arXiv subjects

Ishan Bansal

Publications and source records attributed to Ishan Bansal.

14 recordsLinked to original sources

arXiv2026

Warehouse Problem with Bounds, Fixed Costs and Complementarity Constraints

This paper studies an open question in the warehouse problem where a merchant trading a commodity tries to find an optimal inventory-trading policy to decide on purchase and sale quantities during a fixed time horizon in order to maximize their total pay-off, making use of fluctuations in sale and cost prices. We provide the first known polynomial-time algorithms for the case when there are fixed costs for purchases and sales, optional complementarity constraints that prohibit purchasing and selling during the same time period, and bounds on purchase and sales quantities. We do so by providing an exact characterization of the extreme points of the feasible region and using this to construct a suitable network where a min-cost flow computation provides an optimal solution. We are also able to provide polynomial extended linear formulations for the original feasible regions. Our methods build on the work by Wolsey and Yaman (Discrete Optimization 2018). We also consider the problem without fixed costs and provide a fully polynomial time approximation scheme in a setting with time-dependent bounds.

cs.DS↗

arXiv2026

Improved Approximation Algorithms for Capacitated Network Design and Flexible Graph Connectivity

We present improved approximation algorithms for some problems in the related areas of Capacitated Network Design and Flexible Graph Connectivity. In the Cap-$k$-ECSS problem, we are given a graph $G=(V,E)$ whose edges have non-negative costs and positive integer capacities, and the goal is to find a minimum-cost edge-set $F$ such that every non-trivial cut of the graph $G'=(V,F)$ has capacity at least $k$. We present an $O(\log k)$-approximation algorithm for the Cap-$k$-ECSS problem, asymptotically improving upon the previous best approximation ratio of $\min(O(\log n),\; O(k))$ whenever $\log(k)=o(\log n)$, where $n$ denotes $|V|$. (See section 1, for a detailed discussion.) In the $(p,q)$-Flexible Graph Connectivity problem, denoted $(p,q)$-FGC, the input is a graph $G(V, E)$ where $E$ is partitioned into safe and unsafe edges, and the goal is to find a minimum cost set of edges $F$ such that the subgraph $G'(V, F)$ remains $p$-edge connected upon removal of any $q$ unsafe edges from $F$. We design a $7$-approximation algorithm for the $(1,q)$-FGC problem, improving on the previous best approximation ratio of $(q+1)$. Both of our results are obtained by using natural LP relaxations strengthened with the knapsack-cover inequalities, and then, during the rounding process, utilizing a recent $O(1)$-approximation algorithm for the Cover$\;$Small$\;$Cuts problem. In the latter problem, the goal is to find a minimum-cost set of links such that each non-trivial cut of capacity less than a specified value is covered by a link. We also show that the problem of covering small cuts inherently arises in another variant of $(p,q)$-FGC. Specifically, we give Cook reductions that preserve approximation ratios within $O(1)$ factors between the $(2,q)$-FGC problem and the 2-Cover$\;$Small$\;$Cuts problem; in the latter problem, each small cut needs to be covered by two links.

cs.DS↗

arXiv2026

A $5$-Approximation Analysis for the Cover Small Cuts Problem

In the Cover Small Cuts problem, we are given a capacitated (undirected) graph $G=(V,E,u)$ and a threshold value $λ$, as well as a set of links $L$ with end-nodes in $V$ and a non-negative cost for each link $\ell\in L$; the goal is to find a minimum-cost set of links such that each non-trivial cut of capacity less than $λ$ is covered by a link. Bansal, Cheriyan, Grout, and Ibrahimpur (arXiv:2209.11209, Algorithmica 2024) showed that the WGMV primal-dual algorithm, due to Williamson, Goemans, Mihail, and Vazirani (Combinatorica, 1995), achieves approximation ratio $16$ for the Cover Small Cuts problem; their analysis uses the notion of a pliable family of sets that satisfies a combinatorial property. Later, Bansal (arXiv:2308.15714v2, IPCO 2025) and then Nutov (arXiv:2504.03910, MFCS 2025) proved that the same algorithm achieves approximation ratio $6$. We show that the same algorithm achieves approximation ratio $5$, by using a stronger notion, namely, a pliable family of sets that satisfies symmetry and structural submodularity.

cs.DS↗

arXiv2025

Symmetric Submodular Functions, Uncrossable Functions, and Structural Submodularity

