining like place values.
This document provides an overview and examples for multiplying and dividing monomials with exponents. It defines key terms like monomial, base, and exponent. It explains the product rule for multiplying monomials by adding exponents of the same base. It also explains how to divide monomials by subtracting the exponents of the same base or changing the sign of negative exponents. The document includes 17 examples of multiplying and dividing monomials using these rules.
This document discusses several methods for solving systems of linear equations:
- The graphical method involves drawing the lines defined by the equations on a graph and finding their point of intersection.
- Cramer's rule provides an expression to find the solution using determinants of the coefficient matrix and matrices obtained by replacing columns.
- Matrix inverse involves finding the inverse of the coefficient matrix and multiplying it by the constants vector.
- Gauss elimination is a two step method involving eliminating variables in the forward step and back substitution to find the solution.
- LU decomposition writes the matrix as the product of a lower and upper triangular matrix to solve the system.
This document discusses continuity of functions and discontinuities. It defines a continuous function as one where the limit matches the value of the function at a point. There are two types of discontinuities: essential discontinuities which cannot be removed and removable discontinuities which can be removed by defining the function at the point of discontinuity. The intermediate value theorem states that if a function is continuous on an interval, it must take on all values between the function values at the endpoints.
1) The graphical method involves graphing the lines represented by each equation on the same coordinate plane and finding the point where they intersect, which gives the solution.
2) Cramer's rule expresses each unknown as a ratio of determinants, with the numerator being the determinant of the coefficient matrix with one column replaced by the constants.
3) Gaussian elimination transforms the coefficient matrix into upper triangular form using elementary row operations, then back substitution solves for the unknowns.
Chapter 3: Linear Systems and Matrices - Part 1/SlidesChaimae Baroudi
The document provides information about linear systems and matrices. It begins by defining linear and non-linear equations. It then discusses systems of linear equations, their graphical and geometric interpretations, and the three possible solutions: no solution, a unique solution, or infinitely many solutions. The document also covers matrix notation for representing linear systems, elementary row operations for transforming systems, and determining whether a system has a solution and whether that solution is unique.
Octave - Prototyping Machine Learning AlgorithmsCraig Trim
Octave is a high-level language suitable for prototyping learning algorithms.
Octave is primarily intended for numerical computations and provides extensive graphics capabilities for data visualization and manipulation. Octave is normally used through its interactive command line interface, but it can also be used to write non-interactive programs. The syntax is matrix-based and provides various functions for matrix operations. This tool has been in active development for over 20 years.
* Evaluate 2 × 2 determinants.
* Use Cramer’s Rule to solve a system of equations in two variables.
* Evaluate 3 × 3 determinants.
* Use Cramer’s Rule to solve a system of three equations in three variables.
* Know the properties of determinants.
- Müller's method and Bairstow's method are conventional methods for finding both real and complex roots of polynomials.
- Müller's method fits a parabola to three initial guesses to estimate roots, then iteratively refines the estimate.
- Bairstow's method divides the polynomial by a quadratic factor to estimate roots, then iteratively adjusts the factor's coefficients to minimize the remainder using a process similar to Newton-Raphson.
- Both methods can find all roots of a polynomial by sequentially applying the process after removing already located roots from the polynomial.
This document discusses using matrices to represent and solve systems of equations. A matrix is defined as a rectangular array of numbers. Systems of equations can be represented as matrices, with each row representing an equation and each column representing coefficients of a variable. Row operations, such as switching rows, multiplying rows by constants, and adding/subtracting rows, can be used to put the matrix in reduced row echelon form and solve the system. Examples are provided of representing systems as matrices and using matrices and row operations to solve 2-variable and 3-variable systems of equations. Students are assigned practice problems from the worksheet.
This document discusses three methods for solving simultaneous linear algebraic equations: graphical method, Cramer's rule, and elimination of unknowns. The graphical method involves plotting the equations on a graph and finding their intersection point. Cramer's rule uses determinants to solve for one variable at a time. Elimination of unknowns treats the equations similarly to how single equations are solved, by adding or subtracting equations to eliminate variables until one is isolated. Examples are provided for each method.
