The document discusses solving systems of linear equations with three variables. The standard method is elimination, where one extracts a system of two equations with two unknowns from the original system of three equations. One solves the system of two equations and plugs the answers back into the original system to find the third variable. This process generalizes to systems with N equations and N unknowns by repeatedly extracting and solving smaller systems until reaching a system of two equations. An example problem demonstrates applying this method to find the price of hamburgers, fries, and sodas given three equations relating purchases to total costs.
Malachy Mitchell, Managing Director, Farrelly & Mitchell - reveals how balancing demand generation and ensuring supplies to meet demand is key fpr success. Government is a vital link to production capacity with various support schemes as well.
With the growing number of dietary needs being requested at events, it's about time we understood what they are and how to incorporate them into our menus. This fun, engaging trivia game gives meeting planners, caterers and hoteliers a fun and easy way to understand the needs and learn how to create a better customer experience through the food they are serving. Players/teams will compete to see who knows the most about food allergies and other dietary needs, table etiquette, sustainability, food safety, food culture, nutrition, cooking techniques and ingredients.
Intelligent vehicle control based on identification of road signs by solar po...eSAT Journals
Abstract Our paper deals with automatic vehicle speed limit on roads by using solar powered RFID technology to control an automobile. In the present scenario traffic violations are increasing rapidly. It gives rise to major problems which are beyond human control directly and therefore there is a need for automation. An RFID reader present in the vehicle senses the vehicle speed limit on the tag as a reference speed input(attached to the speed limit signboard).Here ,the active RFID tag is used in the signboard which is having a battery supplied from the solar panel. This reference speed is given to the electronic control unit. Meanwhile the vehicle speed sensor present in the wheel gives the actual speed of the vehicle on road. The output of this ECU unit is given to the proposed braking system present in the automobiles. We here suggest two braking systems in this paper. It’s having an advantage of not supplying the power to rural area signboards. Keywords - Solar panel, RFID, ECU (Electronic control unit), speed limit boards, vehicle speed sensor, hydraulic braking, electrical fuel pump, fuel injector.
Bahan ajar materi spltv kelas x semester 1MartiwiFarisa
Pengembangan bahan ajar dibuat dengan tujuan menambah referensi belajar siswa SMA kelas X tentang materi Sistem Persamaan Linear Tiga Variabel (SPLTV). Di dalam modul ini terdapat 4 metode penyelesaian SPLTV beserta langkah-langkahnya. Semoga bermanfaat..
This learner's module discusses and help the students about the topic Systems of Linear Inequalities. It includes definition, examples, applications of Systems of Linear Inequalities.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
2. Systems of Linear Equations With Three Variables
To solve for three unknowns, we need three pieces of
numerical information about the unknowns,
3. Systems of Linear Equations With Three Variables
To solve for three unknowns, we need three pieces of
numerical information about the unknowns, i.e. three
sequations.
4. Systems of Linear Equations With Three Variables
To solve for three unknowns, we need three pieces of
numerical information about the unknowns, i.e. three
sequations. The standard method for solving systems of
linear equations is the elimination method.
5. Systems of Linear Equations With Three Variables
To solve for three unknowns, we need three pieces of
numerical information about the unknowns, i.e. three
sequations. The standard method for solving systems of
linear equations is the elimination method.
We use elimination method to extract a system of two
equations with two unknowns from the system of
three equations.
6. Systems of Linear Equations With Three Variables
To solve for three unknowns, we need three pieces of
numerical information about the unknowns, i.e. three
sequations. The standard method for solving systems of
linear equations is the elimination method.
We use elimination method to extract a system of two
equations with two unknowns from the system of
three equations. Solve the system of 2 equations and plug
the answers back to get the third answer.
7. Systems of Linear Equations With Three Variables
To solve for three unknowns, we need three pieces of
numerical information about the unknowns, i.e. three
sequations. The standard method for solving systems of
linear equations is the elimination method.
We use elimination method to extract a system of two
equations with two unknowns from the system of
three equations. Solve the system of 2 equations and plug
the answers back to get the third answer.
This is also the general method for solving a system of N
equations with N unknowns.
8. Systems of Linear Equations With Three Variables
To solve for three unknowns, we need three pieces of
numerical information about the unknowns, i.e. three
sequations. The standard method for solving systems of
linear equations is the elimination method.
We use elimination method to extract a system of two
equations with two unknowns from the system of
three equations. Solve the system of 2 equations and plug
the answers back to get the third answer.
