About

Behçet Açikmeşe is a Professor in the Department of Aeronautics & Astronautics at the University of Washington. He holds an adjunct position in Electrical & Computer Engineering. His research areas include infrastructure and smart cities, controls, convex optimization and its engineering applications, guidance, navigation, and control (GN&C) of autonomous vehicles, and model predictive control (MPC). His work focuses on the development and application of advanced control systems for autonomous vehicles and smart infrastructure, contributing to the fields of aerospace and engineering with a particular emphasis on control theory and optimization techniques.

Research topics

• Computer Science
• Algorithm
• Mathematical optimization
• Mathematics

Selected publications

• Smooth and Exact Parameterization of Continuous-time Signal Temporal Logic Specifications for Trajectory Optimization

  arXiv (Cornell University) · 2026-04-05

  preprintOpen accessSenior author

  This paper presents a smooth parameterization of continuous-time Signal Temporal Logic (CT-STL) specifications for nonconvex trajectory optimization that is sound and complete up to the accuracy of the underlying numerical integration scheme. CT-STL provides a natural framework for encoding rich temporal and logical task requirements, but existing trajectory-optimization formulations typically enforce such specifications only at discrete sampling nodes. In contrast, the proposed method evaluates specifications in dense time, thereby guaranteeing continuous-time satisfaction of always predicates, which is critical for path constraints such as obstacle avoidance, while eliminating the node-induced conservatism of eventually predicates by allowing satisfaction at any time within the prescribed interval. These two dense-time constructions also serve as the main building blocks for handling more general CT-STL formulas, including complex until specifications. Furthermore, the proposed parameterization resolves the locality and gradient-masking issues inherent in standard quantitative semantics, yielding a more favorable landscape for gradient-based solvers. Although dense-time evaluation introduces additional function evaluations during discretization, it also permits substantially coarser temporal grids without sacrificing safety or logical fidelity. This, in turn, reduces the dimension of the resulting nonconvex program, which is often the dominant factor in trajectory-generation cost. The numerical effectiveness and semantic exactness of the proposed framework are demonstrated on an agile quadrotor flight problem subject to a complex continuous-time until specification. The implementation is available at https:// github.com/UW-ACL/TrajOpt_CT-STL

  PublisherDOI

• QOCO-GPU: A Quadratic Objective Conic Optimizer with GPU Acceleration

  arXiv (Cornell University) · 2026-03-31

  preprintOpen accessSenior author

  We present a GPU-accelerated backend for QOCO, a C-based solver for quadratic objective second-order cone programs (SOCPs) based on a primal-dual interior point method. Our backend uses NVIDIA's cuDSS library to perform a direct sparse LDL factorization of the KKT system at each iteration. We also develop custom CUDA kernels for cone operations and show that parallelizing these operations is essential for achieving peak performance. Additionally, we refactor QOCO to introduce a modular backend abstraction that decouples solver logic from the underlying linear algebra implementations, allowing the existing CPU and new GPU backend to share a unified codebase. This GPU backend is accessible through a direct Python interface and through CVXPY, allowing for easy use. Numerical experiments on a range of large-scale quadratic programs and SOCPs with tens to hundreds of millions of nonzero elements in the KKT matrix, demonstrate speedups of up to 50-70 times over the CPU implementation.

  PublisherDOI

• QOCO: a quadratic objective conic optimizer with custom solver generation

  Mathematical Programming Computation · 2026-03-28

  articleSenior author
  PublisherDOI

• QOCO: a quadratic objective conic optimizer with custom solver generation

  Mathematical Programming Computation · 2026-03-28

  preprintOpen accessSenior author
  PublisherOA PDFDOI

• QOCO-GPU: A Quadratic Objective Conic Optimizer with GPU Acceleration

  arXiv (Cornell University) · 2026-03-31

  articleOpen accessSenior author

  We present a GPU-accelerated backend for QOCO, a C-based solver for quadratic objective second-order cone programs (SOCPs) based on a primal-dual interior point method. Our backend uses NVIDIA's cuDSS library to perform a direct sparse LDL factorization of the KKT system at each iteration. We also develop custom CUDA kernels for cone operations and show that parallelizing these operations is essential for achieving peak performance. Additionally, we refactor QOCO to introduce a modular backend abstraction that decouples solver logic from the underlying linear algebra implementations, allowing the existing CPU and new GPU backend to share a unified codebase. This GPU backend is accessible through a direct Python interface and through CVXPY, allowing for easy use. Numerical experiments on a range of large-scale quadratic programs and SOCPs with tens to hundreds of millions of nonzero elements in the KKT matrix, demonstrate speedups of up to 50-70 times over the CPU implementation.

  PublisherOA PDF

• Smooth and Exact Parameterization of Continuous-time Signal Temporal Logic Specifications for Trajectory Optimization

  arXiv (Cornell University) · 2026-04-05

  articleOpen accessSenior author

  This paper presents a smooth parameterization of continuous-time Signal Temporal Logic (CT-STL) specifications for nonconvex trajectory optimization that is sound and complete up to the accuracy of the underlying numerical integration scheme. CT-STL provides a natural framework for encoding rich temporal and logical task requirements, but existing trajectory-optimization formulations typically enforce such specifications only at discrete sampling nodes. In contrast, the proposed method evaluates specifications in dense time, thereby guaranteeing continuous-time satisfaction of always predicates, which is critical for path constraints such as obstacle avoidance, while eliminating the node-induced conservatism of eventually predicates by allowing satisfaction at any time within the prescribed interval. These two dense-time constructions also serve as the main building blocks for handling more general CT-STL formulas, including complex until specifications. Furthermore, the proposed parameterization resolves the locality and gradient-masking issues inherent in standard quantitative semantics, yielding a more favorable landscape for gradient-based solvers. Although dense-time evaluation introduces additional function evaluations during discretization, it also permits substantially coarser temporal grids without sacrificing safety or logical fidelity. This, in turn, reduces the dimension of the resulting nonconvex program, which is often the dominant factor in trajectory-generation cost. The numerical effectiveness and semantic exactness of the proposed framework are demonstrated on an agile quadrotor flight problem subject to a complex continuous-time until specification. The implementation is available at https:// github.com/UW-ACL/TrajOpt_CT-STL