This document provides an overview of least-squares regression techniques including:
- Simple linear regression to fit a line to data
- Polynomial regression to fit higher order curves
- Multiple regression to fit surfaces using two or more variables
It discusses calculating regression coefficients, quantifying errors, and performing statistical analysis of the regression results including determining confidence intervals. Examples are provided to demonstrate applying these techniques.
The document provides examples of solving systems of linear equations by substitution and elimination. It also demonstrates how to graph systems of linear inequalities by writing each inequality in slope-intercept form, determining whether the line is solid or dashed, and shading the appropriate regions. Finally, it shows how to apply systems of equations to word problems by setting up the corresponding equations and solving.
Powerpoint on adding and subtracting decimals notesrazipacibe
The document provides instructions for adding and subtracting decimals. It explains that to add decimals, you should line up the decimal points and add the columns from right to left, placing the decimal in the answer below the other decimals. Two examples of decimal addition are shown. It also explains that to subtract decimals, you should line up the decimal points, subtract the columns from right to left while regrouping if needed, and place the decimal in the answer below the other decimals. One example of decimal subtraction is provided.
A linear inequality is similar to a linear equation but uses inequality symbols like < or > instead of =. A solution to a linear inequality is any coordinate pair that makes the inequality true. A linear inequality describes a half-plane region on a coordinate plane where all points in the region satisfy the inequality, with the boundary line given by the related equation. To graph a linear inequality, you solve it for y, graph the boundary line as solid or dotted, and shade the correct half-plane above or below the line.
Synthetic division is a method for dividing polynomials that can be used when the divisor is of the form x - r or x + r, where r is a constant. It involves setting up a table with boxes and lines and systematically filling in numbers from the dividend polynomial and performing operations to arrive at the coefficients of the quotient and the remainder. The resulting expression provides the quotient polynomial and remainder over the divisor from the original problem.
Gaussian Elimination is a variation of the Gauss elimination method that can solve up to 15-20 simultaneous equations with 8-10 significant digits of precision on a computer. It differs from Gaussian elimination by normalizing all rows when using them as the pivot equation, resulting in an identity matrix rather than a triangular matrix. This avoids needing to perform back substitution. The method is demonstrated through solving a system of 3 equations with 3 unknowns via Gaussian elimination, resulting in values for the 3 unknowns. Advantages of the Gaussian-Jordan method include requiring approximately 50% fewer operations than Gaussian elimination and providing a direct method for obtaining the inverse matrix.
This document discusses various matrix decompositions including LU, QR, Cholesky, and singular value decompositions. It provides details on LU decomposition, including that an LU decomposition of a matrix A exists such that A = LU where L is a lower triangular matrix and U is an upper triangular matrix. The document also discusses that LU decomposition can be used to solve systems of linear equations by applying forward and back substitution on the decomposed matrices. Further, it introduces QR decomposition, where a matrix A can be decomposed as A = QR, where Q is an orthonormal matrix and R is an upper triangular matrix.
This document discusses methods for solving systems of linear equations, including Gauss elimination and iterative Gauss methods like the Gauss-Jacobi and Gauss-Seidel methods. It provides explanations of row-echelon form and reduced row-echelon form. Examples are given to demonstrate using elementary row operations to solve systems of linear equations and the Gauss-Jacobi iterative method.
Bresenham's line algorithm is an efficient method for drawing lines on a digital display. It works by calculating the next pixel coordinate along the line using integer math only. This avoids complex floating point calculations. It starts at the initial coordinate and iteratively calculates the next x,y coordinate using integer addition and comparisons until it reaches the final endpoint.
This document provides instructions for adding and subtracting decimals. It explains that decimals should be aligned by place value, with zeros added to make the columns even. It also notes that when whole numbers are used in calculations, an "understood" decimal point and zeros are present but not written. Examples are provided demonstrating how to correctly add, subtract, and align decimals by placing them in columns and only combining like place values.
This document provides an overview and examples for multiplying and dividing monomials with exponents. It defines key terms like monomial, base, and exponent. It explains the product rule for multiplying monomials by adding exponents of the same base. It also explains how to divide monomials by subtracting the exponents of the same base or changing the sign of negative exponents. The document includes 17 examples of multiplying and dividing monomials using these rules.