This is also the general method for solving a system of N
equations with N unknowns. We use elimination method to
extract a system of (N – 1) equations with (N – 1) unknowns.
from the system of N equations.
9. Systems of Linear Equations With Three Variables
To solve for three unknowns, we need three pieces of
numerical information about the unknowns, i.e. three
sequations. The standard method for solving systems of
linear equations is the elimination method.
We use elimination method to extract a system of two
equations with two unknowns from the system of
three equations. Solve the system of 2 equations and plug
the answers back to get the third answer.
This is also the general method for solving a system of N
equations with N unknowns. We use elimination method to
extract a system of (N – 1) equations with (N – 1) unknowns.
from the system of N equations. Then we extract a system of
(N – 2) equations with (N – 2) unknowns from the (N – 1)
equations.
10. Systems of Linear Equations With Three Variables
To solve for three unknowns, we need three pieces of
numerical information about the unknowns, i.e. three
sequations. The standard method for solving systems of
linear equations is the elimination method.
We use elimination method to extract a system of two
equations with two unknowns from the system of
three equations. Solve the system of 2 equations and plug
the answers back to get the third answer.
This is also the general method for solving a system of N
equations with N unknowns. We use elimination method to
extract a system of (N – 1) equations with (N – 1) unknowns.
from the system of N equations. Then we extract a system of
(N – 2) equations with (N – 2) unknowns from the (N – 1)
equations. Continue this process until we get to and solve a
system of 2 equations.
11. Systems of Linear Equations With Three Variables
To solve for three unknowns, we need three pieces of
numerical information about the unknowns, i.e. three
sequations. The standard method for solving systems of
linear equations is the elimination method.
We use elimination method to extract a system of two
equations with two unknowns from the system of
three equations. Solve the system of 2 equations and plug
the answers back to get the third answer.
This is also the general method for solving a system of N
equations with N unknowns. We use elimination method to
extract a system of (N – 1) equations with (N – 1) unknowns.
from the system of N equations. Then we extract a system of
(N – 2) equations with (N – 2) unknowns from the (N – 1)
equations. Continue this process until we get to and solve a
system of 2 equations. Then plug the answers back to get
the other answers.
12. Systems of Linear Equations With Three Variables
Example A. We bought the following items:
2 hamburgers, 3 orders of fries and 3 sodas cost $13.
1 hamburger, 2 orders of fries and 2 sodas cost $8.
3 hamburgers, 2 fries, 3 sodas cost $13.
Find the price of each item.
13. Systems of Linear Equations With Three Variables
Example A. We bought the following items:
2 hamburgers, 3 orders of fries and 3 sodas cost $13.
1 hamburger, 2 orders of fries and 2 sodas cost $8.
3 hamburgers, 2 fries, 3 sodas cost $13.
Find the price of each item.
Let x = cost of a hamburger, y = cost of an order of fries,
z = cost of a soda.
14. Systems of Linear Equations With Three Variables
Example A. We bought the following items:
2 hamburgers, 3 orders of fries and 3 sodas cost $13.
1 hamburger, 2 orders of fries and 2 sodas cost $8.
3 hamburgers, 2 fries, 3 sodas cost $13.
Find the price of each item.
Let x = cost of a hamburger, y = cost of an order of fries,
z = cost of a soda.
2x + 3y + 3z = 13 E1
15. Systems of Linear Equations With Three Variables
Example A. We bought the following items:
2 hamburgers, 3 orders of fries and 3 sodas cost $13.
1 hamburger, 2 orders of fries and 2 sodas cost $8.
3 hamburgers, 2 fries, 3 sodas cost $13.
Find the price of each item.
Let x = cost of a hamburger, y = cost of an order of fries,
z = cost of a soda.
2x + 3y + 3z = 13 E1
x + 2y + 2z = 8 E2
16. Systems of Linear Equations With Three Variables
Example A. We bought the following items:
2 hamburgers, 3 orders of fries and 3 sodas cost $13.
1 hamburger, 2 orders of fries and 2 sodas cost $8.
3 hamburgers, 2 fries, 3 sodas cost $13.
Find the price of each item.
Let x = cost of a hamburger, y = cost of an order of fries,
z = cost of a soda.
{
2x + 3y + 3z = 13 E1
x + 2y + 2z = 8 E2
3x + 2y + 3z = 13 E3
17. Systems of Linear Equations With Three Variables
Example A. We bought the following items:
2 hamburgers, 3 orders of fries and 3 sodas cost $13.
