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UGC NET Computer Science Discrete Structures and Optimization Syllabus

The Discrete Structures and Optimization syllabus for UGC NET Computer Science in full: 7 chapters and 54 topics, every one listed below, plus 55 Discrete Structures and Optimization flashcards with answers.

7Chapters
54Topics
55Flashcards

Discrete Structures and Optimization syllabus: all 7 chapters

Expand any chapter to see its topics and sub-topics.

  1. Mathematical Logic

    6 topics
    • Propositional and Predicate Logic
    • Propositional Equivalences
    • Normal Forms
    • Predicates and Quantifiers
    • Nested Quantifiers
    • Rules of Inference
  2. Sets and Relations

    4 topics
    • Set Operations
    • Representation and Properties of Relations
    • Equivalence Relations
    • Partially Ordering
  3. Counting, Mathematical Induction and Discrete Probability

    7 topics
    • Basics of Counting
    • Pigeonhole Principle
    • Permutations and Combinations
    • Inclusion-Exclusion Principle
    • Mathematical Induction
    • Probability
    • Bayes’ Theorem
  4. Group Theory

    11 topics
    • Groups
    • Subgroups
    • Semi Groups
    • Product and Quotients of Algebraic Structures
    • Isomorphism
    • Homomorphism
    • Automorphism
    • Rings
    • Integral Domains
    • Fields
    • Applications of Group Theory
  5. Graph Theory

    14 topics
    • Simple Graph
    • Multigraph
    • Weighted Graph
    • Paths and Circuits
    • Shortest Paths in Weighted Graphs
    • Eulerian Paths and Circuits
    • Hamiltonian Paths and Circuits
    • Planner graph
    • Graph Coloring
    • Bipartite Graphs
    • Trees and Rooted Trees
    • Prefix Codes
    • Tree Traversals
    • Spanning Trees and Cut-Sets
  6. Boolean Algebra

    2 topics
    • Boolean Functions and its Representation
    • Simplifications of Boolean Functions
  7. Optimization

    10 topics
    • Linear Programming - Mathematical Model
    • Graphical Solution
    • Simplex and Dual Simplex Method
    • Sensitive Analysis
    • Integer Programming
    • Transportation and Assignment Models
    • PERT-CPM: Diagram Representation
    • Critical Path Calculations
    • Resource Levelling
    • Cost Consideration in Project Scheduling

UGC NET Computer Science Discrete Structures and Optimization flashcards — 55 questions with answers

  1. In propositional logic, when is a conditional statement p → q false?

    Only when p is true and q is false. In all other cases (T→T, F→T, F→F) it is true.

  2. State the logical equivalence for the contrapositive, and why it matters.

    p → q ≡ ¬q → ¬p. A conditional and its contrapositive are logically equivalent, so proving one proves the other.

  3. What are the two De Morgan's laws for propositions?

    ¬(p ∧ q) ≡ ¬p ∨ ¬q and ¬(p ∨ q) ≡ ¬p ∧ ¬q.

  4. Define tautology, contradiction, and contingency.

    A tautology is always true, a contradiction is always false, and a contingency is sometimes true and sometimes false depending on truth values.

  5. What is the difference between Disjunctive Normal Form (DNF) and Conjunctive Normal Form (CNF)?

    DNF is a disjunction (OR) of conjunctive clauses (sum of products); CNF is a conjunction (AND) of disjunctive clauses (product of sums).

  6. What is the negation of the quantified statement ∀x P(x)?

    ¬∀x P(x) ≡ ∃x ¬P(x). The negation of a universal is an existential of the negation (De Morgan for quantifiers).

  7. In nested quantifiers, does ∀x ∃y P(x,y) mean the same as ∃y ∀x P(x,y)?

    No. ∀x ∃y allows y to depend on x; ∃y ∀x requires a single y that works for all x. ∃y∀x ⇒ ∀x∃y, but not the reverse.

  8. State the rules of inference Modus Ponens and Modus Tollens.

    Modus Ponens: from p → q and p, infer q. Modus Tollens: from p → q and ¬q, infer ¬p.

  9. What does the rule of Hypothetical Syllogism state?

    From p → q and q → r, infer p → r (transitivity of implication).

  10. State the inclusion-exclusion formula for the union of two sets.

    |A ∪ B| = |A| + |B| − |A ∩ B|.

  11. What is the formula for the number of subsets (power set size) of a set with n elements?

    2^n.