Diestel, et al. (see Order 35 (2017), JCT-A 167 (2019), arXiv:1805.01439) introduced the notion of abstract separation systems that satisfy a submodularity property, and they call this structural submodularity. Williamson, Goemans, Mihail, and Vazirani (Combinatorica 15 (1995)) call a family of sets $\mathcal{F}$ uncrossable if the following holds: for any pair of sets $A,B\in\mathcal{F}$, both $A\cap{B},A\cup{B}$ are in $\mathcal{F}$, or both $A-B,B-A$ are in $\mathcal{F}$. Bansal, Cheriyan, Grout, and Ibrahimpur (Algorithmica 86 (2024), arXiv:2209.11209) call a family of sets $\mathcal{F}$ pliable if the following holds: for any pair of sets $A,B\in\mathcal{F}$, at least two of the sets $A\cap{B},A\cup{B},A-B,B-A$ are in $\mathcal{F}$. We say that a pliable family of sets $\mathcal{F}$ satisfies structural submodularity if the following holds: for any pair of crossing sets $A,B\in\mathcal{F}$, at least one of the sets $A\cap{B},A\cup{B}$ is in $\mathcal{F}$, and at least one of the sets $A-B,B-A$ is in $\mathcal{F}$. For any positive integer $d\geq2$, we construct a pliable family of sets $\mathcal{F}$ that satisfies structural submodularity such that (a) there do not exist a symmetric submodular function $g$ and $λ\in{\mathbb Q}$ such that $\mathcal{F} = \{ S \,:\, g(S)<λ\}$, and (b) $\mathcal{F}$ cannot be partitioned into $d$ (or fewer) uncrossable families.

cs.DM↗

arXiv2025

Parallel Token Swapping for Qubit Routing

In this paper we study a combinatorial reconfiguration problem that involves finding an optimal sequence of swaps to move an initial configuration of tokens that are placed on the vertices of a graph to a final desired one. This problem arises as a crucial step in reducing the depth of a quantum circuit when compiling a quantum algorithm. We provide the first known constant factor approximation algorithms for the parallel token swapping problem on graph topologies that are commonly found in modern quantum computers, including cycle graphs, subdivided star graphs, and grid graphs. We also study the so-called stretch factor of a natural lower bound to the problem, which has been shown to be useful when designing heuristics for the qubit routing problem. Finally, we study the colored version of this reconfiguration problem where some tokens share the same color and are considered indistinguishable.

cs.DS↗

arXiv2025

A Bad Example for Jain's Iterative Rounding Theorem for the Cover Small Cuts Problem

Jain's iterative rounding theorem is a well-known result in the area of approximation algorithms and, more broadly, in combinatorial optimization. The theorem asserts that LP relaxations of several problems in network design and combinatorial optimization have the following key property: for every basic solution $x$ there exists a variable $x_e$ that has value at least a constant (e.g., $x_e\geq\frac12$). We construct an example showing that this property fails to hold for the Cover Small Cuts problem. In this problem, we are given an undirected, capacitated graph $G=(V,E),u$ and a threshold value $λ$, as well as a set of links $L$ with end-nodes in $V$ and a non-negative cost for each link $\ell\in L$; the goal is to find a minimum-cost set of links such that each non-trivial cut of capacity less than $λ$ is covered by a link. This indicates that the polyhedron of feasible solutions to the LP relaxation (of Cover Small Cuts) differs in an essential way from the polyhedrons associated with several problems in combinatorial optimization. Moreover, our example shows that a direct application of Jain's iterative rounding algorithm does not give an $O(1)$ approximation algorithm for Cover Small Cuts. We mention that Bansal et al. (Algorithmica 2024) present an $O(1)$ approximation algorithm for Cover Small Cuts based on the primal-dual method of Williamson et al. (Combinatorica 1995).

cs.DS↗

arXiv2025

A Global Analysis of the Primal-Dual Method for Pliable Families

We study a core algorithmic problem in network design called ${F}$-augmentation that involves increasing the connectivity of a given family of cuts ${F}$. Over 30 years ago, Williamson et al. (STOC `93) provided a 2-approximation primal-dual algorithm when ${F}$ is a so-called uncrossable family but extending their results to families that are non-uncrossable has remained a challenging question. In this paper, we introduce the novel concept of the crossing density of a set family and show how this opens up a completely new approach to analyzing primal-dual algorithms. We study pliable families, a strict generalization of uncrossable families introduced by Bansal et al. (ICALP `23), and provide the first approximation algorithm for ${F}$-augmentation of general pliable families. We also improve on the results in Bansal et al. (ICALP `23) by providing a 6-approximation algorithm for the ${F}$-augmentation problem when ${F}$ is a family of near min-cuts. This immediately improves approximation factors for the Capacitated Network Design Problem. Finally, we study the $(p,3)$-flexible graph connectivity problem. By carefully analyzing the structure of feasible solutions and using the techniques developed in this paper, we provide the first constant factor approximation algorithm for this problem exhibiting an 12-approximation algorithm.

