arXiv subjects

Dorit S. Hochbaum

Publications and source records attributed to Dorit S. Hochbaum.

25 records · Page 2Linked to original sources

arXiv2017

HNCcorr: A Novel Combinatorial Approach for Cell Identification in Calcium-Imaging Movies

Calcium imaging has emerged as a workhorse method in neuroscience to investigate patterns of neuronal activity. Instrumentation to acquire calcium imaging movies has rapidly progressed and has become standard across labs. Still, algorithms to automatically detect and extract activity signals from calcium imaging movies are highly variable from~lab~to~lab and more advanced algorithms are continuously being developed. Here we present HNCcorr, a novel algorithm for cell identification in calcium imaging movies based on combinatorial optimization. The algorithm identifies cells by finding distinct groups of highly similar pixels in correlation space, where a pixel is represented by the vector of correlations to a set of other pixels. The HNCcorr algorithm achieves the best known results for the cell identification benchmark of Neurofinder, and guarantees an optimal solution to the underlying deterministic optimization model resulting in a transparent mapping from input data to outcome.

q-bio.QM↗

arXiv2012

Multiflow Transmission in Delay Constrained Cooperative Wireless Networks

This paper considers the problem of energy-efficient transmission in multi-flow multihop cooperative wireless networks. Although the performance gains of cooperative approaches are well known, the combinatorial nature of these schemes makes it difficult to design efficient polynomial-time algorithms for joint routing, scheduling and power control. This becomes more so when there is more than one flow in the network. It has been conjectured by many authors, in the literature, that the multiflow problem in cooperative networks is an NP-hard problem. In this paper, we formulate the problem, as a combinatorial optimization problem, for a general setting of $k$-flows, and formally prove that the problem is not only NP-hard but it is $o(n^{1/7-ε})$ inapproxmiable. To our knowledge*, these results provide the first such inapproxmiablity proof in the context of multiflow cooperative wireless networks. We further prove that for a special case of k = 1 the solution is a simple path, and devise a polynomial time algorithm for jointly optimizing routing, scheduling and power control. We then use this algorithm to establish analytical upper and lower bounds for the optimal performance for the general case of $k$ flows. Furthermore, we propose a polynomial time heuristic for calculating the solution for the general case and evaluate the performance of this heuristic under different channel conditions and against the analytical upper and lower bounds.

cs.NI↗

arXiv2011

Practical and theoretical improvements for bipartite matching using the pseudoflow algorithm

We show that the pseudoflow algorithm for maximum flow is particularly efficient for the bipartite matching problem both in theory and in practice. We develop several implementations of the pseudoflow algorithm for bipartite matching, and compare them over a wide set of benchmark instances to state-of-the-art implementations of push-relabel and augmenting path algorithms that are specifically designed to solve these problems. The experiments show that the pseudoflow variants are in most cases faster than the other algorithms. We also show that one particular implementation---the matching pseudoflow algorithm---is theoretically efficient. For a graph with $n$ nodes, $m$ arcs, $n_1$ the size of the smaller set in the bipartition, and the maximum matching value $κ\leq n_1$, the algorithm's complexity given input in the form of adjacency lists is $O(\min{n_1κ,m} + \sqrtκ\min{κ^2,m})$. Similar algorithmic ideas are shown to work for an adaptation of Hopcroft and Karp's bipartite matching algorithm with the same complexity. Using boolean operations on words of size $λ$, the complexity of the pseudoflow algorithm is further improved to $O(\min{n_1κ, \frac{n_1n_2}λ, m} + κ^2 + \frac{κ^{2.5}}λ)$. This run time is faster than for previous algorithms such as Cheriyan and Mehlhorn's algorithm of complexity $O(\frac{n^{2.5}}λ)$.

cs.DS↗

arXiv2011

Benchmark Problems for Totally Unimodular Set System Auction

We consider a generalization of the $k$-flow set system auction where the set to be procured by a customer corresponds to a feasible solution to a linear programming problem where the coefficient matrix and right-hand-side together constitute a totally unimodular matrix. Our results generalize and strengthen bounds identified for several benchmarks, which form a crucial component in the study of frugality ratios of truthful auction mechanisms.