  12. Define the symmetric difference of sets A and B.

    A △ B = (A − B) ∪ (B − A) = (A ∪ B) − (A ∩ B): elements in exactly one of the two sets.

  13. What are the three properties that define an equivalence relation?

    Reflexive, Symmetric, and Transitive.

  14. What does an equivalence relation on a set produce?

    A partition of the set into disjoint equivalence classes whose union is the whole set.

  15. What three properties define a partial order relation?

    Reflexive, Antisymmetric, and Transitive.

  16. In a poset, what is the difference between a maximal element and a greatest (maximum) element?

    A maximal element has nothing strictly above it; a greatest element is ≥ every element. A greatest element is unique and maximal, but a maximal element need not be greatest.

  17. How many ways can a relation on a set of n elements be? (count of relations)

    2^(n²), since a relation is any subset of the n×n set of ordered pairs.

  18. State the product rule and sum rule of counting.

    Product rule: if a task has k steps with n₁,…,n_k choices, total = n₁·n₂·…·n_k. Sum rule: for mutually exclusive alternatives, total = n₁ + n₂ + … + n_k.

  19. State the Pigeonhole Principle (generalized form).

    If N objects are placed into k boxes, then at least one box contains ⌈N/k⌉ objects.

  20. Give the formulas for permutations P(n,r) and combinations C(n,r).

    P(n,r) = n!/(n−r)!; C(n,r) = n!/[r!(n−r)!].

Show the other 35 Discrete Structures and Optimization flashcards
  1. State the Binomial Theorem.

    (x + y)^n = Σ_{k=0}^{n} C(n,k) x^{n−k} y^k.

  2. State the inclusion-exclusion principle for three sets.

    |A∪B∪C| = |A|+|B|+|C| − |A∩B| − |A∩C| − |B∩C| + |A∩B∩C|.

  3. What are the two steps of proof by Mathematical Induction?

    Basis step: prove P(1) (or base case). Inductive step: assume P(k) and prove P(k+1). Then P(n) holds for all n ≥ base.

  4. State Bayes' Theorem.

    P(A|B) = P(B|A)·P(A) / P(B), where P(B) = Σ P(B|A_i)P(A_i) over a partition {A_i}.

  5. For independent events A and B, what are P(A∩B) and the addition rule P(A∪B)?

    P(A∩B) = P(A)·P(B); P(A∪B) = P(A) + P(B) − P(A)P(B).

  6. What four axioms must a set with a binary operation satisfy to be a group?

    Closure, Associativity, existence of an Identity element, and existence of an Inverse for every element.

  7. Distinguish a semigroup, a monoid, and a group.

    Semigroup: closure + associativity. Monoid: semigroup with an identity element. Group: monoid in which every element has an inverse.

  8. State Lagrange's Theorem for finite groups.

    The order of any subgroup H of a finite group G divides the order of G; i.e., |H| divides |G|.

  9. Define a group homomorphism and an isomorphism.

    A homomorphism f: G→H satisfies f(ab)=f(a)f(b). An isomorphism is a bijective homomorphism, making the two groups structurally identical.

  10. What is an automorphism of a group?

    An isomorphism from a group onto itself (a structure-preserving bijection G → G).

  11. Define a ring (algebraic structure).

    A set with two operations (+ and ·) where (R,+) is an abelian group, · is associative, and · distributes over +.

  12. What distinguishes an integral domain and a field from a general commutative ring?

    An integral domain is a commutative ring with unity and no zero divisors. A field additionally requires every nonzero element to have a multiplicative inverse.

  13. What is the difference between a simple graph, a multigraph, and a weighted graph?

    Simple graph: no loops or parallel edges. Multigraph: allows multiple (parallel) edges between vertices. Weighted graph: each edge carries a numeric weight.

  14. State the Handshaking Theorem for graphs.

    The sum of all vertex degrees equals twice the number of edges: Σ deg(v) = 2|E|. Hence the number of odd-degree vertices is even.

  15. What is the condition for a connected graph to have an Eulerian circuit versus an Eulerian path?

    Eulerian circuit exists iff every vertex has even degree. Eulerian path (not circuit) exists iff exactly two vertices have odd degree.

  16. How does a Hamiltonian path/circuit differ from an Eulerian one?

    A Hamiltonian path/circuit visits every vertex exactly once; an Eulerian path/circuit traverses every edge exactly once.

  17. Which algorithm finds shortest paths from a single source in a weighted graph with non-negative weights, and its complexity?

    Dijkstra's algorithm; O((V+E) log V) with a binary heap (O(V²) with an array).