cs.DS↗

arXiv2024

Network Design on Undirected Series-Parallel Graphs

We study the single pair capacitated network design problem and the budget constrained max flow problem on undirected series-parallel graphs. These problems were well studied on directed series-parallel graphs, but little is known in the context of undirected graphs. The major difference between the cases is that the source and sink of the problem instance do not necessarily coincide with the terminals of the underlying series-parallel graph in the undirected case, thus creating certain complications. We provide pseudopolynomial time algorithms to solve both of the problems and provide an FPTAS for the budget constrained max flow problem. We also provide some extensions, arguing important cases when the problems are polynomial-time solvable, and describing a series-parallel gadget that captures an edge upgrade version of the problems.

cs.DS↗

arXiv2024

Warehouse Problem with Multiple Vendors and Generalized Complementarity Constraints

We study the warehouse problem, arising in the area of inventory management and production planning. Here, a merchant wants to decide an optimal trading policy that computes quantities of a single commodity to purchase, store and sell during each time period of a finite discrete time horizon. Motivated by recent applications in energy markets, we extend the models by Wolsey and Yaman (2018) and Bansal and Günlük (2023) and consider markets with multiple vendors and a more general form of the complementarity constraints. We show that these extensions can capture various practical conditions such as surge pricing and discounted sales, ramp-up and ramp-down constraints and batch pricing. We analyze the extreme points of the underlying non-linear integer program and provide an algorithm that exactly solves the problem. Our algorithm runs in polynomial time under reasonable practical conditions. We also show that the absence of such conditions renders the problem NP-Hard.

cs.DS↗

arXiv2024

Improved Approximation Algorithms by Generalizing the Primal-Dual Method Beyond Uncrossable Functions

We address long-standing open questions raised by Williamson, Goemans, Vazirani and Mihail pertaining to the design of approximation algorithms for problems in network design via the primal-dual method (Combinatorica 15(3):435-454, 1995). Williamson et al. prove an approximation guarantee of two for connectivity augmentation problems where the connectivity requirements can be specified by so-called uncrossable functions. They state: ``Extending our algorithm to handle non-uncrossable functions remains a challenging open problem. The key feature of uncrossable functions is that there exists an optimal dual solution which is laminar. This property characterizes uncrossable functions\dots\ A larger open issue is to explore further the power of the primal-dual approach for obtaining approximation algorithms for other combinatorial optimization problems.'' Our main result proves that the primal-dual algorithm of Williamson et al. achieves an approximation ratio of 16 for a class of functions that generalizes the notion of an uncrossable function. There exist instances that can be handled by our methods where none of the optimal dual solutions has a laminar support. We present three applications of our main result. (1) A 16-approximation algorithm for augmenting a family of small cuts of a graph $G$. (2) A $16 \cdot {\lceil k/u_{min} \rceil}$-approximation algorithm for the Cap-$k$-ECSS problem which is as follows: Given an undirected graph $G = (V,E)$ with edge costs $c \in \mathbb{Q}_{\geq 0}^E$ and edge capacities $u \in \mathbb{Z}_{\geq 0}^E$, find a minimum-cost subset of the edges $F\subseteq E$ such that the capacity of any cut in $(V,F)$ is at least $k$; we use $u_{min}$ to denote the minimum capacity of an edge in $E$. (3) An $O(1)$-approximation algorithm for the model of $(p,2)$-Flexible Graph Connectivity.

