Tag: COMPUTER SCIENCE AND APPLICATIONS UGC Notes

  • UGC NET Computer Science Unit 1 SET-1

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    Expected Questions with Solutions

    Unit – 1 : Discrete Structures and Optimization


    A. Mathematical Logic (Propositional & Predicate Logic, Inference)

    1. ¬∃x Q(x) is equivalent to:
      (1) ∃x ¬Q(x)
      (2) ∀x ¬Q(x)
      (3) ¬∀x ¬Q(x)
      (4) ∀x Q(x)
      Answer: (2)
      Explanation: Negation of an existential becomes universal negation: ¬∃x Q(x) ≡ ∀x ¬Q(x).

    2. From (P → Q) ∧ (R → S) and P ∨ R, what follows (Y)?
      (1) P ∨ R
      (2) P ∨ S
      (3) Q ∨ R
      (4) Q ∨ S
      Answer: (4)
      Explanation: If P then Q; if R then S. From P∨R we derive Q∨S.

    3. “Agra and Gwalior are both in India.” Student wrote: In(Agra,India) ∨ In(Gwalior,India). This is:
      (1) Syntactically valid but wrong meaning
      (2) Syntactically valid and correct meaning
      (3) Syntactically invalid but correct meaning
      (4) Invalid and wrong meaning
      Answer: (1)
      Explanation: Sentence requires AND; OR changes meaning. Syntax is valid but semantics wrong.

    4. Knowledge base: ∃x AsHighAs(x, Everest). After existential instantiation we get
      (a) AsHighAs(Everest, Everest)
      (b) AsHighAs(Kilimanjaro, Everest)
      Options:
      (1) Both (a) and (b) are sound conclusions.
      (2) Both (a) and (b) are unsound conclusions.
      (3) (a) is sound but (b) is unsound.
      (4) (a) is unsound but (b) is sound.
      Answer: (2) — (In some papers the key differs; see explanation below.)
      Short explanation: Existential instantiation must introduce a new arbitrary constant; substituting existing named constants (Everest, Kilimanjaro) is unsound.

    5. Given FOL: ((R ∨ Q) ∧ (P ∨ Q)). Which is equivalent?
      Options show same expression repeated in the paper; all choices are identical.
      Answer: Any option (they are identical)
      Explanation: The options are identical textually; the expression stands as given.


    B. Sets & Relations (Set operations, Equivalence, Partial Orders)

    1. If Aᵢ = {−i, …, −1, 0, 1, …, i} for i ≥ 1, then ⋃_{i=1}^{∞} Aᵢ =
      (1) Z
      (2) Q
      (3) R
      (4) C
      Answer: (1) Z
      Explanation: As i → ∞ the union includes all integers.

    2. Which of the following relations on {0,1,2,3} is an equivalence relation?
      (1) {(0,0),(0,2),(2,0),(2,2),(2,3),(3,2),(3,3)}
      (2) {(0,0),(1,1),(2,2),(3,3)}
      (3) {(0,0),(0,1),(0,2),(1,0),(1,1),(1,2),(2,0)}
      (4) {(0,0),(0,2),(2,3),(1,1),(2,2)}
      Answer: (2)
      Explanation: Identity relation has reflexivity, symmetry, transitivity.

    3. Which relation on functions f,g: Z→Z is an equivalence relation?
      (1) f(x) − g(x) = 1
      (2) f(0)=g(0) OR f(1)=g(1)
      (3) f(0)=g(1) AND f(1)=g(0)
      (4) f(x) − g(x) = k (for some integer k)
      Answer: (4)
      Explanation: Difference-by-constant is reflexive (k=0), symmetric (negate), transitive (sum).

    4. Which is true?
      (1) (Z, ≤) is not totally ordered
      (2) ⊆ is a partial order on P(S)
      (3) (Z, ≠) is a poset
      (4) Directed graph is not a partial order
      Answer: (2)
      Explanation: Subset relation is reflexive, antisymmetric and transitive.

    5. Match:
      (a) ∀x∀y (f(x)=f(y) → x=y)
      (b) ∀y ∃x (f(x)=y)
      (c) ∀x f(x)=k
      (i) Constant (ii) Injective (iii) Surjective
      Codes: (1) (i)(ii)(iii) (2) (iii)(ii)(i) (3) (ii)(i)(iii) (4) (ii)(iii)(i)
      Answer: (4)
      Explanation: (a) injective, (b) surjective, (c) constant.