This document discusses several methods for solving systems of linear equations:
- The graphical method involves drawing the lines defined by the equations on a graph and finding their point of intersection.
- Cramer's rule provides an expression to find the solution using determinants of the coefficient matrix and matrices obtained by replacing columns.
- Matrix inverse involves finding the inverse of the coefficient matrix and multiplying it by the constants vector.
- Gauss elimination is a two step method involving eliminating variables in the forward step and back substitution to find the solution.
- LU decomposition writes the matrix as the product of a lower and upper triangular matrix to solve the system.
This document discusses continuity of functions and discontinuities. It defines a continuous function as one where the limit matches the value of the function at a point. There are two types of discontinuities: essential discontinuities which cannot be removed and removable discontinuities which can be removed by defining the function at the point of discontinuity. The intermediate value theorem states that if a function is continuous on an interval, it must take on all values between the function values at the endpoints.
1) The graphical method involves graphing the lines represented by each equation on the same coordinate plane and finding the point where they intersect, which gives the solution.
2) Cramer's rule expresses each unknown as a ratio of determinants, with the numerator being the determinant of the coefficient matrix with one column replaced by the constants.
3) Gaussian elimination transforms the coefficient matrix into upper triangular form using elementary row operations, then back substitution solves for the unknowns.
Chapter 3: Linear Systems and Matrices - Part 1/SlidesChaimae Baroudi
The document provides information about linear systems and matrices. It begins by defining linear and non-linear equations. It then discusses systems of linear equations, their graphical and geometric interpretations, and the three possible solutions: no solution, a unique solution, or infinitely many solutions. The document also covers matrix notation for representing linear systems, elementary row operations for transforming systems, and determining whether a system has a solution and whether that solution is unique.
Octave - Prototyping Machine Learning AlgorithmsCraig Trim
Octave is a high-level language suitable for prototyping learning algorithms.
Octave is primarily intended for numerical computations and provides extensive graphics capabilities for data visualization and manipulation. Octave is normally used through its interactive command line interface, but it can also be used to write non-interactive programs. The syntax is matrix-based and provides various functions for matrix operations. This tool has been in active development for over 20 years.
* Evaluate 2 × 2 determinants.
* Use Cramer’s Rule to solve a system of equations in two variables.
* Evaluate 3 × 3 determinants.
* Use Cramer’s Rule to solve a system of three equations in three variables.
* Know the properties of determinants.
This document discusses Cramer's Rule, a method for solving systems of linear equations. It defines determinants and how to calculate them for matrices. Cramer's Rule involves setting up the coefficients of a system as a matrix, then calculating the determinant of the original matrix (D) and replacements where each column is replaced with the constants. The value of each variable is equal to the ratio of its corresponding determinant to the original determinant D. An example demonstrates setting up and solving a 2x2 system using Cramer's Rule.
Cramer's Rule is a method for solving systems of linear equations using determinants. It can be used to solve systems with the same number of equations as unknowns. The rule involves writing the system as a coefficient matrix and calculating determinants of the matrix and related matrices to find the values of the unknowns. If the determinant of the coefficient matrix is zero, the system has no unique solution or an infinite number of solutions. Otherwise, Cramer's Rule provides a way to calculate the specific values of each unknown.
This document discusses determinants and Cramer's rule. It defines determinants as special numbers associated with square matrices. It provides examples of calculating determinants of 2x2 and 3x3 matrices by rewriting columns and multiplying diagonals. The document also introduces Cramer's rule as a method to solve linear systems using determinants, where the variable is replaced with constants in the coefficient matrix. Examples are given to demonstrate solving 2x2 and 3x3 systems using Cramer's rule.
Cramer's Rule is a method for solving systems of linear equations. It uses determinants of coefficient matrices. For a system of two equations, the solutions are determined by taking the determinants of the coefficient matrix and matrices with the constant terms replacing coefficients in the numerator. For three equations, the solutions are similarly determined using 3x3 determinants. Cramer's Rule will fail if the determinant of the coefficient matrix is zero, indicating the system is dependent with no unique solution.