  18. State Euler's formula for connected planar graphs.

    v − e + f = 2, where v = vertices, e = edges, f = faces (regions, including the outer one).

  19. State the Four Color Theorem and define a graph's chromatic number.

    Every planar graph is 4-colorable. The chromatic number χ(G) is the minimum number of colors needed to color vertices so no two adjacent vertices share a color.

  20. What is the chromatic number of a bipartite graph, and how is bipartiteness characterized?

    A bipartite graph has chromatic number 2 (or 1 if no edges). A graph is bipartite iff it contains no odd-length cycle.

  21. For a tree with n vertices, how many edges does it have, and why?

    Exactly n − 1 edges. A tree is connected and acyclic, so adding any edge creates a cycle and removing any disconnects it.

  22. What property must a set of codewords satisfy to be a prefix code, and why is it useful?

    No codeword is a prefix of another. This allows unambiguous (instantaneous) decoding without separators; Huffman coding builds optimal prefix codes.

  23. List the three depth-first tree traversal orders and their order of visiting root.

    Preorder: root, left, right. Inorder: left, root, right. Postorder: left, right, root.

  24. What is a spanning tree, and which algorithms find a minimum spanning tree?

    A spanning tree is a subgraph that is a tree connecting all vertices. Minimum spanning trees are found by Kruskal's and Prim's algorithms.

  25. How are Boolean functions canonically represented in SOP and POS forms?

    SOP (Sum of Products) = OR of minterms (where output is 1). POS (Product of Sums) = AND of maxterms (where output is 0).

  26. What tool is commonly used to simplify Boolean functions, and what is its goal?

    The Karnaugh map (K-map); it groups adjacent 1-cells into the largest power-of-two blocks to minimize the number of literals/terms (also Quine–McCluskey method).

  27. What are the components of a Linear Programming mathematical model?

    An objective function (to maximize/minimize), decision variables, and a set of linear constraints plus non-negativity restrictions.

  28. In the graphical solution of an LP, where does the optimal solution occur?

    At a corner (vertex/extreme point) of the feasible region; the optimum of a linear objective over a convex polygon lies at a vertex.

  29. What is the role of the Simplex method and what does the Dual Simplex method handle?

    Simplex iterates from vertex to vertex of the feasible region improving the objective until optimal. Dual Simplex maintains optimality while restoring feasibility, useful when the basis is optimal but infeasible (e.g., added constraints).

  30. What does sensitivity analysis in LP study?

    How changes in objective coefficients or right-hand-side (resource) values affect the optimal solution, including ranges over which the current basis stays optimal and shadow prices.

  31. How does Integer Programming differ from Linear Programming?

    Integer Programming requires some or all decision variables to take integer values, making it NP-hard; LP allows continuous values and is solvable in polynomial time.

  32. In a balanced transportation problem, what is the relationship between supply and demand and the number of basic cells?

    Total supply = total demand; a non-degenerate basic feasible solution has exactly m + n − 1 occupied (basic) cells for m sources and n destinations.

  33. Which method solves the Assignment Model optimally, and what type of problem is it?

    The Hungarian method; it solves the assignment problem (a special transportation/matching problem) of pairing n agents to n tasks at minimum total cost.

  34. In PERT/CPM, what is the critical path?

    The longest-duration path through the project network; it determines the minimum project completion time and has zero slack (float).

  35. How is activity slack (float) calculated in CPM, and what does the PERT expected time formula give?

    Slack = LS − ES = LF − EF. PERT expected time t_e = (optimistic + 4·most likely + pessimistic)/6.

UGC NET Computer Science Discrete Structures and Optimization FAQ

What is in the UGC NET Computer Science Discrete Structures and Optimization syllabus?

UGC NET Computer Science Discrete Structures and Optimization is split into 7 chapters: Mathematical Logic, Sets and Relations, Counting, Mathematical Induction and Discrete Probability, Group Theory, Graph Theory, Boolean Algebra and Optimization. Together they contain 54 topics.

How many chapters are in UGC NET Computer Science Discrete Structures and Optimization?

7 chapters, containing 54 topics. The largest chapter is Graph Theory, with 14 topics.

Are there flashcards for UGC NET Computer Science Discrete Structures and Optimization?

Yes — a 55-card Discrete Structures and Optimization deck, printed in full in the flashcards section of this page and available in the Examius app with spaced repetition. The free plan covers one exam of your choice, including its flashcards; Examius Pro, a one-time purchase, unlocks every other exam and deck.