A problem is provided which is solved by using graphical and analytical method of linear programming method and then it is solved by using geometrical concept and algebraic concept of simplex method.
A problem is provided which is solved by using graphical and analytical method of linear programming method and then it is solved by using geometrical concept and algebraic concept of simplex method.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Unit 8 - Information and Communication Technology (Paper I).pdf
LPP, Duality and Game Theory
1. Linear Programming Problem, Duality
and Game Theory
(February 21{26, 2015)
Dr. Purnima Pandit
Assistant Professor
Department of Applied Mathematics
Faculty of Technology and Engg.
The Maharaja Sayajirao University of Baroda
3. Elements
Variables
Objective function
Constraints
Obtain values of the variables
that optimizes the objective function
( satisfying the constraints )
4. • Linear programming (LP) problems are
optimization problems where the objective
function and the constraints of the problem are
all linear.
• Many practical problems in operations research
can be expressed as linear programming
problems.
• A lot of work is generated on the research of
specialized algorithms for the solutions of
specific LP problems.
• In mathematical optimization theory, the
simplex algorithm of George Dantzig is the
fundamental technique for numerical solution of
the LP problem.
Introduction to LPP
5. Constrained Optimization
If objective function and constraints all
linear, this is “linear programming”
Observation: minimum must lie at corner
of region formed by constraints
Simplex method: move from vertex to
vertex, minimizing objective function
6. LP Model Formulation
Decision variables
mathematical symbols representing levels of activity of an
operation
Objective function
a linear relationship reflecting the objective of an operation
most frequent objective of business firms is to maximize
profit
most frequent objective of individual operational units (such
as a production or packaging department) is to minimize cost
Constraint
a linear relationship representing a restriction on decision
making
8. MCQ
1. Which of the following is not correct about LPP?
(a) All constraints must be linear relationship.
(b) Objective function must be linear
(c) All the constraints and decision variables must be of either ≤ or ≥ type.
(d) All decision variables must be non-negative.
2. A constraint in LPP restricts
(a) value of objective function
(b) value of decision variable.
(c) use of available resource
(d) uncertainity of optimum value.
3. Which of the following is correct?
(a) LP takes into consideration the effect of time and uncertainity
(b) An LPP can have only two decision variables.
(c) Decision variables in an LPP may be more or less than the number of constraints
(d) LP deals with problems involving only a single objective.
9. LP Model: Example
Labor Clay Revenue
PRODUCT (hr/unit) (lb/unit) ($/unit)
Bowl 1 4 40
Mug 2 3 50
There are 40 hours of labor and 120 pounds of clay
available each day
Decision variables
x1 = number of bowls to produce
x2 = number of mugs to produce
RESOURCE REQUIREMENTS
11. Graphical Solution Method
1. Plot model constraint on a set of coordinates
in a plane
2. Plot objective function to find the point on
boundary of this space that maximizes (or
minimizes) value of objective function
2 dimensional problems
Observation: Optimum must lie at corner
of region formed by constraints
12. Graphical Solution Method
1. Plot model constraint on a set of coordinates
in a plane
2. Identify the feasible solution space on the
graph where all constraints are satisfied
simultaneously
3. Plot objective function to find the point on
boundary of this space that maximizes (or
minimizes) value of objective function
15. ISO – Profit:
4 x1 + 3 x2 120 lb
x1 + 2 x2 40 hr
Area common to
both constraints
50 –
40 –
30 –
20 –
10 –
0 – |
10
|
60
|
50
|
20
|
30
|
40 x1
x2
16. 4x1 + 3x2 120 lb
x1 + 2x2 40 hr
40 –
30 –
20 –
10 –
0 –
B
|
10
|
20
|
30
|
40 x1
x2
C
A
Z = 70x1 + 20x2
Optimal point:
x1 = 30 bowls
x2 = 0 mugs
Z = $2,100
Objective Function
17. Minimization Problem
CHEMICAL CONTRIBUTION
Brand Nitrogen (lb/bag) Phosphate (lb/bag)
Gro-plus 2 4
Crop-fast 4 3
Minimize Z = $6x1 + $3x2
subject to
2x1 + 4x2 16 lb of nitrogen
4x1 + 3x2 24 lb of phosphate
x1, x2 0
18. 14 –
12 –
10 –
8 –
6 –
4 –
2 –
0 – |
2
|
4
|
6
|
8
|
10
|
12
|
14 x1
x2
A
B
C
Graphical Solution
x1 = 0 bags of Gro-plus
x2 = 8 bags of Crop-fast
Z = $24
Z = 6x1 + 3x2
19. 19
Two type of Constraints
Binding and Nonbinding constraints:
A constraint is binding if the left-hand and right-
hand side of the constraint are equal when the
optimal values of the decision variables are
substituted into the constraint.
