7 citations · 13 across the 11 of their papers we have counts for
14 papers
Rank-sensitive vertex bounds for semidefinite lifts
Avinash Bhardwaj
We study how the ranks of factors in a positive semidefinite slack factorization constrain its size. We bound the number of low-rank vertex factors in any fixed factorization, and…
Optimal Cuts and Optimal Faces of Exact Max-Cut Semidefinite Relaxations
Avinash Bhardwaj, Chen Chen, Vishnu Narayanan
An exact Max-Cut semidefinite relaxation can have optima outside the convex hull of optimal cut matrices. We study when this hull equals the optimal face and how to recognize equal…
The Complexity of Recognizing SDP Exactness for the Maximum Cut Problem
Avinash Bhardwaj
The standard semidefinite programming (SDP) relaxation of Max-Cut is exact when its optimum equals the maximum cut value. Delorme and Poljak resolved NP-completeness of recognizing…
Max--Cut via Node Features
Avinash Bhardwaj, Hritiz Gogoi, Vishnu Narayanan
We study the Max--Cut problem from a node-feature perspective, where each vertex is associated with a feature vector and edge weights are given by pairwise inner products. We fi…
On exactness of SDP relaxation for the maximum cut problem
Avinash Bhardwaj, Hritiz Gogoi, Vishnu Narayanan +1
Semidefinite programming (SDP) provides a powerful relaxation for the maximum cut problem. In this work, we characterize a few classes of graphs for which the SDP relaxation is exa…
On generators of -PSD closures of the positive semidefinite cone
Avinash Bhardwaj, Vishnu Narayanan, Abhishek Pathapati
Positive semidefinite (PSD) cone is the cone of positive semidefinite matrices, and is the object of interest in semidefinite programming (SDP). A computational efficient approxima…
Online Universal Dirichlet Factor Portfolios
Purushottam Parthasarathy, Avinash Bhardwaj, Manjesh K. Hanawal
We revisit the online portfolio allocation problem and propose universal portfolios that use factor weighing to produce portfolios that out-perform uniform dirichlet allocation sch…
A few more Lonely Runners
Avinash Bhardwaj, Vishnu Narayanan, Hrishikesh Venkataraman
Lonely Runner Conjecture, proposed by Jörg M. Wills and so nomenclatured by Luis Goddyn, has been an object of interest since it was first conceived in 1967 : Given positive intege…
Almost Exact Risk Budgeting with Return Forecasts for Portfolio Allocation
Avinash Bhardwaj, Manjesh K Hanawal, Purushottam Parthasarathy
In this paper, we revisit the portfolio allocation problem with designated risk-budget [Qian, 2005]. We generalize the problem of arbitrary risk budgets with unequal correlations t…
Unsupervised Early Exit in DNNs with Multiple Exits
Hari Narayan N U, Manjesh K. Hanawal, Avinash Bhardwaj
Deep Neural Networks (DNNs) are generally designed as sequentially cascaded differentiable blocks/layers with a prediction module connected only to its last layer. DNNs can be atta…
Exact augmented Lagrangian duality for mixed integer convex optimization
Avinash Bhardwaj, Vishnu Narayanan, Abhishek Pathapati
Augmented Lagrangian dual augments the classical Lagrangian dual with a non-negative non-linear penalty function of the violation of the relaxed/dualized constraints in order to re…
On the quality of the PSD closure approximation
Avinash Bhardwaj, Harshit Kothari, Vishnu Narayanan
Postive semidefinite (PSD) cone is the cone of positive semidefinite matrices, and is the object of interest in semidefinite programming (SDP). A computational efficient approximat…
Network Design with Probabilistic Capacities
Alper Atamturk, Avinash Bhardwaj
We consider a network design problem with random arc capacities and give a formulation with a probabilistic capacity constraint on each cut of the network. To handle the exponentia…
Deciding polyhedrality of spectrahedra
Avinash Bhardwaj, Philipp Rostalski, Raman Sanyal
Spectrahedra are linear sections of the cone of positive semidefinite matrices that, as convex bodies, generalize the class of polyhedra. In this paper we investigate the problem o…