Papers by Sabrish Gopalakrishnan
Proceedings of the Genetic and Evolutionary Computation Conference Companion
Many combinatorial optimization problems can be formulated as a problem to determine the order of... more Many combinatorial optimization problems can be formulated as a problem to determine the order of sequence or to find a corresponding mapping of the objects. We call such problems permutationbased optimization problems. Many such problems can be formulated as a quadratic unconstrained binary optimization (QUBO) or Ising model by introducing a penalty coefficient to the permutation constraint terms. While classical and quantum annealing approaches have been proposed to solve QUBOs to date, they face issues with optimality and feasibility. Here we treat a given QUBO solver as a black box and propose techniques to enhance its performance. First, to ensure an effective search for good quality solutions, a smooth energy landscape is needed; we propose a data scaling approach that reduces the amplitudes of the input without compromising optimality. Second, we need to tune the penalty coefficient. In this paper, we illustrate that for certain problems, it suffices to tune the parameter by performing random sampling on the penalty coefficients to achieve good performance. Finally, to handle possible infeasibility of the solution, we introduce a polynomial-time projection algorithm. We apply these techniques along with a divideand-conquer strategy to solve some large-scale permutation-based problems and present results for TSP and QAP.
The Journal of Markets and Morality, 2020
In recent years, a cottage industry has grown up around identifying, describing, and offering sol... more In recent years, a cottage industry has grown up around identifying, describing, and offering solutions to the problem of polarization in American social and political life. Whether and how much the problem is unique to or especially pronounced in twenty-first century America vis-a-vis any other time and place remains debatable, but that there is much “talking past each other” in contemporary public discourse is so obvious as to be indisputable. Kevin Schmiesing, "Editorial: Communicating Ideas," Journal of Markets & Morality 23, no. 1 (2020): 1-3.
In this paper, we propose a hybrid framework to solve large-scale permutation-based combinatorial... more In this paper, we propose a hybrid framework to solve large-scale permutation-based combinatorial problems effectively using a high-performance quadratic unconstrained binary optimization (QUBO) solver. To do so, transformations are required to change a constrained optimization model to an unconstrained model that involves parameter tuning. We propose techniques to overcome the challenges in using a QUBO solver that typically comes with limited numbers of bits. First, to smooth the energy landscape, we reduce the magnitudes of the input without compromising optimality. We propose a machine learning approach to tune the parameters for good performance effectively. To handle possible infeasibility, we introduce a polynomial-time projection algorithm. Finally, to solve large-scale problems, we introduce a divide-and-conquer approach that calls the QUBO solver repeatedly on small sub-problems. We tested our approach on provably hard Euclidean Traveling Salesman (E-TSP) instances and Flo...
arXiv: Optimization and Control, 2020
In this paper, we propose a hybrid framework to solve large-scale permutation-based combinatorial... more In this paper, we propose a hybrid framework to solve large-scale permutation-based combinatorial problems effectively using a high-performance quadratic unconstrained binary optimization (QUBO) solver. To do so, transformations are required to change a constrained optimization model to an unconstrained model that involves parameter tuning. We propose techniques to overcome the challenges in using a QUBO solver that typically comes with limited numbers of bits. First, to smooth the energy landscape, we reduce the magnitudes of the input without compromising optimality. We propose a machine learning approach to tune the parameters for good performance effectively. To handle possible infeasibility, we introduce a polynomial-time projection algorithm. Finally, to solve large-scale problems, we introduce a divide-and-conquer approach that calls the QUBO solver repeatedly on small sub-problems. We tested our approach on provably hard Euclidean Traveling Salesman (E-TSP) instances and Flo...
QROSS: QUBO Relaxation Parameter optimisation via Learning Solver Surrogates
2021 IEEE 41st International Conference on Distributed Computing Systems Workshops (ICDCSW)
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Papers by Sabrish Gopalakrishnan