A constraint is nonbinding if the left-hand side
and the right-hand side of the constraint are
unequal when the optimal values of the decision
variables are substituted into the constraint.
20. 20
Special Cases of Graphical Solution
• Some LPs have an infinite number of solutions
(alternative or multiple optimal solutions).
• Some LPs have no feasible solution (infeasible
LPs).
• Some LPs are unbounded: There are points in
the feasible region with arbitrarily large (in a
maximization problem) z-values.
21. 21
Alternative or Multiple Solutions
Any point (solution)
falling on line segment
AE will yield an optimal
solution with the same
objective value
X1
X2
10 20 30 40
1020304050
Feasible Region
F
50
60
z = 60
z = 100
z = 120
A
B
C
D
E
22. 22
No feasible solution
X1
X2
10 20 30 40
1020304050
No Feasible Region
50
60
x1 >= 0
x2 >=0
No feasible region exists
Some LPs have no
solution. Consider
the following
formulation:
23. 23
Unbounded LP
X11 2 3 4
1
2
3
4
X2
5
6
5 6
A
B
C
Feasible Region
z = 4
z = 6
D
The constraints are
satisfied by all points
bounded by the x2 axis
and on or above AB and
CD.
There are points in the
feasible region which
will produce arbitrarily
large z-values
(unbounded LP).
24. MCQ
1.A feasible solution to an LPP
(a) must satisfy all the problem's constraints simultaneously
(b) must be a corner point of the feasible region.
(c) need not satisfy all the constraints, only some of them.
(d) must optimize the value of the objective function.
2. An iso-profit line represents
(a) An infinite number of solutions all of which yield the same profit.
(b) an infinite numbe of optimum solutions.
(c) an infinite number of solutions all of which uses the same resources.
(d) a boundary of feasible region.
3. If an iso-profit line yielding the optimum solution coincided with a constraint
line, then
(a) the soultion is unbounded.
(b) the solution is infeasible.
(c) the constraint which coincides is redundant.
(d) none of above.
25. MCQ
4. Using graphic method, the optimum solution of the LPP of
Maximizing Z = 10x+15y
s.t.
2x+y 26, x+2y 28 y-x 5 and x 0 and y 0
is obtained as:
(a) x=8 and y =10
(b) x=6 and y =1
(c) x=6 and y =10
(d)x=8 and y =8
26. Standard form
Standard form is a basic way of describing a LP
problem.
It consists of 3 parts:
A linear function to be maximized
maximize c1x1 + c2x2 + … + cnxn
Problem constraints
subject to a11x1 + a12x2 + … + a1nxn < b1
a21x1 + a22x2 + … + a2nxn < b2
…
am1x1 + am2x2 + … + amnxn < bm
Non-negative variables
x1> 0, x2 > 0,... , xn > 0
27. Step 0 – Obtain Canonical Form
General Simplex LP model:
min (or max) z = ci xi
s.t.
A x = b
x 0
In order to get and maintain this form, use
slack, if x b, then x + slack = b
surplus, if x b, then x - surplus = b
artificial variables (sometimes need to be added to
ensure all variables 0 )
IMPORTANT: Simplex only deals with equalities
28. Simplex method
Solve the following problem using the simplex
method
Maximize Z = 3X1+ 5X2
Subject to
X1 4
2 X2 12
3X1 +2X2 18
X1 , X2 0
29. Simplex method
Standard form
Maximize Z,
Subject to
Z - 3X1- 5X2 = 0
X1 + S1 = 4
2 X2 + S2 = 12
3X1 +2X2 + S3 = 18
X1 , X2, S1, S2, S3 0
Sometimes it is called
the augmented form of
the problem because the
original form has been
augmented by some
supplementary variables
needed to apply the
simplex method
30. Definitions
A basic solution is an augmented corner point solution.
A basic solution has the following properties:
1. Each variable is designated as either a non-basic variable or a
basic variable.
2. The number of basic variables equals the number of functional
constraints. Therefore, the number of non-basic variables
equals the total number of variables minus the number of
functional constraints.
3. The non-basic variables are set equal to zero.
4. The values of the basic variables are obtained as simultaneous
solution of the system of equations (functional constraints in
augmented form). The set of basic variables are called “basis”
5. If the basic variables satisfy the non-negativity constraints, the
basic solution is a Basic Feasible (BF) solution.