1 hamburger, 2 orders of fries and 2 sodas cost $8.
3 hamburgers, 2 fries, 3 sodas cost $13.
Find the price of each item.
Let x = cost of a hamburger, y = cost of an order of fries,
z = cost of a soda.
{
2x + 3y + 3z = 13 E1
x + 2y + 2z = 8 E2
3x + 2y + 3z = 13 E3
Select x to eliminate since there is 1x in E2.
18. Systems of Linear Equations With Three Variables
Example A. We bought the following items:
2 hamburgers, 3 orders of fries and 3 sodas cost $13.
1 hamburger, 2 orders of fries and 2 sodas cost $8.
3 hamburgers, 2 fries, 3 sodas cost $13.
Find the price of each item.
Let x = cost of a hamburger, y = cost of an order of fries,
z = cost of a soda.
{
2x + 3y + 3z = 13 E1
x + 2y + 2z = 8 E2
3x + 2y + 3z = 13 E3
Select x to eliminate since there is 1x in E2.
–2*E 2 + E1:
19. Systems of Linear Equations With Three Variables
Example A. We bought the following items:
2 hamburgers, 3 orders of fries and 3 sodas cost $13.
1 hamburger, 2 orders of fries and 2 sodas cost $8.
3 hamburgers, 2 fries, 3 sodas cost $13.
Find the price of each item.
Let x = cost of a hamburger, y = cost of an order of fries,
z = cost of a soda.
{
2x + 3y + 3z = 13 E1
x + 2y + 2z = 8 E2
3x + 2y + 3z = 13 E3
Select x to eliminate since there is 1x in E2.
–2*E 2 + E1:
–2x – 4y – 4z = -16
20. Systems of Linear Equations With Three Variables
Example A. We bought the following items:
2 hamburgers, 3 orders of fries and 3 sodas cost $13.
1 hamburger, 2 orders of fries and 2 sodas cost $8.
3 hamburgers, 2 fries, 3 sodas cost $13.
Find the price of each item.
Let x = cost of a hamburger, y = cost of an order of fries,
z = cost of a soda.
{
2x + 3y + 3z = 13 E1
x + 2y + 2z = 8 E2
3x + 2y + 3z = 13 E3
Select x to eliminate since there is 1x in E2.
–2*E 2 + E1:
–2x – 4y – 4z = -16
+) 2x + 3y + 3z = 13
21. Systems of Linear Equations With Three Variables
Example A. We bought the following items:
2 hamburgers, 3 orders of fries and 3 sodas cost $13.
1 hamburger, 2 orders of fries and 2 sodas cost $8.
3 hamburgers, 2 fries, 3 sodas cost $13.
Find the price of each item.
Let x = cost of a hamburger, y = cost of an order of fries,
z = cost of a soda.
{
2x + 3y + 3z = 13 E1
x + 2y + 2z = 8 E2
3x + 2y + 3z = 13 E3
Select x to eliminate since there is 1x in E2.
–2*E 2 + E1:
–2x – 4y – 4z = -16
+) 2x + 3y + 3z = 13
0– y – z =–3
22. Systems of Linear Equations With Three Variables
Example A. We bought the following items:
2 hamburgers, 3 orders of fries and 3 sodas cost $13.
1 hamburger, 2 orders of fries and 2 sodas cost $8.
3 hamburgers, 2 fries, 3 sodas cost $13.
Find the price of each item.
Let x = cost of a hamburger, y = cost of an order of fries,
z = cost of a soda.
{
2x + 3y + 3z = 13 E1
x + 2y + 2z = 8 E2
3x + 2y + 3z = 13 E3
Select x to eliminate since there is 1x in E2.
–2*E 2 + E1: –3*E 2 + E3:
–2x – 4y – 4z = -16
+) 2x + 3y + 3z = 13
0– y – z =–3
23. Systems of Linear Equations With Three Variables
Example A. We bought the following items:
2 hamburgers, 3 orders of fries and 3 sodas cost $13.
1 hamburger, 2 orders of fries and 2 sodas cost $8.
3 hamburgers, 2 fries, 3 sodas cost $13.
Find the price of each item.
Let x = cost of a hamburger, y = cost of an order of fries,
z = cost of a soda.
{
2x + 3y + 3z = 13 E1
x + 2y + 2z = 8 E2
3x + 2y + 3z = 13 E3
Select x to eliminate since there is 1x in E2.
