A First Course on Operations Research and Information Theory by Sanat Kumar Mazumder, published by New Central Book

Name: A First Course on Operations Research and Information Theory by Sanat Kumar Mazumder, published by New Central Book - Booksbp.com
SKU: 9788173818844
Price: 260.00 INR
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SKU: 9788173818844

📋 Full Chapter & Topic Details:-

1. Mathematical Foundation & Background

Essential mathematical prerequisites required to master operations research concepts:

Linear Algebra: Matrices, determinants, and vector spaces.

Probability & Stochastic Processes: Review of probability distributions, stochastic processes, and the z-transform.

2. Linear Programming Models (LPP)

The core computational methodology of optimization:

Formulation: Standard form of an LP model, resource allocation, and structural modeling.

Variables: Managing slack, surplus, and unrestricted variables.

Analytical Techniques: The Simplex Method, Alternative Optima, and the Simplex Algorithm.

Advanced LP Solvers: Charnes’ Big-M (Penalty) Method and the Two-Phase Simplex Method.

3. Duality and Dual-Simplex Methods

Fundamental duality theorems mapping primal problems to dual problems.

The Primal-Dual Method and the Dual-Simplex Algorithm.

4. Post-Optimality & Sensitivity Analysis

Analyzing modifications in the optimization environment.

Changes in the Cost Vector and Requirement Vector.

Structural changes within the Linear Programming Model.

5. Integer Linear Programming (ILP)

Cutting Plane constraints.

Mixed Integer Programming Problems (IPP).

Branch and Bound evaluation techniques.

6. Transportation and Assignment Problems

Initial Basic Feasible Solution methods: Northwest Corner Method (NWCM), Least Cost Method (LCM), and Vogel’s Approximation Method (VAM).

Optimality testing utilizing the MODI Method and Hungarian Method.

7. Information Theory & Statistics

Measures of information, source coding, entropy, and channel capacity.

The graphical layout featured on the cover specifically illustrates data frequency distributions, depicting Normal Distribution, Left-Skewed Distribution, and Right-Skewed Distribution templates used in statistical analysis.