DeepAI AI Chat
Log In Sign Up

A Primal-Dual Solver for Large-Scale Tracking-by-Assignment

by   Stefan Haller, et al.

We propose a fast approximate solver for the combinatorial problem known as tracking-by-assignment, which we apply to cell tracking. The latter plays a key role in discovery in many life sciences, especially in cell and developmental biology. So far, in the most general setting this problem was addressed by off-the-shelf solvers like Gurobi, whose run time and memory requirements rapidly grow with the size of the input. In contrast, for our method this growth is nearly linear. Our contribution consists of a new (1) decomposable compact representation of the problem; (2) dual block-coordinate ascent method for optimizing the decomposition-based dual; and (3) primal heuristics that reconstructs a feasible integer solution based on the dual information. Compared to solving the problem with Gurobi, we observe an up to 60 times speed-up, while reducing the memory footprint significantly. We demonstrate the efficacy of our method on real-world tracking problems.


A Dual Ascent Framework for Lagrangean Decomposition of Combinatorial Problems

We propose a general dual ascent framework for Lagrangean decomposition ...

FastDOG: Fast Discrete Optimization on GPU

We present a massively parallel Lagrange decomposition method for solvin...

A nonsmooth primal-dual method with simultaneous adaptive PDE constraint solver

We introduce an efficient first-order primal-dual method for the solutio...

Doubly Stochastic Primal-Dual Coordinate Method for Bilinear Saddle-Point Problem

We propose a doubly stochastic primal-dual coordinate optimization algor...

Cell tracking for live-cell microscopy using an activity-prioritized assignment strategy

Cell tracking is an essential tool in live-cell imaging to determine sin...

Robust and efficient primal-dual Newton-Krylov solvers for viscous-plastic sea-ice models

We present a Newton-Krylov solver for a viscous-plastic sea-ice model. T...