    C. Counting, Pigeonhole, Permutations & Combinations, Induction

    1. Consider A = {1,2,…,1000}. How many members are divisible by 3 or 5 or both?
      (A) 533 (B) 599 (C) 467 (D) 66
      Answer: (paper keys vary — mathematically = C) (C) 467; several uploaded keys show (A) 533 (answer-key discrepancy).
      Explanation: Using inclusion–exclusion: ⌊1000/3⌋+⌊1000/5⌋−⌊1000/15⌋ = 333+200−66 = 467.

    2. How many multiples of 6 between 0 and 100, and between −6 and 34?
      (1) 16 and 6
      (2) 17 and 6
      (3) 17 and 7
      (4) 16 and 7
      Answer: (3)
      Explanation: Multiples of 6 from 0 to 100: 0,6,…,96 → 17 numbers (including 0). From −6 to 34: −6,0,6,…,30 → 7 numbers.

    3. Number of atomic events (five-card poker hands)?
      (1) 2,598,960 (2) 3,468,960 (3) 3,958,590 (4) 2,645,590
      Answer: (1)
      Explanation: C(52,5) = 2,598,960.

    4. Number of ways to arrange letters of “DISCRETE”:
      (1) 8! (2) 8!/2! (3) 8!/3! (4) 7!
      Answer: (2)
      Explanation: 8 letters with E repeated twice → 8!/2!.

    5. Number of permutations of 5 distinct objects taken 3 at a time:
      (A) 60 (B) 10 (C) 125 (D) 5
      Answer: (A) 60
      Explanation: P(5,3)=5×4×3=60.

    6. Pigeonhole: 13 pigeons into 12 holes ⇒ some hole has at least how many pigeons?
      (A)1 (B)2 (C)13 (D)12
      Answer: (B) 2
      Explanation: Ceiling(13/12)=2.

    7. Probability: rolling two dice, P(sum=7):
      (1) 1/6 (2) 1/9 (3) 1/12 (4) 1/36
      Answer: (1) 1/6
      Explanation: 6 favourable / 36 total.

    8. Flipping coin 10 times: number of atomic events:
      (1) 2⁵ (2) 2¹⁰ (3) 10! (4) 100
      Answer: (2) 2¹⁰
      Explanation: 2 outcomes per flip → 2¹⁰ sequences.


    D. Probability & Bayes

    1. Selecting k uniformly from {1..1,000,000} a million times: P(k=1 appears at least once):
      (A) 0.5 (B) 0.704 (C) 0.632121 (D) 0.68
      Answer: (C) ≈ 0.632121
      Explanation: P(not 1 each trial) = 1 − 1/1e6; (1 − 1/1e6)^{1e6} ≈ e^{−1}; 1 − e^{−1} ≈ 0.632121.

    2. If A and B independent with P(A)=0.3, P(B)=0.4, then P(A∪B)=
      (A) 0.12 (B) 0.58 (C) 0.7 (D) 0.3
      Answer: (B) 0.58
      Explanation: P(A)+P(B)−P(A)P(B)=0.3+0.4−0.12=0.58.

    3. Bayes formula: P(H|E)= ?
      (A) P(E|H)P(E)/P(H)
      (B) P(E|H)P(H)/P(E)
      (C) P(H)P(E)
      (D) P(E)/P(H)
      Answer: (B)
      Explanation: Standard Bayes’ rule.

    4. If A,B,C are events, which is true?
      (1) P(A∪B) = P(A)+P(B)−P(AB)
      (2) P(A∪B∪C) = P(A)+P(B)+P(C)
      (3) P(A∪B) ≥ P(A)
      (4) P(A∪B) ≤ P(A)
      Answer: (3)
      Explanation: Union never decreases probability.


    E. Graph Theory & Trees

    1. A tree with two vertices degree 4, one vertex degree 3, one vertex degree 2, rest degree 1 → total vertices?
      (A)5 (B)n−3 (C)20 (D)11
      Answer: (D) 11
      Explanation: Let number of leaves = x, n = x+4; sum degrees = 13 + x = 2(n−1)=2x+6 → x=7 → n=11.