–2*E 2 + E1: –3*E 2 + E3:
–2x – 4y – 4z = -16 –3x – 6y – 6z = –24
+) 2x + 3y + 3z = 13
0– y – z =–3
24. Systems of Linear Equations With Three Variables
Example A. We bought the following items:
2 hamburgers, 3 orders of fries and 3 sodas cost $13.
1 hamburger, 2 orders of fries and 2 sodas cost $8.
3 hamburgers, 2 fries, 3 sodas cost $13.
Find the price of each item.
Let x = cost of a hamburger, y = cost of an order of fries,
z = cost of a soda.
{
2x + 3y + 3z = 13 E1
x + 2y + 2z = 8 E2
3x + 2y + 3z = 13 E3
Select x to eliminate since there is 1x in E2.
–2*E 2 + E1: –3*E 2 + E3:
–2x – 4y – 4z = -16 –3x – 6y – 6z = –24
+) 2x + 3y + 3z = 13 +) 3x + 2y + 3z = 13
0– y – z =–3
25. Systems of Linear Equations With Three Variables
Example A. We bought the following items:
2 hamburgers, 3 orders of fries and 3 sodas cost $13.
1 hamburger, 2 orders of fries and 2 sodas cost $8.
3 hamburgers, 2 fries, 3 sodas cost $13.
Find the price of each item.
Let x = cost of a hamburger, y = cost of an order of fries,
z = cost of a soda.
{
2x + 3y + 3z = 13 E1
x + 2y + 2z = 8 E2
3x + 2y + 3z = 13 E3
Select x to eliminate since there is 1x in E2.
–2*E 2 + E1: –3*E 2 + E3:
–2x – 4y – 4z = -16 –3x – 6y – 6z = –24
+) 2x + 3y + 3z = 13 +) 3x + 2y + 3z = 13
0– y – z =–3 0 – 4y – 3z = –11
26. Systems of Linear Equations With Three Variables
Example A. We bought the following items:
2 hamburgers, 3 orders of fries and 3 sodas cost $13.
1 hamburger, 2 orders of fries and 2 sodas cost $8.
3 hamburgers, 2 fries, 3 sodas cost $13.
Find the price of each item.
Let x = cost of a hamburger, y = cost of an order of fries,
z = cost of a soda.
{
2x + 3y + 3z = 13 E1
x + 2y + 2z = 8 E2
3x + 2y + 3z = 13 E3
Select x to eliminate since there is 1x in E2.
–2*E 2 + E1: –3*E 2 + E3:
–2x – 4y – 4z = -16 –3x – 6y – 6z = –24
+) 2x + 3y + 3z = 13 +) 3x + 2y + 3z = 13
0– y – z =–3 0 – 4y – 3z = –11
Group these two equations into a system.
27. Systems of Linear Equations With Three Variables
Hence, we've reduced the original system to two equations
with two unknowns:
–y–z =–3
{–4y – 3z = –11
28. Systems of Linear Equations With Three Variables
Hence, we've reduced the original system to two equations
with two unknowns:
–y–z =–3 (-1)
{–4y – 3z = –11
29. Systems of Linear Equations With Three Variables
Hence, we've reduced the original system to two equations
with two unknowns:
E4
{ –y–z =–3
–4y – 3z = –11
(-1)
{ y +z = 3
4y + 3z = 11 E5
30. Systems of Linear Equations With Three Variables
Hence, we've reduced the original system to two equations
with two unknowns:
E4
{ –y–z =–3
–4y – 3z = –11
(-1)
{ y +z = 3
4y + 3z = 11 E5
To eliminate z we –3*E 4 + E5:
31. Systems of Linear Equations With Three Variables
Hence, we've reduced the original system to two equations
with two unknowns:
E4
{ –y–z =–3
–4y – 3z = –11
(-1)
{ y +z = 3
4y + 3z = 11 E5
To eliminate z we –3*E 4 + E5:
–3y – 3z = –9
32. Systems of Linear Equations With Three Variables
Hence, we've reduced the original system to two equations
with two unknowns:
E4
{ –y–z =–3
–4y – 3z = –11
(-1)
{ y +z = 3
4y + 3z = 11 E5
To eliminate z we –3*E 4 + E5:
–3y – 3z = –9