cs.GT↗

arXiv2010

Replacing spectral techniques for expander ratio, normalized cut and conductance by combinatorial flow algorithms

Several challenging problem in clustering, partitioning and imaging have traditionally been solved using the "spectral technique". These problems include the normalized cut problem, the graph expander ratio problem, the Cheeger constant problem and the conductance problem. These problems share several common features: all seek a bipartition of a set of elements; the problems are formulated as a form of ratio cut; the formulation as discrete optimization is shown here to be equivalent to a quadratic ratio, sometimes referred to as the Raleigh ratio, on discrete variables and a single sum constraint which we call the balance or orthogonality constraint; when the discrete nature of the variables is disregarded, the continuous relaxation is solved by the spectral method. Indeed the spectral relaxation technique is a dominant method providing an approximate solution to these problems. We propose an algorithm for these problems which involves a relaxation of the orthogonality constraint only. This relaxation is shown here to be solved optimally, and in strongly polynomial time, in O(mn log((n^2) / m) for a graph on $n$ nodes and $m$ edges. The algorithm, using HPF (Hochbaum's Pseudo-Flow) as subroutine, is efficient enough to be used to solve these bi-partitioning problems on millions of elements and more than 300 million edges within less than 10 minutes. It is also demonstrated, via a preliminary experimental study, that the results of the combinatorial algorithm proposed often improve dramatically on the quality of the results of the spectral method.

math.OC↗

arXiv2010

Competitive Analysis of Minimum-Cut Maximum Flow Algorithms in Vision Problems

Rapid advances in image acquisition and storage technology underline the need for algorithms that are capable of solving large scale image processing and computer-vision problems. The minimum cut problem plays an important role in processing many of these imaging problems such as, image and video segmentation, stereo vision, multi-view reconstruction and surface fitting. While several min-cut/max-flow algorithms can be found in the literature, their performance in practice has been studied primarily outside the scope of computer vision. We present here the results of a comprehensive computational study, in terms of execution times and memory utilization, of four recently published algorithms, which optimally solve the {\em s-t} cut and maximum flow problems: (i) Goldberg's and Tarjan's {\em Push-Relabel}; (ii) Hochbaum's {\em pseudoflow}; (iii) Boykov's and Kolmogorov's {\em augmenting paths}; and (iv) Goldberg's {\em partial augment-relabel}. Our results demonstrate that the {\em Hochbaum's pseudoflow} algorithm, is faster and utilizes less memory than the other algorithms on all problem instances investigated.

cs.CV↗

arXiv2008

Polynomial time algorithms for bi-criteria, multi-objective and ratio problems in clustering and imaging. Part I: Normalized cut and ratio regions

Partitioning and grouping of similar objects plays a fundamental role in image segmentation and in clustering problems. In such problems a typical goal is to group together similar objects, or pixels in the case of image processing. At the same time another goal is to have each group distinctly dissimilar from the rest and possibly to have the group size fairly large. These goals are often combined as a ratio optimization problem. One example of such problem is the normalized cut problem, another is the ratio regions problem. We devise here the first polynomial time algorithms solving these problems optimally. The algorithms are efficient and combinatorial. This contrasts with the heuristic approaches used in the image segmentation literature that formulate those problems as nonlinear optimization problems, which are then relaxed and solved with spectral techniques in real numbers. These approaches not only fail to deliver an optimal solution, but they are also computationally expensive. The algorithms presented here use as a subroutine a minimum $s,t-cut procedure on a related graph which is of polynomial size. The output consists of the optimal solution to the respective ratio problem, as well as a sequence of nested solution with respect to any relative weighting of the objectives of the numerator and denominator. An extension of the results here to bi-criteria and multi-criteria objective functions is presented in part II.

cs.CV↗