    2. This graph is a:
      (A) Complete Graph (B) Bipartite Graph (C) Hamiltonian Graph (D) All of the above
      Answer: (D)
      Explanation: Given graph (in paper) satisfies all three properties.

    3. Hamiltonian graph G (|V|=n≥3). Which is true?
      (1) deg(v) ≥ n/2 for each v
      (2) |E| ≥ ½ (n−1)(n−2) + 2
      (3) deg(v)+deg(w) ≥ n whenever v and w not adjacent
      (4) All of the above
      Answer: (4)
      Explanation: These are sufficient conditions referenced in the paper.

    4. Strict (full) binary tree with 19 leaves has how many nodes?
      (1) cannot have more than 37 nodes
      (2) has exactly 37 nodes
      (3) has exactly 35 nodes
      (4) cannot have more than 35 nodes
      Answer: (2) 37
      Explanation: In full binary tree: number nodes = 2L − 1.

    5. Shortest path algorithm allowing negative edges (no negative cycles):
      (1) Dijkstra (2) Bellman–Ford (3) Prim (4) Floyd only
      Answer: (2) Bellman–Ford
      Explanation: Bellman–Ford handles negative edge weights.

    6. Chromatic number of a cycle C_n is:
      (1) 2 (if n even), 3 (if n odd)
      (2) 3 (if n even), 2 (if n odd)
      (3) Always 2
      (4) Always 3
      Answer: (1)
      Explanation: Even cycles are 2-colorable, odd cycles need 3 colors.


    F. Boolean Algebra & Automata

    1. Consensus theorem identity:
      (A) XY + X′Z + YZ = XY + X′Z
      (B) X + X′Y = X + Y
      (C) XX′ = 0
      (D) X + X′ = 1
      Answer: (A)
      Explanation: Consensus term YZ redundant and can be removed.

    2. Simplify (A + B)(A + B′):
      (1) A (2) B (3) AB (4) A′
      Answer: (1) A
      Explanation: Use absorption law: (A+B)(A+B′)=A.

    3. Order of algorithm determining whether a Boolean function of n variables outputs 1 (brute force):
      (1) Logarithmic (2) Linear (3) Quadratic (4) Exponential
      Answer: (4) Exponential
      Explanation: Exhaustive checking needs O(2^n) evaluations.


    G. Group Theory & Coding (selected Unit-1 items present)

    1. Which structure is always commutative?
      (A) Any group (B) Abelian group (C) Semigroup (D) Non-abelian group
      Answer: (B) Abelian group
      Explanation: Abelian = group where operation commutes.

    2. Order of a permutation expressed as disjoint cycles equals:
      (A) sum of cycle lengths (B) product of cycle lengths (C) LCM of cycle lengths (D) minimum cycle length
      Answer: (C) LCM
      Explanation: The permutation repeats after LCM of cycle lengths.

    3. Hamming bound uses which RHS term for q-ary n-length code:
      Options: (1) q^n (2) q^t (3) q^{−n} (4) q^{−t}
      Answer: (1) q^n
      Explanation: Sphere-packing bound compares sum of spheres to q^n.


    H. Optimization (LPP, Transportation, Assignment, PERT/CPM)

    1. Given LPP with constraints (in paper) — which is true?
      (1) Solution x₁=100, x₂=0, x₃=0
      (2) Unbounded solution
      (3) No solution
      (4) Solution x₁=50, x₂=70, x₃=60
      Answer: (3) No solution (as per paper key)
      Explanation: The constraints as given are inconsistent → infeasible.

    2. Transportation degeneracy occurs when number of basic variables <
      (A) m + n (B) m + n − 1 (C) mn (D) m − n
      Answer: (B) m + n − 1
      Explanation: Basic feasible solution should have m+n−1 basics; fewer indicates degeneracy.

    3. A basic feasible solution of LP corresponds to:
      (A) any interior point (B) a vertex of polytope (C) centroid (D) none
      Answer: (B) a vertex (extreme point)
      Explanation: Basic feasible solutions map to vertices of feasible region.

    4. PERT uses which distribution for activity times?
      (1) Exponential (2) Normal (3) Beta (4) Poisson
      Answer: (3) Beta
      Explanation: PERT models activity time with Beta distribution (approx. using optimistic, pessimistic, most likely).

