arXiv subjects

Ramachandran Vaidyanathan

Publications and source records attributed to Ramachandran Vaidyanathan.

4 recordsLinked to original sources

arXiv2026

Learning Interior Point Method Central Path Projection for Optimal Power Flow

This paper proposes a learning-based approach to accelerate the interior-point method (IPM) for solving optimal power flow (OPF) problems by learning the structure of the IPM central path from its early stable iterations. Unlike traditional learning models that attempt to predict the OPF solution directly, our approach learns the structure of the IPM trajectory itself, since even accurate predictions may not reliably reduce IPM iterations. The IPM follows a central path that iteratively progresses toward the optimal solution. While this trajectory encodes critical information about the optimization landscape, the later iterations become increasingly expensive due to ill-conditioned linear systems. Our analysis of the IPM central path reveals that its initial segments contain the most informative features for guiding the trajectory toward optimality. Leveraging this insight, we model the central path as a time series and use a Long Short-Term Memory (LSTM) network to project the path using only the first few stable iterations. To ensure that the learned trajectory remains within the feasible region--especially near the optimal point--we introduce a grid-informed mechanism into the LSTM that enforces key operational constraints on generation, voltage magnitudes, and line flows. This framework, referred to as Learning-IPM (L-IPM), significantly reduces both the number of IPM iterations and overall solution time. To improve generalization, we use a sampling-based strategy to generate a diverse set of load conditions that effectively span the operational space. Simulation results across a range of test systems--including a 2869-bus European transmission network--demonstrate that L-IPM achieves up to a 94% reduction in solution time and an 85.5% reduction in iterations, without compromising feasibility or accuracy.

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arXiv2020

Efficient, Effective and Well Justified Estimation of Active Nodes within a Cluster

Reliable and efficient estimation of the size of a dynamically changing cluster in an IoT network is critical in its nominal operation. Most previous estimation schemes worked with relatively smaller frame size and large number of rounds. Here we propose a new estimator named \textquotedblleft Gaussian Estimator of Active Nodes,\textquotedblright (GEAN), that works with large enough frame size under which testing statistics is well approximated as a Gaussian variable, thereby requiring less number of frames, and thus less total number of channel slots to attain a desired accuracy in estimation. More specifically, the selection of the frame size is done according to Triangular Array Central Limit Theorem which also enables us to quantify the approximation error. Larger frame size helps the statistical average to converge faster to the ensemble mean of the estimator and the quantification of the approximation error helps to determine the number of rounds to keep up with the accuracy requirements. We present the analysis of our scheme under two different channel models i.e. $ \{0,1 \} $ and $ \{0,1,e \} $, whereas all previous schemes worked only under $ \{0,1 \} $ channel model. The overall performance of GEAN is better than the previously proposed schemes considering the number of slots required for estimation to achieve a given level of estimation accuracy.

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arXiv2015

Asymptotic Error Free Partitioning over Noisy Boolean Multiaccess Channels

In this paper, we consider the problem of partitioning active users in a manner that facilitates multi-access without collision. The setting is of a noisy, synchronous, Boolean, multi-access channel where $K$ active users (out of a total of $N$ users) seek to access. A solution to the partition problem places each of the $N$ users in one of $K$ groups (or blocks) such that no two active nodes are in the same block. We consider a simple, but non-trivial and illustrative case of $K=2$ active users and study the number of steps $T$ used to solve the partition problem. By random coding and a suboptimal decoding scheme, we show that for any $T\geq (C_1 +ξ_1)\log N$, where $C_1$ and $ξ_1$ are positive constants (independent of $N$), and $ξ_1$ can be arbitrary small, the partition problem can be solved with error probability $P_e^{(N)} \to 0$, for large $N$. Under the same scheme, we also bound $T$ from the other direction, establishing that, for any $T \leq (C_2 - ξ_2) \log N$, the error probability $P_e^{(N)} \to 1$ for large $N$; again $C_2$ and $ξ_2$ are constants and $ξ_2$ can be arbitrarily small. These bounds on the number of steps are lower than the tight achievable lower-bound in terms of $T \geq (C_g +ξ)\log N $ for group testing (in which all active users are identified, rather than just partitioned). Thus, partitioning may prove to be a more efficient approach for multi-access than group testing.

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arXiv2014

Partition Information and its Transmission over Boolean Multi-Access Channels

In this paper, we propose a novel partition reservation system to study the partition information and its transmission over a noise-free Boolean multi-access channel. The objective of transmission is not message restoration, but to partition active users into distinct groups so that they can, subsequently, transmit their messages without collision. We first calculate (by mutual information) the amount of information needed for the partitioning without channel effects, and then propose two different coding schemes to obtain achievable transmission rates over the channel. The first one is the brute force method, where the codebook design is based on centralized source coding; the second method uses random coding where the codebook is generated randomly and optimal Bayesian decoding is employed to reconstruct the partition. Both methods shed light on the internal structure of the partition problem. A novel hypergraph formulation is proposed for the random coding scheme, which intuitively describes the information in terms of a strong coloring of a hypergraph induced by a sequence of channel operations and interactions between active users. An extended Fibonacci structure is found for a simple, but non-trivial, case with two active users. A comparison between these methods and group testing is conducted to demonstrate the uniqueness of our problem.

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