Basic linear programming, convex, concave optimization, MILP problem, Genetic Algorithm and simple coding questions. Difficult was Easy for both theoretical and pr Basic Linear Programming: Be familiar with the formulation of linear programming problems, understanding of objective functions, constraints, feasible regions, and basic solution methods like the Simplex algorithm. Convex and Concave Optimization: Understand the properties of convex and concave functions, and how they influence optimization problems. Be ready to discuss applications and methods for solving these types of problems. Mixed-Integer Linear Programming (MILP): Know how MILP extends linear programming by allowing integer variables. Understand common solution techniques and applications in various fields. Genetic Algorithm: Be prepared to explain the concept of genetic algorithms, how they mimic natural selection processes, and their applications in optimization problems. You might be asked to discuss crossover, mutation, selection, and termination criteria. Coding Questions: Expect to solve simple to moderate coding problems, possibly related to the above topics. Be comfortable with writing efficient, clean, and well-documented code.

The process was in 3 stages. There were two rounds of interviews and there was a coding round. The company came for our campus placements. The questions were regarding heuristic problems and linear, non-linear, mixed integer and quadratic programming problems.