    5. In PERT/CPM the critical path is:
      (1) minimum duration path (2) maximum duration path (3) any path with slack (4) both (2) and (3)
      Answer: (2) maximum duration path (equivalently zero slack)
      Explanation: Critical path has longest duration; activities on it have zero total float.

    6. Which of the following is FALSE about convex minimization?
      (1) Local minimum ⇒ global minimum
      (2) Global minima set is convex
      (3) Global minima set is concave
      (4) Strictly convex function has unique minimum
      Answer: (3) (false)
      Explanation: Global minima set of convex functions is convex (not concave).

    MORE PRACTICE CUM EXPECTED QUESTIONS

    Q41.

    If A,B,C are events in a probability space, then which of the following is TRUE?
    (1) P(AB)=P(A)+P(B)P(AB)
    (2) P(ABC)=P(A)+P(B)+P(C)
    (3) P(AB)P(A)
    (4) P(AB)P(A)

    Answer: (3)
    Explanation: A union can never reduce probability: P(AB)P(A)


    Q42.

    The number of atomic events in the sample space when a fair coin is flipped 10 times is:
    (1) 25
    (2) 210
    (3) 10!
    (4) 100

    Answer: (2)
    Explanation: Each flip has 2 outcomes → 210 sequences.


    Q43.

    A strictly binary tree with L leaves has
    (1) 2L+1 nodes
    (2) 2L1 nodes
    (3) L1 nodes
    (4) 2L nodes

    Answer: (2)
    Explanation: Full binary tree nodes = 2L1.


    Q44.

    If a function f is injective, then
    (1) f(x)=f(y)x=y
    (2) f(x)=k for all x
    (3) f is onto
    (4) f(x) never repeats

    Answer: (1)
    Explanation: Injective means one-to-one.


    Q45.

    If a relation R is reflexive and symmetric but not transitive, then R is
    (1) Equivalence relation
    (2) Partial order
    (3) Not an equivalence relation
    (4) Total order

    Answer: (3)
    Explanation: Equivalence requires all 3; missing transitivity → not equivalence.


    Q46.

    For a universal set U and subset AU:
    (1) AA=U
    (2) AA=U
    (3) (A)=
    (4) AA

    Answer: (1)
    Explanation: A set union its complement gives the universal set.


    Q47.

    Number of arrangements of the letters of “DISCRETE”:
    (1) 8!
    (2) 8!/2!
    (3) 8!/3!
    (4) 7!

    Answer: (2)
    Explanation: “E” is repeated twice → divide by 2!.


    Q48.

    If a fair die is rolled twice, the probability that the sum is 7 is
    (1) 1/6
    (2) 1/9
    (3) 1/12
    (4) 1/36

    Answer: (1)
    Explanation: 6 favourable outcomes → 6/36 = 1/6.


    Q49.

    The chromatic number of the cycle graph Cn is:
    (1) 2 if n even; 3 if n odd
    (2) 3 if n even; 2 if n odd
    (3) Always 2
    (4) Always 3

    Answer: (1)
    Explanation: Even cycle: bipartite (2 colors). Odd cycle: needs 3.


    Q50.

    A relation R on A is antisymmetric if
    (1) aRb and bRaa=b
    (2) aRbbRa
    (3) R is neither symmetric nor reflexive
    (4) aRb and bRaab

    Answer: (1)
    Explanation: This is the definition of antisymmetry.


    Q51.

    Simplify the Boolean expression:
    (A+B)(A+B)
    (1) A
    (2) B
    (3) AB
    (4) A’

    Answer: (1)
    Explanation: Use absorption: (A + B)(A + B′) = A.


    Q52.

    Shortest-path algorithm that works with negative edge weights (but no negative cycles):
    (1) Dijkstra
    (2) Bellman–Ford
    (3) Prim
    (4) Floyd–Warshall only

    Answer: (2)
    Explanation: Bellman–Ford is designed for graphs with negative edges.


    Q53.

    PERT uses which probability distribution for activity times?
    (1) Exponential
    (2) Normal
    (3) Beta
    (4) Poisson

    Answer: (3)
    Explanation: PERT uses 3-point Beta distribution.


    Q54.

    The number of atomic events when flipping a fair coin n times is
    (1) n
    (2) n!
    (3) 2n
    (4) n2

    Answer: (3)
    Explanation: Each flip has 2 outcomes → 2n.


