activity
20202026
most citedRobust Conic Satisficing

2 citations · 3 across the 8 of their papers we have counts for

collaborators

8 papers

math.PR2026

Dimension Dependent Correlation Gap Bounds under Restricted Independence

Arjun Ramachandra

The pairwise independent correlation gap is the ratio of the maximum expected value of a set function under arbitrary dependence to that under pairwise independence, measuring the…

math.OC2026

Counterexample to a conjecture on the pairwise independent correlation gap using AI

Arjun Ramachandra, Karthik Natarajan

Aided by the AI tool GPT5.5 Pro, we provide a counterexample to a conjecture made by Ramachandra and Natarajan (2025) [Pairwise independent correlation gap, Operations Research Let…

math.OC2023

Distributionally robust optimization through the lens of submodularity

Karthik Natarajan, Divya Padmanabhan, Arjun Ramachandra

Distributionally robust optimization is used to tackle decision making problems under uncertainty where the distribution of the uncertain data is ambiguous. Many ambiguity sets hav…

math.PR2022

Probability bounds for random events under -wise independence

Karthik Natarajan, Arjun Kodagehalli Ramachandra, Colin Tan

A collection of random events is said to be -wise independent if any events among them are mutually independent. We characterise all probability measures with…

math.OC2022

Pairwise independent correlation gap

Arjun Ramachandra, Karthik Natarajan

In this paper, we introduce the notion of a ``pairwise independent correlation gap'' for set functions with random elements. The pairwise independent correlation gap is defined as…

math.OC2021★ 1 cited

Extremal Probability Bounds in Combinatorial Optimization

Divya Padmanabhan, Selin Damla Ahipasaoglu, Arjun Ramachandra +1

In this paper, we compute the tightest possible bounds on the probability that the optimal value of a combinatorial optimization problem in maximization form with a random objectiv…

math.OC2021★ 2 cited

Robust Conic Satisficing

Arjun Ramachandra, Napat Rujeerapaiboon, Melvyn Sim

In practical optimization problems, we typically model uncertainty as a random variable though its true probability distribution is unobservable to the decision maker. Historical d…

math.OC2020

Tight Probability Bounds with Pairwise Independence

Arjun Ramachandra, Karthik Natarajan

While useful probability bounds for pairwise independent Bernoulli random variables adding up to at least an integer have been proposed in the literature, none of these bou…