cs.DS↗

arXiv2022

Extensions of the $(p,q)$-Flexible-Graph-Connectivity model

We present approximation algorithms for network design problems in some models related to the $(p,q)$-FGC model. Adjiashvili, Hommelsheim and Mühlenthaler introduced the model of Flexible Graph Connectivity that we denote by FGC. Boyd, Cheriyan, Haddadan and Ibrahimpur introduced a generalization of FGC. Let $p\geq 1$ and $q\geq 0$ be integers. In an instance of the $(p,q)$-Flexible Graph Connectivity problem, denoted $(p,q)$-FGC, we have an undirected connected graph $G = (V,E)$, a partition of $E$ into a set of safe edges and a set of unsafe edges, and nonnegative costs $c\in\mathbb{R}_{\geq0}^E$ on the edges. A subset $F \subseteq E$ of edges is feasible for the $(p,q)$-FGC problem if for any set of unsafe edges, $F'$, with $|F'|\leq q$, the subgraph $(V, F \setminus F')$ is $p$-edge connected. The algorithmic goal is to find a feasible edge-set $F$ that minimizes $c(F) = \sum_{e \in F} c_e$.

cs.DS↗

arXiv2022

Algorithms for 2-connected network design and flexible Steiner trees with a constant number of terminals

The $k$-Steiner-2NCS problem is as follows: Given a constant $k$, and an undirected connected graph $G = (V,E)$, non-negative costs $c$ on $E$, and a partition $(T, V-T)$ of $V$ into a set of terminals, $T$, and a set of non-terminals (or, Steiner nodes), where $|T|=k$, find a minimum-cost two-node connected subgraph that contains the terminals. We present a randomized polynomial-time algorithm for the unweighted problem, and a randomized PTAS for the weighted problem. We obtain similar results for the $k$-Steiner-2ECS problem, where the input is the same, and the algorithmic goal is to find a minimum-cost two-edge connected subgraph that contains the terminals. Our methods build on results by Björklund, Husfeldt, and Taslaman (ACM-SIAM SODA 2012) that give a randomized polynomial-time algorithm for the unweighted $k$-Steiner-cycle problem; this problem has the same inputs as the unweighted $k$-Steiner-2NCS problem, and the algorithmic goal is to find a minimum-size simple cycle $C$ that contains the terminals ($C$ may contain any number of Steiner nodes).

cs.DS↗

arXiv2020

On the Polarizing Behavior and Scaling Exponent of Polar Codes with Product Kernels

Polar codes, introduced by Arikan, achieve the capacity of arbitrary binary-input discrete memoryless channel $W$ under successive cancellation decoding. Any such channel having capacity $I(W)$ and for any coding scheme allowing transmission at rate $R$, scaling exponent is a parameter which characterizes how fast gap to capacity decreases as a function of code length $N$ for a fixed probability of error. The relation between them is given by $N\geqslant α/(I(W)-R)^μ$. Scaling exponent for kernels of small size up to $L=8$ have been exhaustively found. In this paper, we consider product kernels $T_{L}$ obtained by taking Kronecker product of component kernels. We derive the properties of polarizing product kernels relating to number of product kernels, self duality and partial distances in terms of the respective properties of the smaller component kernels. Subsequently, polarization behavior of component kernel $T_{l}$ is used to calculate scaling exponent of $T_{L}=T_{2}\otimes T_{l}$. Using this method, we show that $μ(T_{2}\otimes T_{5})=3.942.$ Further, we employ a heuristic approach to construct good kernel of $L=14$ from kernel having size $l=8$ having best $μ$ and find $μ(T_{2}\otimes T_{7})=3.485.$

cs.IT↗

arXiv2018

DeCoILFNet: Depth Concatenation and Inter-Layer Fusion based ConvNet Accelerator

Convolutional Neural Networks (CNNs) are rapidly gaining popularity in varied fields. Due to their increasingly deep and computationally heavy structures, it is difficult to deploy them on energy constrained mobile applications. Hardware accelerators such as FPGAs have come up as an attractive alternative. However, with the limited on-chip memory and computation resources of FPGA, meeting the high memory throughput requirement and exploiting the parallelism of CNNs is a major challenge. We propose a high-performance FPGA based architecture - Depth Concatenation and Inter-Layer Fusion based ConvNet Accelerator - DeCoILFNet which exploits the intra-layer parallelism of CNNs by flattening across depth and combines it with a highly pipelined data flow across the layers enabling inter-layer fusion. This architecture significantly reduces off-chip memory accesses and maximizes the throughput. Compared to a 3.5GHz hexa-core Intel Xeon E7 caffe-implementation, our 120MHz FPGA accelerator is 30X faster. In addition, our design reduces external memory access by 11.5X along with a speedup of more than 2X in the number of clock cycles compared to state-of-the-art FPGA accelerators.

cs.DC↗