    Q55.

    In a graph, the degree sum formula says:
    (1) Sum of degrees = E
    (2) Sum of degrees = 2E
    (3) Sum of degrees = V
    (4) Sum of degrees = V + E

    Answer: (2)
    Explanation: Handshaking lemma: ∑deg(v) = 2|E|.


    Q56.

    If a full binary tree has 15 internal nodes, the number of leaves is:
    (1) 14
    (2) 15
    (3) 16
    (4) 30

    Answer: (3)
    Explanation: In full binary tree: L = I + 1 = 16.


    Q57.

    A relation R on A is transitive if
    (1) aRb and bRcaRc
    (2) aRb=bRa
    (3) aRa for all a
    (4) None

    Answer: (1)
    Explanation: Direct definition of transitivity.


    Q58.

    Probability of getting exactly 2 heads in 4 fair coin tosses:
    (1) 1/4
    (2) 3/8
    (3) 6/16
    (4) 1/2

    Answer: (3)
    Explanation: C(4,2)=6; total outcomes=16 → 6/16.


    Q59.

    A graph is bipartite if and only if
    (1) It has no odd cycle
    (2) It is planar
    (3) It is complete
    (4) It has Euler path

    Answer: (1)
    Explanation: Characterization of bipartite graphs: no odd-length cycle.


    Q60.

    The value of C(10, 3) is
    (1) 120
    (2) 720
    (3) 10
    (4) 3

    Answer: (1)
    Explanation: 10C3 = 120.


    Q61.

    If events A and B are independent, then
    (1) P(AB)=P(A)
    (2) P(AB)=0
    (3) P(AB)>P(A)
    (4) None of these

    Answer: (1)
    Explanation: Independence implies P(A|B)=P(A).


    Q62.

    The number of spanning trees in a tree with 10 vertices is
    (1) 1
    (2) 9
    (3) 10
    (4) 0

    Answer: (1)
    Explanation: A tree already IS a spanning tree → exactly one.


    Q63.

    In Boolean algebra, the identity element for AND is
    (1) 0
    (2) 1
    (3) X
    (4) X’

    Answer: (2)
    Explanation: A·1 = A.


    Q64.

    The number of subsets of a 5-element set is
    (1) 10
    (2) 25
    (3) 32
    (4) 64

    Answer: (3)
    Explanation: 2⁵ = 32.


    Q65.

    For a simple graph with 7 vertices, maximum possible number of edges is
    (1) 21
    (2) 7
    (3) 14
    (4) 35

    Answer: (1)
    Explanation: Maximum edges = n(n−1)/2 = 7×6/2 = 21.


    Q66.

    Which is a valid probability value?
    (1) −0.2
    (2) 1.4
    (3) 0.5
    (4) 2

    Answer: (3)
    Explanation: Probability must be in [0,1].


    Q67.

    A function f: A → B is surjective if
    (1) every element of A is mapped
    (2) f repeats values
    (3) every element of B has a pre-image
    (4) f is injective

    Answer: (3)
    Explanation: Surjection means onto.


    Q68.

    The chromatic number of a tree is
    (1) 1
    (2) 2
    (3) 3
    (4) n

    Answer: (2)
    Explanation: Any tree is bipartite.


    Q69.

    The probability of an impossible event is
    (1) 1
    (2) 1/2
    (3) 0
    (4) Undefined

    Answer: (3)
    Explanation: Impossible event = probability 0.


    Q70.

    In LP, the optimal solution always occurs at
    (1) any point
    (2) interior point
    (3) a corner point
    (4) outside feasible region

    Answer: (3)
    Explanation: Fundamental theorem of linear programming.


    Q71.

    In adjacency-matrix representation of a graph with n vertices, the matrix size is
    (1) n
    (2) n²
    (3) n×n
    (4) n−1

    Answer: (3)
    Explanation: Adjacency matrix is n×n.


    Q72.

    If P(A)=0.4, P(B)=0.5, and A,B mutually exclusive, then P(A∪B)=
    (1) 0.9
    (2) 0.2
    (3) 1
    (4) 0.1

    Answer: (1)
    Explanation: No overlap → sum of probabilities.


    Q73.