+) 4y + 3z = 11
33. Systems of Linear Equations With Three Variables
Hence, we've reduced the original system to two equations
with two unknowns:
E4
{ –y–z =–3
–4y – 3z = –11
(-1)
{ y +z = 3
4y + 3z = 11 E5
To eliminate z we –3*E 4 + E5:
–3y – 3z = –9
+) 4y + 3z = 11
y+0 = 2
y= 2
34. Systems of Linear Equations With Three Variables
Hence, we've reduced the original system to two equations
with two unknowns:
E4
{ –y–z =–3
–4y – 3z = –11
(-1)
{ y +z = 3
4y + 3z = 11 E5
To eliminate z we –3*E 4 + E5:
–3y – 3z = –9
+) 4y + 3z = 11
y+0 = 2
y= 2
To get z, set 2 for y in E4:
35. Systems of Linear Equations With Three Variables
Hence, we've reduced the original system to two equations
with two unknowns:
E4
{ –y–z =–3
–4y – 3z = –11
(-1)
{ y +z = 3
4y + 3z = 11 E5
To eliminate z we –3*E 4 + E5:
–3y – 3z = –9
+) 4y + 3z = 11
y+0 = 2
y= 2
To get z, set 2 for y in E4:
2+z=3
36. Systems of Linear Equations With Three Variables
Hence, we've reduced the original system to two equations
with two unknowns:
E4
{ –y–z =–3
–4y – 3z = –11
(-1)
{ y +z = 3
4y + 3z = 11 E5
To eliminate z we –3*E 4 + E5:
–3y – 3z = –9
+) 4y + 3z = 11
y+0 = 2
y= 2
To get z, set 2 for y in E4:
2+z=3 z=1
37. Systems of Linear Equations With Three Variables
Hence, we've reduced the original system to two equations
with two unknowns:
E4
{ –y–z =–3
–4y – 3z = –11
(-1)
{ y +z = 3
4y + 3z = 11 E5
To eliminate z we –3*E 4 + E5:
–3y – 3z = –9
+) 4y + 3z = 11
y+0 = 2
y= 2
To get z, set 2 for y in E4:
2+z=3 z=1
For x, set 2 for y , set 1 for z in E2: x + 2y + 2z = 8
38. Systems of Linear Equations With Three Variables
Hence, we've reduced the original system to two equations
with two unknowns:
E4
{ –y–z =–3
–4y – 3z = –11
(-1)
{ y +z = 3
4y + 3z = 11 E5
To eliminate z we –3*E 4 + E5:
–3y – 3z = –9
+) 4y + 3z = 11
y+0 = 2
y= 2
To get z, set 2 for y in E4:
2+z=3 z=1
For x, set 2 for y , set 1 for z in E2: x + 2y + 2z = 8
x + 2(2) + 2(1) = 8
39. Systems of Linear Equations With Three Variables
Hence, we've reduced the original system to two equations
with two unknowns:
E4
{ –y–z =–3
–4y – 3z = –11
(-1)
{ y +z = 3
4y + 3z = 11 E5
To eliminate z we –3*E 4 + E5:
–3y – 3z = –9
+) 4y + 3z = 11
y+0 = 2
y= 2
To get z, set 2 for y in E4:
2+z=3 z=1
For x, set 2 for y , set 1 for z in E2: x + 2y + 2z = 8
x + 2(2) + 2(1) = 8
x+6 =8
40. Systems of Linear Equations With Three Variables
Hence, we've reduced the original system to two equations
with two unknowns:
E4
{ –y–z =–3
–4y – 3z = –11
(-1)
{ y +z = 3
4y + 3z = 11 E5
To eliminate z we –3*E 4 + E5:
–3y – 3z = –9
+) 4y + 3z = 11
y+0 = 2
y= 2
To get z, set 2 for y in E4:
2+z=3 z=1
For x, set 2 for y , set 1 for z in E2: x + 2y + 2z = 8
x + 2(2) + 2(1) = 8
x+6 =8
x=2
41. Systems of Linear Equations With Three Variables
Hence, we've reduced the original system to two equations
with two unknowns:
E4
{ –y–z =–3
–4y – 3z = –11
(-1)
{ y +z = 3
4y + 3z = 11 E5
To eliminate z we –3*E 4 + E5:
–3y – 3z = –9
+) 4y + 3z = 11
y+0 = 2
y= 2
To get z, set 2 for y in E4:
2+z=3 z=1
For x, set 2 for y , set 1 for z in E2: x + 2y + 2z = 8
x + 2(2) + 2(1) = 8
x+6 =8
x=2
Hence the solution is (2, 2, 1).