Cramer's Rule is a method for solving systems of linear equations. It uses determinants of coefficient matrices. For a system of two equations, the solutions are determined by taking the determinants of the coefficient matrix and matrices with the constant terms replacing coefficients in the numerator. For three equations, the solutions are similarly determined using 3x3 determinants. Cramer's Rule will fail if the determinant of the coefficient matrix is zero, indicating the system is dependent with no unique solution.
9.3 Solving Systems With Gaussian Eliminationsmiller5
Write the augmented matrix of a system of equations.
Write the system of equations from an augmented matrix.
Perform row operations on a matrix.
Solve a system of linear equations using matrices.
This document discusses how matrices can be used to solve systems of equations. It provides two examples:
1) Using the inverse of a matrix to solve a system of 2 equations with 2 unknowns. The inverse cancels out the coefficient matrix, leaving the solution.
2) Using Cramer's Rule to solve systems of equations by setting up matrices of just the coefficients and replacing columns with values from each equation to find determinants and ratios to solve for each unknown.
7.6 Solving Systems with Gaussian Eliminationsmiller5
* Write the augmented matrix of a system of equations.
* Write the system of equations from an augmented matrix.
* Perform row operations on a matrix.
* Solve a system of linear equations using matrices.
1. The document provides information about homework due dates, memo locations, and consultation times. It then discusses determinants and Cramer's Rule for solving systems of linear equations.
2. It defines the determinant of a 2x2 matrix as ad - bc and provides examples of calculating determinants. Cramer's Rule is introduced as a way to solve systems of linear equations using determinants.
3. Examples are given of using Cramer's Rule and calculating determinants to solve 2x2 and 3x3 systems of linear equations. The limitations of Cramer's Rule for larger systems are also noted.
This document discusses matrices and their use in solving systems of linear equations. It defines what a matrix is, including its dimensions and entries. It also explains elementary row operations that can be performed on matrices to put them in row echelon form. Gaussian elimination is introduced as a method of using matrices and elementary row operations to solve systems of linear equations through back-substitution. Examples are provided to demonstrate these concepts.
This document defines determinants and provides examples of calculating determinants of 2x2 and 3x3 matrices. It introduces key terminology like minors, cofactors, and expands on Cramer's rule for solving systems of linear equations using determinants. The document explains that the determinant of a square matrix is a single number associated with the matrix, and provides rules and examples for calculating determinants by summing the products of elements and their cofactors.
1) The graphical method involves graphing the lines represented by each equation on the same coordinate plane and finding the point where they intersect, which gives the solution.
2) Cramer's rule expresses each unknown as a ratio of determinants, with the numerator being the determinant of the coefficients with one column replaced by constants.
3) Gaussian elimination transforms the matrix of coefficients into upper triangular form using elementary row operations, then back substitution can be used to solve for the unknowns.
This document discusses various direct methods for solving linear systems of equations, including graphical methods, Cramer's rule, elimination of unknowns, Gaussian elimination, Gaussian-Jordan elimination, and LU decomposition. It provides examples and explanations of each method. Graphical methods can solve systems of 2 equations visually by plotting the lines. Cramer's rule uses determinants to find solutions. Elimination of unknowns combines equations to remove variables. Gaussian elimination converts the matrix to upper triangular form. Gaussian-Jordan elimination converts it to an identity matrix. LU decomposition factors the matrix into lower and upper triangular matrices.
1) The graphical method involves graphing the lines represented by each equation on the same coordinate plane and finding the point where they intersect, which gives the solution.
2) Cramer's rule expresses each unknown as a ratio of determinants, with the numerator being the determinant of the coefficient matrix with one column replaced by the constants.
3) Gaussian elimination transforms the coefficient matrix into upper triangular form using elementary row operations, then back substitution solves for the unknowns.