    If a∈A, then in set-builder form A =
    (1) {x : x ≠ a}
    (2) {x : x = a}
    (3) {x : x has property of A}
    (4) All

    Answer: (3)
    Explanation: Set-builder describes all elements satisfying the defining property.


    Q74.

    A graph with all even degrees is
    (1) Hamiltonian
    (2) Eulerian
    (3) Tree
    (4) Complete

    Answer: (2)
    Explanation: Eulerian graph ↔ connected + all vertices have even degree.


    Q75.

    If P(A)=0.6 and P(A′)=0.4, then
    (1) Correct
    (2) Incorrect
    (3) Cannot say
    (4) None

    Answer: (1)
    Explanation: A + A′ = 1.


    Q76.

    Number of edges in a tree with 30 vertices:
    (1) 29
    (2) 30
    (3) 28
    (4) None

    Answer: (1)
    Explanation: Tree with n vertices has n−1 edges.


    Q77.

    If a graph has an odd cycle, it is
    (1) Bipartite
    (2) Not bipartite
    (3) Complete
    (4) Tree

    Answer: (2)
    Explanation: Odd cycle → not bipartite.


    Q78.

    For a set of size n, number of proper subsets is
    (1) 2n
    (2) 2n1
    (3) n
    (4) n!

    Answer: (2)
    Explanation: Exclude the set itself.


    Q79.

    Which operation is associative?
    (1) Union
    (2) Intersection
    (3) Both
    (4) None

    Answer: (3)
    Explanation: Both ∪ and ∩ are associative.


    Q80.

    In a simple graph, maximum degree of any vertex is
    (1) n
    (2) n−1
    (3) n²
    (4) n/2

    Answer: (2)
    Explanation: A vertex can be connected to at most n−1 others.

  • UGC NET Computer Science Unit 1 – Optimization

    Go back to UGC NET COMUPTER SCIENCE

    UGC NET Computer Science Application – OPTIMIZATION 

    (Linear Programming · Simplex · Dual Simplex · Sensitivity · Integer Programming · Transportation · Assignment · PERT · CPM · Resource Levelling · Cost Considerations)


    1. LINEAR PROGRAMMING (LP)

    Definition: Linear Programming

    Linear programming is a mathematical technique for optimizing (maximizing or minimizing) a linear objective function, subject to a set of linear constraints.

    General structure:

    • Objective function

      Z=c1x1+c2x2++cnxn

    • Subject to constraints:

      ai1x1+ai2x2++ainxn,=,bi

    • with non-negativity constraints:

      xj0

    Explanation

    LP is used where resources (labor, materials, machines) are limited and must be allocated optimally.

    Simple Example

    Maximize

    Z=3x+2y

    Subject to:

    x+y4,x,y0


    2. MATHEMATICAL MODEL (FORMULATION)

    Definition

    Formulation is the process of expressing a real-world optimization problem as an LPP using:

    • Decision variables

    • Objective function

    • Constraints

    Explanation

    This converts a word problem into equations.

    Example

    A factory makes chairs (x) and tables (y).
    Profit = 50x + 80y
    Wood constraint → 3x + 5y ≤ 30
    Labor constraint → 2x + y ≤ 10


    3. GRAPHICAL SOLUTION METHOD

    Definition

    A method to solve LPPs with two variables by drawing constraints on a 2D graph and identifying the feasible region.

    Explanation

    1. Convert inequalities into lines.

    2. Shade feasible region.

    3. Compute objective value at each vertex.

    4. Best value gives the optimum.

    Example

    Line x + y = 4 divides the plane; feasible region lies below it.


    4. SIMPLEX METHOD

    Definition

    The Simplex Method is an iterative algebraic method to solve LPPs with two or more variables.

    Explanation

    • Converts inequalities to equalities by adding slack, surplus, artificial variables

    • Builds a tableau

    • Performs pivoting to improve the solution

    • Stops when no negative indicators remain in the objective row

    Key Terms

    • Basic variables: variables with non-zero values

    • Non-basic variables: set to zero

    • Pivot element: used for row operations


    5. DUAL SIMPLEX METHOD

    Definition

    A modification of simplex used when:

    • The objective row is feasible

    • The basic solution is infeasible (negative RHS)

    Explanation

    Dual Simplex works by making the RHS feasible through pivoting until all RHS ≥ 0.


