1 citations · 2 across the 14 of their papers we have counts for
15 papers
Disjunctive Submodular Functions: Envelopes and Applications to Inventory and 0-1 Quadratic Optimization
Zhongqi Wu, Taotao He, Mohit Tawarmalani
This paper considers convex envelopes of disjunctive submodular functions---functions that are lattice family submodular over faces of a hypercube---and constructs the first strong…
Axis-Aligned Relaxations for Mixed-Integer Nonlinear Programming
Haisheng Zhu, Taotao He, Mohit Tawarmalani
We present a novel relaxation framework for general mixed-integer nonlinear programming (MINLP) grounded in computational geometry. Our approach constructs polyhedral relaxations b…
Minimizing risk measures with applications in network traffic engineering
Ashish Chandra, Mohit Tawarmalani
This paper presents a novel two-stage optimization framework designed to model integrated quantile functions, which leads to the formulation of a bilinear optimization problem (P).…
Minimum reflux calculation for multicomponent distillation in multi-feed, multi-product columns: Algorithms and examples
Zheyu Jiang, Mohit Tawarmalani, Rakesh Agrawal
In this work, we present the first algorithm for identifying the minimum reboiler vapor duty requirement for a general multi-feed, multi-product (MFMP) distillation column separati…
Discreteness to Convexity: Promotion Planning via Simplotope Triangulation
Taotao He, Mohit Tawarmalani
Price promotion optimization is a computationally challenging problem central to supermarket operations, requiring simultaneous pricing decisions across multiple products and perio…
Active Learning for Fair and Stable Online Allocations
Riddhiman Bhattacharya, Thanh Nguyen, Will Wei Sun +1
We explore an active learning approach for dynamic fair resource allocation problems. Unlike previous work that assumes full feedback from all agents on their allocations, we consi…
New finite relaxation hierarchies for concavo-convex, disjoint bilinear programs, and facial disjunctions
Mohit Tawarmalani
This paper introduces novel relaxation hierarchies for concavo-convex programs (CXP), a class of problems that includes disjoint bilinear programming (DBP) and concave minimization…
Convexification Techniques for Fractional Programs
Taotao He, Siyue Liu, Mohit Tawarmalani
This paper develops a correspondence relating convex hulls of fractional functions with those of polynomial functions over the same domain. Using this result, we develop a number o…
MIP Relaxations in Factorable Programming
Taotao He, Mohit Tawarmalani
In this paper, we develop new discrete relaxations for nonlinear expressions in factorable programming. We utilize specialized convexification results as well as composite relaxati…
Extracting structure from functional expressions for continuous and discrete relaxations of MINLP
Taotao He, Mohit Tawarmalani
In this paper, we develop new continuous and discrete relaxations for nonlinear expressions in an MINLP. In contrast to factorable programming, our techniques utilize the inner-fun…
FloMore: Meeting bandwidth requirements of flows
Chuan jiang, Sanjay Rao, Mohit Tawarmalani
Wide-area cloud provider networks must support the bandwidth requirements of diverse services (e.g., applications, product groups, customers) despite failures. Existing traffic eng…
Optimal Design of Membrane Cascades for Gaseous and Liquid Mixtures via MINLP
Jose Adrian Chavez Velasco, Radhakrishna Tumbalam Gooty, Mohit Tawarmalani +1
Given the growing concern of reducing CO2 emissions, it is desirable to identify, for a given separation carried out through a membrane cascade, the optimum design that yields the…
Advances in MINLP to Identify Energy-efficient Distillation Configurations
Radhakrishna Tumbalam Gooty, Rakesh Agrawal, Mohit Tawarmalani
In this paper, we describe the first mixed-integer nonlinear programming (MINLP) based solution approach that successfully identifies the most energy-efficient distillation configu…
Convexification of Permutation-Invariant Sets and an Application to Sparse PCA
Jinhak Kim, Mohit Tawarmalani, Jean-Philippe P. Richard
We develop techniques to convexify a set that is invariant under permutation and/or change of sign of variables and discuss applications of these results. First, we convexify the i…
Information Theoretic Limits for Linear Prediction with Graph-Structured Sparsity
Adarsh Barik, Jean Honorio, Mohit Tawarmalani
We analyze the necessary number of samples for sparse vector recovery in a noisy linear prediction setup. This model includes problems such as linear regression and classification.…