  PublisherOA PDF

• Autotuned Primal–Dual Successive Convexification for Reentry Guidance

  Journal of Guidance Control and Dynamics · 2025-07-31 · 3 citations

  articleSenior author

  This paper presents autotuned primal–dual successive convexification (Auto-SCvx), an algorithm designed to reliably achieve dynamically feasible trajectory solutions for constrained hypersonic reentry optimal control problems across a large mission parameter space. In Auto-SCvx, the authors solve a sequence of convex subproblems until convergence to a solution of the original nonconvex problem. This method iteratively optimizes dual variables in closed form in order to update the penalty hyperparameters used in the primal variable updates. A benefit of this method is that it is autotuning and requires no hand tuning by the user with respect to the constraint penalty weights. Several example hypersonic reentry problems are posed and solved using this method, and comparative studies are conducted against current methods. In these numerical studies, our algorithm demonstrates equal and often improved performance while not requiring hand tuning of penalty hyperparameters.

  PublisherDOI

• A Proximal Method for Composite Optimization with Smooth and Convex Components

  ArXiv.org · 2025-12-22

  articleOpen access

  We introduce prox-convex for minimizing $F(x)=g(x)+h(C(x))+s(R(x))$, where $g$ and $h$ are convex, $C$ and $s$ are smooth, and each component of $R$ is convex (possibly nonsmooth). Here $g$ captures general convex objectives and indicator functions for convex constraints, while the composite template simultaneously models convex penalties on smooth features $(h \circ C)$ and smooth couplings of convex (possibly nonsmooth) features $(s \circ R)$. Each prox-convex step forms a convex subproblem by linearizing only the smooth maps while preserving the existing convex structure. The resulting subproblem is made strongly convex with the proximal metric $Q_k=μ_k I+H_k^+ \succ 0$ where $μ_k$ is adapted using an implicit trust-region strategy, and $H_k^+ \succeq 0$ is an optional curvature term for local acceleration. Under mild Lipschitz/smoothness and a per-coordinate monotone-or-smooth condition, we prove subdifferential regularity, derive two-sided quadratic model error bounds with explicit constants, and obtain sufficient decrease with $O(\varepsilon^{-2})$ complexity for driving the norm of the metric prox-gradient below $\varepsilon$. Furthermore, a local error-bound condition for $F$ guarantees a metric step-size error bound and hence local $Q$-linear convergence of the function values. Using the Taylor-like model framework of Drusvyatskiy, Ioffe, and Lewis, we show that every cluster point of the iterates is limiting-stationary; under our regularity conditions, this further implies Fréchet stationarity. The same framework also establishes robustness to inexact subproblem solves and justifies a model-decrease termination rule.

  PublisherOA PDF

• Continuous-Time Line-of-Sight Constrained Trajectory Planning for 6-Degree of Freedom Systems

  IEEE Robotics and Automation Letters · 2025-02-24 · 1 citations

  article

  Perception algorithms are ubiquitous in modern autonomy stacks, providing necessary environmental information to operate in the real world. Many of these algorithms depend on the visibility of keypoints, which must remain within the robot's line-of-sight (LoS) for reliable operation. This letter tackles the challenge of maintaining LoS on such keypoints during robot movement. We propose a novel method that addresses these issues by ensuring applicability to various sensor footprints, adaptability to arbitrary nonlinear system dynamics, and constant enforcement of LoS throughout the robot's path. Our experiments show that the proposed approach achieves significantly reduced LoS violation and runtime compared to existing state-of-the-art methods in several representative and challenging scenarios.

  PublisherDOI

• Impulsive Relative Motion Control with Continuous-Time Constraint Satisfaction for Cislunar Space Missions

  ArXiv.org · 2025-01-31

  preprintOpen accessSenior author

  Recent investments in cislunar applications open new frontiers for space missions within highly nonlinear dynamical regimes. In this paper, we propose a method based on Sequential Convex Programming (SCP) to loiter around a given target with impulsive actuation while satisfying path constraints continuously over the finite time-horizon, i.e., independently of the number of nodes in which domain is discretized. Location, timing, magnitude, and direction of a fixed number of impulses are optimized in a model predictive framework, exploiting the exact nonlinear dynamics of non-stationary orbital regimes. The proposed approach is first validated on a relative orbiting problem with respect to a selenocentric near rectilinear halo orbit. The approach is then compared to a formulation with path constraints imposed only at nodes and with mesh refined to ensure complete satisfaction of path constraints over the continuous-time horizon. CPU time per iteration of 400 ms for the refined-mesh approach reduce to 5.5 ms for the proposed approach.

  PublisherOA PDFDOI

Frequent coauthors

• Purnanand Elango

  8 shared

• Danylo Malyuta

  University of Washington

  8 shared

• Taewan Kim

  University of Washington

  4 shared

• Taewan Kim

  4 shared

• Edward Mettler

  California Institute of Technology

  1 shared

• Jordi Casoliva

  1 shared

• W. G. Breckenridge

  1 shared

• Fred Y. Hadaegh

  1 shared

Awards & honors

• Samuel Buckner wins AIAA GNC Award
• Best GNC Paper Award (2026)
• Auto-tuning algorithm Skye Mceowen wins AIAA award