    6. SENSITIVITY ANALYSIS

    Definition

    Sensitivity analysis studies how changes in:

    • Coefficients of objective function

    • RHS of constraints

    • Constraint coefficients
      affect the optimal solution.

    Key Terms

    • Shadow price: change in optimal Z when RHS of a constraint increases by 1 unit.

    • Reduced cost: amount by which objective coefficient must improve before variable enters basis.

    • Allowable range: how much parameters can vary without altering the optimal solution.


    7. INTEGER PROGRAMMING (IP)

    Definition

    An optimization model where some or all decision variables must be integers.

    Types

    1. Pure Integer Programming – all variables integer

    2. Mixed Integer Programming – some integer

    3. 0–1 (Binary) Programming – variables can be 0 or 1

    Explanation

    Used where fractional decisions are impossible (e.g., number of machines can’t be 2.5).

    Methods

    • Branch & Bound

    • Cutting Plane

    • Dynamic Programming


    8. TRANSPORTATION PROBLEM

    Definition

    A special LPP that deals with transporting commodities from multiple sources to multiple destinations at minimum cost.

    Explanation

    • Rows: sources

    • Columns: destinations

    • Each cell: cost of transporting one unit

    Balance condition:
    Total supply = Total demand

    Initial Solution Methods

    • North-West Corner

    • Least Cost Method

    • Vogel’s Approximation Method (VAM)

    Optimal Solution Methods

    • MODI (u – v method)

    • Stepping-Stone Method


    9. ASSIGNMENT PROBLEM

    Definition

    A special case of transportation where:

    • Supply = demand = 1

    • One person does one task only

    Goal: Minimize cost or maximize efficiency.

    Explanation

    Square cost matrix (NxN).
    Solved by Hungarian Method.

    Steps

    1. Row reduction

    2. Column reduction

    3. Covering zeros

    4. Optimal assignment


    10. PERT (PROGRAM EVALUATION REVIEW TECHNIQUE)

    Definition

    PERT is a project management tool using probabilistic time estimates to compute expected project duration.

    Explanation

    Each activity has three time estimates:

    • a = optimistic time

    • m = most likely time

    • b = pessimistic time

    Expected Time

    te=a+4m+b6

    Variance

    σ2=(ba6)2

    Used when time is uncertain.


    11. CPM (CRITICAL PATH METHOD)

    Definition

    A deterministic technique to determine the longest duration path (critical path) through a project network.

    Explanation

    • Activities have certain times

    • Critical Path = longest path

    • Activities on this path have zero float

    Float Types

    1. Total Float (TF)

    2. Free Float (FF)

    3. Independent Float (IF)

    Critical Path Formula

    Critical path = sequence of activities with

    Slack=0


    12. CRITICAL PATH CALCULATIONS

    Steps

    1. Forward Pass (Earliest Times)

    • Compute Earliest Start (ES)

    • Compute Earliest Finish (EF = ES + duration)

    2. Backward Pass (Latest Times)

    • Compute Latest Finish (LF)

    • Compute Latest Start (LS = LF – duration)

    3. Slack Calculation

    Slack=LSES

    Activities with Slack = 0 → critical.


    13. RESOURCE LEVELLING

    Definition

    Resource levelling adjusts activity start times to smooth out resource usage over time.

    Explanation

    Prevents:

    • sudden peaks in manpower

    • idle resources

    • overburdening specific days

    Used for optimizing:

    • labor

    • machines

    • budgets


    14. COST CONSIDERATION IN PROJECT SCHEDULING

    Definition

    Scheduling must consider both direct and indirect costs to obtain the lowest total project cost.

    Direct Costs

    Cost of performing activity itself.

    Indirect Costs

    Administrative, supervision, overhead.

    Crash Cost

    Additional cost for reducing activity duration.

    Explanation

    Project duration vs cost curve is U-shaped.
    Goal → Find minimum total cost.