32. Optimality test
By investigating the last row of the initial
tableau, we find that there are some
negative numbers. Therefore, the current
solution is not optimal
33. Basic
variable
X1 X2 S1 S2 S3 RHS
S1 1 0 1 0 0 4
X2 0 1 0 1/2 0 6
S3 3 0 0 -1 1 6
Z -3 0 0 5/2 0 30
The most negative
value; therefore, X1 is
the entering variable
The smallest ratio is 6/3
=2; therefore, S3 is the
leaving variable
This solution is not optimal, since there is a negative numbers in the last
row
34. Apply the same rules we will obtain this solution:
Basic
variable
X1 X2 S1 S2 S3 RHS
S1 0 0 1 1/3 -1/3 2
X2 0 1 0 1/2 0 6
X1 1 0 0 -1/3 1/3 2
Z 0 0 0 3/2 1 36
This solution is optimal; since there is no negative solution in the last row: basic
variables are X1 = 2, X2 = 6 and S1 = 2; the non-basic variables are S2 = S3 = 0
Z = 36
35. Special cases of linear
programming
Infeasible solution
Multiple solution (infinitely many solution)
Unbounded solution
Degenerated solution
36. Notes on the Simplex tableau
1. In any Simplex tableau, the intersection of any basic variable with itself is always one
and the rest of the column is zeroes.
2. In any simplex tableau, the objective function row (Z row) is always in terms of the
nonbasic variables. This means that under any basic variable (in any tableau) there is
a zero in the Z row. For the non basic there is no condition ( it can take any value in
this row).
3. If there is a zero under one or more nonbasic variables in the last tableau (optimal
solution tableau), then there is a multiple optimal solution.
4. When determining the leaving variable of any tableau, if there is no positive ratio (all the
entries in the pivot column are negative and zeroes), then the solution is unbounded.
5. If there is a tie (more than one variables have the same most negative or positive) in
determining the entering variable, choose any variable to be the entering one.
6. If there is a tie in determining the leaving variable, choose any one to be the leaving
variable. In this case a zero will appear in RHS column; therefore, a “cycle” will occur,
this means that the value of the objective function will be the same for several
iterations.
7. A Solution that has a basic variable with zero value is called a “degenerate solution”.
8. If there is no Artificial variables in the problem, there is no room for “infeasible solution”
37. Example Problem
Maximize Z = 5x1 + 2x2 + x3
subject to
x1 + 3x2 - x3 ≤ 6,
x2 + x3 ≤ 4,
3x1 + x2 ≤ 7,
x1, x2, x3 ≥ 0.
39. LP: Dual Formation
Formulation of the Dual from the Prime:
Standardize constraint set:
Max problem - all to (≤) type /Min problem, all to (≥).
Replace equality constraint with two inequality ones.
Transforming standardized Prime to Dual following rules.
No. of Variables in Dual = No. of Constraints in Prime
No. of Constraints in Dual = No. of Variables in Prime
Cj in Prime → bi in Dual / bi in Prime → Cj in Dual
aij in Prime → aji in Dual
46. Rules, Strategies, Payoffs, and
Equilibrium
A game is a contest involving two or more
decision makers, each of whom wants to win
A two-person game involves two parties (X
and Y)
A zero-sum game means that the sum of losses
for one player must equal the sum of gains for the
other. Thus, the overall sum is zero
47. Minimax Criterion
The value of a game is the average or expected game outcome if the game is
played an infinite number of times
A saddle point indicates that each player has a pure strategy i.e., the strategy is
followed no matter what the opponent does
48. Pure Strategy - Minimax Criterion
Player Y’s
Strategies
Minimum Row
Number
Y1 Y2
Player X’s
strategies
X1 10 6 6
X2 -12 2 -12
Maximum
Column Number
10 6
49. Mixed Strategy Game
When there is no saddle point:
The most common way to solve a mixed strategy is to use the expected gain or loss
approach
A player plays each strategy a particular percentage of the time so that the expected
value of the game does not depend upon what the opponent does
Y1
P
Y2
1-P
Expected Gain
X1
Q
4 2 4P+2(1-P)
X2
1-Q
1 10 1P+10(1-p)
4Q+1(1-Q) 2Q+10(1-q)
50. Solving for P & Q
4P+2(1-P) = 1P+10(1-P)
or: P = 8/11 and 1-p = 3/11
Expected payoff:
1P+10(1-P)
=1(8/11)+10(3/11)
EPX= 3.46
4Q+1(1-Q)=2Q+10(1-q)
or: Q=9/11 and 1-Q = 2/11
Expected payoff:
EPY=3.46
55. Dominance
A strategy can be eliminated if all its game’s
outcomes are the same or worse than the
corresponding outcomes of another strategy
A strategy for a player is said to be dominated
if the player can always do as well or better
playing another strategy
56. Domination
S-56
Y1 Y2
X1 4 3
X2 2 20
X3 1 1
Initial Game
X3 is a dominated strategy as player X can
always do better with X1 or X2
Y1 Y2
X1 4 3
X2 2 20
Revised Game