Cramer's rule is a method for solving systems of linear equations. It involves calculating the determinants of matrices. For a system of n variables (x1, x2, ..., xn) written in matrix form AX = B, the value of each variable is calculated as the determinant of the coefficient matrix with one column replaced by the constants column, divided by the determinant of the coefficient matrix. For a 2x2 system, Cramer's rule involves calculating the determinants D, Dx1, and Dx2. For a 3x3 system, the determinants are D, Dx1, Dx2, and Dx3. An example of applying Cramer's rule to a 2x
This document provides an introduction and contents overview for a unit on matrices and determinants. It discusses how matrices can be used to represent systems of linear equations and the matrix equation Ax = b. The unit will cover solving systems of linear equations using Gaussian elimination, properties of matrices, determinants, applications including polynomial interpolation and least squares approximations, and issues of ill-conditioning when solving systems numerically. Sections include simultaneous linear equations, properties of matrices, applications of matrices, ill-conditioning, and computing activities.
1) Linear algebra is a core area of computational mathematics. Many numerical methods transform equations into systems of linear algebraic equations.
2) A system of linear algebraic equations can be written in matrix form as Ax=b, where A is the coefficient matrix, x is the vector of unknowns, and b is the constant vector.
3) Solving such a system involves finding the intersection point(s) of lines or surfaces in n-dimensional space defined by the equations. If a unique intersection exists, the solution is unique.
This document defines matrices and determinants, including examples and types of matrices. It describes how to add, subtract, and multiply matrices, and defines determinants and Cramer's rule. Cramer's rule is used to solve a 3x3 system of equations. The relationship between matrices and determinants is that determinants are uniquely related to square matrices but not vice versa, and determinants are used to calculate inverses.
Similar to 9.3 Determinant Solution of Linear Systems (20)
* Model exponential growth and decay
* Use Newton's Law of Cooling
* Use logistic-growth models
* Choose an appropriate model for data
* Express an exponential model in base e
* Construct perpendicular and angle bisectors
* Use bisectors to solve problems
* Identify the circumcenter and incenter of a triangle
* Use triangle segments to solve problems
* Identify, write, and analyze conditional statements
* Write the inverse, converse, and contrapositive of a conditional statement
* Write a counterexample to a fake conjecture
* Find the distance between two points
* Find the midpoint of two given points
* Find the coordinates of an endpoint given one endpoint and a midpoint
* Find the coordinates of a point a fractional distance from one end of a segment
This document provides instruction on factoring polynomials and quadratic equations. It begins by reviewing factoring techniques like finding the greatest common factor and factoring trinomials and binomials. Examples are provided to demonstrate the factoring methods. The document then discusses solving quadratic equations by factoring, putting the equation in standard form, and setting each factor equal to zero. An example problem demonstrates solving a quadratic equation through factoring. The document concludes by assigning homework and an optional reading for the next class.
* Connect functions to their graphs
* Graph piecewise-defined functions
* Graph absolute value functions
* Graph greatest-integer functions
* Interpret graphs
* Use the vertical line test to determine a function
* Connect functions to their graphs
* Graph piecewise-defined functions
* Graph absolute value functions
* Graph greatest-integer functions
* Interpret graphs
* Use the vertical line test to determine a function
* Introduce functions and function notation
* Develop skills in constructing and interpreting the graphs of functions
* Learn to apply this knowledge in a variety of situations
* Recognize graphs of common functions.
* Graph functions using vertical and horizontal shifts.
* Graph functions using reflections about the x-axis and the y-axis.
* Graph functions using compressions and stretches.
* Combine transformations.
This document discusses functions and their graphs. It defines increasing, decreasing and constant functions based on how the function values change as the input increases. Relative maxima and minima are points where a function changes from increasing to decreasing. Symmetry of functions is classified by the y-axis, x-axis and origin. Even functions are symmetric about the y-axis, odd functions are symmetric about the origin. Piecewise functions have different definitions over different intervals.
This document provides examples and steps for solving various types of equations beyond linear equations, including:
1) Polynomial equations solved by factoring
2) Equations with radicals where radicals are eliminated by raising both sides to a power
3) Equations with rational exponents where both sides are raised to the reciprocal power
4) Equations quadratic in form where an algebraic substitution is made to transform into a quadratic equation
5) Absolute value equations where both positive and negative solutions must be considered.