    COMPLETE CHAPTER SUMMARY (Exam Sheet)

    Concept Definition Keyword
    LP

    Optimize linear objective under linear constraints

    linear + feasible
    Simplex Table-based root-finding pivot
    Dual Simplex Fix infeasible RHS

    RHS negative

    Sensitivity Post-optimal impact study

    shadow price

    Integer Programming

    Variables restricted to integers branch & bound
    Transportation

    Min cost supply–demand problem

    VAM, MODI
    Assignment One-to-one allocation Hungarian
    PERT Probabilistic time

    expected time

    CPM Deterministic time

    critical path

    Resource Levelling Smooth resource usage no peaks
    Cost Scheduling Total = Direct + Indirect crashing
  • UGC NETComputer Science Unit 1 – Counting, Mathematical Induction and Discrete Probability

    Go back to UGC NET COMUPTER SCIENCE

    UNIT 1 — Counting, Mathematical Induction and Discrete Probability

     (UGC NET)

    (Counting • Pigeonhole Principle • Permutations • Combinations • Inclusion–Exclusion • Mathematical Induction • Probability • Bayes’ Theorem)


    1. BASICS OF COUNTING — DEFINITIONS


    Definition 1: Counting Principle (Product Rule)

    If an action consists of k independent stages, and the stages can be performed in

    n1,n2,,nk ways respectively,

    then the total number of ways of performing the action is

    n1×n2××nk.


    Definition 2: Sum Rule (Addition Principle)

    If an action can be performed in m ways or n ways, and the two sets of possibilities are mutually exclusive,

    Total ways=m+n.


    Definition 3: Factorial (n!)

    For any positive integer n:

    n!=n(n1)(n2)1

    and by definition

    0!=1.



    2. PERMUTATIONS — DEFINITIONS


    Definition 4: Permutation

    A permutation is an arrangement of objects in a specific order.


    Definition 5: r-Permutation of n Objects

    The number of ways to arrange r objects out of n:

    nPr=n!(nr)!


    Definition 6: Permutation with Repetition

    If among n objects,

    • n1 are alike,

    • n2 are alike, etc.,

    then number of distinct permutations:

    n!n1!n2!



    3. COMBINATIONS — DEFINITIONS


    Definition 7: Combination

    A combination is a selection of r objects from n objects where order does not matter.


    Definition 8: r-Combination of n Objects

    nCr=n!r!(nr)!


    Key Relation

    nPr=nCrr!



    4. PIGEONHOLE PRINCIPLE — DEFINITIONS & STATEMENTS


    Definition 9: Pigeonhole Principle (Basic)

    If more than n objects are placed into n boxes, then at least one box contains more than one object.

    Formal Statement:
    If n+1 objects are placed into n boxes, at least one box contains ≥ 2 objects.


    Definition 10: Generalized Pigeonhole Principle

    If N objects are placed into k boxes, then at least one box contains

    Nk

    objects.



    5. INCLUSION–EXCLUSION PRINCIPLE — STATEMENTS


    Definition 11: Inclusion–Exclusion Principle (Two Sets)

    For any two sets A and B,

    AB=A+BAB.


    Definition 12: Inclusion–Exclusion (Three Sets)

    ABC=A+B+CABACBC+ABC.



    6. MATHEMATICAL INDUCTION — DEFINITIONS & STEPS


    Definition 13: Principle of Mathematical Induction

    Let P(n) be a statement depending on a natural number n.
    If:

    1. Base Step: P(1) is true.

    2. Induction Step: P(k)P(k+1) for all natural numbers k,

    Then P(n) is true for all natural numbers.


    Definition 14: Strong Induction

    A statement P(n) is true for all natural numbers n if:

    1. P(1) is true.

    2. Assuming all of P(1),P(2),,P(k) are true implies P(k+1).



    7. PROBABILITY — DEFINITIONS


    Definition 15: Sample Space (S)

    The set of all possible outcomes of an experiment.


    Definition 16: Event (E)

    Any subset of the sample space.


    Definition 17: Probability of an Event

    If all outcomes are equally likely:

    P(E)=ES


    Definition 18: Complement of an Event

    P(E)=1P(E)


    Definition 19: Conditional Probability

    P(AB)=P(AB)P(B),P(B)>0


    Definition 20: Independent Events

    Two events A and B are independent if:

    P(AB)=P(A)P(B)



    8. BAYES’ THEOREM — STATEMENT


    Definition 21: Bayes’ Theorem

    If A1,A2,,An form a partition of the sample space and B is any event with P(B)>0, then:

    P(AiB)=P(BAi)P(Ai)j=1nP(BAj)P(Aj)

    This theorem is used to reverse conditional probability.