This document provides instruction on factoring quadratic equations. It begins by reviewing factoring polynomials and trinomials. It then discusses factoring binomials using difference of squares, sum/difference of cubes, and other patterns. Finally, it explains that a quadratic equation can be solved by factoring if it can be written as a product of two linear factors. An example demonstrates factoring a quadratic equation by finding the two values that make each factor equal to zero.
This document provides an overview of functions and their graphs. It defines what constitutes a function, discusses domain and range, and how to identify functions using the vertical line test. Key points covered include:
- A function is a relation where each input has a single, unique output
- The domain is the set of inputs and the range is the set of outputs
- Functions can be represented by ordered pairs, graphs, or equations
- The vertical line test identifies functions as those where a vertical line intersects the graph at most once
- Intercepts occur where the graph crosses the x or y-axis
The document discusses the binomial theorem, which provides a formula for expanding binomial expressions of the form (a + b)^n. It gives the formula for finding the coefficient of the term containing b^r as nCr. Several examples are worked out applying the binomial theorem to expand binomial expressions and find specific terms. Factorial notation is introduced for writing the coefficients. The document also discusses using calculators and Desmos to evaluate binomial coefficients. Practice problems are assigned from previous sections.
The document discusses using Venn diagrams and two-way tables to organize data and calculate probabilities. It provides examples of completing Venn diagrams and two-way tables based on survey data about students' activities. It then uses the tables and diagrams to calculate probabilities of different outcomes. The examples illustrate how to set up and use these visual representations of categorical data.
* Solve counting problems using the Addition Principle.
* Solve counting problems using the Multiplication Principle.
* Solve counting problems using permutations involving n distinct objects.
* Solve counting problems using combinations.
* Find the number of subsets of a given set.
* Solve counting problems using permutations involving n non-distinct objects.
* Use summation notation.
* Use the formula for the sum of the first n terms of an arithmetic series.
* Use the formula for the sum of the first n terms of a geometric series.
* Use the formula for the sum of an infinite geometric series.
* Solve annuity problems.
* Find the common ratio for a geometric sequence.
* List the terms of a geometric sequence.
* Use a recursive formula for a geometric sequence.
* Use an explicit formula for a geometric sequence.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
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A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Physiology and chemistry of skin and pigmentation, hairs, scalp, lips and nail, Cleansing cream, Lotions, Face powders, Face packs, Lipsticks, Bath products, soaps and baby product,
Preparation and standardization of the following : Tonic, Bleaches, Dentifrices and Mouth washes & Tooth Pastes, Cosmetics for Nails.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
2. Concepts and Objectives
Determinant Solution of Linear Systems
Calculate the determinant of a square matrix
Use Cramer’s Rule to solve a system of equations
3. Systems and Matrices
A matrix is a rectangular array of numbers enclosed in
brackets. Each number is called an element of the
matrix.
There are three different ways of using matrices to solve
a system:
Use the multiplicative inverse.
The Gauss-Jordan Method, which uses augmented
matrices.
Cramer’s Rule, which uses determinants.
4. Determinants
Every n n matrix A is associated with a real number
called the determinant of A, written A .
The determinant is the sum of the diagonals in one
direction minus the sum of the diagonals in the other
direction.
Example:
3 4
6 8
24 24 48
3 8 6 4
a b
c d
ad cb
12. Cramer’s Rule
To solve a system using Cramer’s Rule, set up a matrix of
the coefficients and calculate the determinant (D).
Then, replace the first column of the matrix with the
constants and calculate that determinant (Dx).
Continue, replacing the column of the variable with the
constants and calculating the determinant (Dy, etc.)
The value of the variable is the ratio of the variable
determinant to the original determinant.
13. Cramer’s Rule
Example: Solve the system using Cramer’s Rule.
5 7 1
6 8 1
x y
x y
14. Cramer’s Rule
Example: Solve the system using Cramer’s Rule.
5
6 1
7 1
8
x y
x y
40 4
7
6 8
2 2
5
D
7
1
1
8 7 15
8
x
D
1
5
6
5 6 11
1
y
D
15
7.5
2
x
D
x
D
11
5.5
2
y
D
y
D