Stochastic gradient descent is the method of choice for solving large-scale optimization problems in machine learning. However, the question of how to effectively select the step-sizes in stochastic gradient descent methods is challenging, and can gr...
In this paper, a novel guidance algorithm based on adaptive convex optimization is proposed to ensure robustness in the uncertainty of a lunar lander’s parameters and satisfy the constraints of terminal position, velocity, attitude, and thrust....
With the development of the electric vehicle industry, electric vehicles have provided more choices for people. However, the performance of electric vehicles needs improvement, which makes most consumers take a wait-and-see attitude. Therefore, findi...
In constrained nonlinear optimization, we aim to achieve two goals: one is to minimize the objective function, and the other is to satisfy the constraints. A common way to balance these competing targets is to use penalty functions. Suppose that an a...
In this short paper, we study the problem of traversing a crossbar through a bent channel, which has been formulated as a nonlinear convex optimization problem. The result is a MATLAB code that we can use to compute the maximum length of the crossbar...
This paper is devoted to derive optimality conditions and duality theorems for interval-valued optimization problems based on gH-symmetrically derivative. Further, the concepts of symmetric pseudo-convexity and symmetric quasi-convexity for interval-...
In midcourse guidance, strong constraints and dual-channel control coupling pose major challenges for trajectory optimization. To address this, this paper proposes an optimal guidance method based on terminal relaxation and range convex programming....
The problem of electricity pricing for charging stations is a multi-objective mixed integer nonlinear programming. Existing algorithms have low efficiency in solving this problem. In this paper, a convex optimization algorithm is proposed to get the...
This paper proposes a constrained trajectory optimization framework for autonomous vehicles (AVs) based on convex programming techniques. An enhanced kinematic vehicle model is introduced to capture dynamic motion characteristics that are often overl...
Three-dimensional (3-D) imaging sonar systems require large planar arrays, which incur hardware costs. In contrast, a cross array consisting of two perpendicular linear arrays can also support 3-D imaging while dramatically reducing the number of sen...
Trajectory planning for morphing gliding reentry vehicles is a nonconvex optimization problem driven by nonlinearity, parameter uncertainty, and multiple constraints. No-fly zones (NFZs) are a critical constraint because their rapid movement and expa...
In this paper, we consider convex multiobjective optimization problems with equality and inequality constraints in real Banach space. We establish saddle point necessary and sufficient Pareto optimality conditions for considered problems under some c...
Non-smooth, non-convex optimization problems frequently arise in modern machine learning applications, yet solving them efficiently remains a challenge. This paper addresses the minimization of functions of the form
This paper addresses the critical multiple-uncertainty challenge in laser link acquisition for space gravitational wave detection missions—a key bottleneck where spacecraft attitude alignment for laser link establishment is perturbed by inheren...
The interaction capability provided by the Internet of Things (IoT) significantly increases communication between human and machine, changing our lives gradually. However, the abundant constructions of 5G small base stations (SBSs) and large-scaled a...
This work presents a comprehensive study on formulations for the radiation pattern design of antenna arrays through convex optimization techniques, with a focus on linear, quadratic, and second-order cone programming. The proposed approaches heavily...
In this work, we consider a bilevel optimization problem consisting of the minimizing sum of two convex functions in which one of them is a composition of a convex function and a nonzero linear transformation subject to the set of all feasible points...
The concept of vector topical functions, which take values in a partially ordered Banach space endowed with a complete lattice structure, was introduced in our preceding study. This structure enabled the development of an abstract convexity theory fo...
Amid the exponential growth of streaming data and rising demands for real-time decision-making, Online Convex Optimization (OCO) has emerged as a foundational framework for sequential data processing in dynamic environments. This survey presents a sy...
This paper proposes an Exponentially Varying Whale Optimization Algorithm (EVWOA) to solve the single-objective non-convex Cogeneration Units problem. This problem seeks to evaluate the optimal output of the generator unit to minimize a CHP system’s...
In the development of algorithms for convex optimization problems, symmetry plays a very important role in the approximation of solutions in various real-world problems. In this paper, based on a fixed point algorithm with the inertial technique, we...
The paper describes a convex optimization formulation of the extractive text summarization problem and a simple and scalable algorithm to solve it. The optimization program is constructed as a convex relaxation of an intuitive but computationally har...
We propose a novel distributed method for non-convex optimization problems with coupling equality and inequality constraints. This method transforms the optimization problem into a specific form to allow distributed implementation of modified gradien...
Offshore wind farms have boomed worldwide due to the sustainability of wind power and ocean resources. Power grid companies should consider the wind power consumption problem with more power generated. Power-flow calculation is the most fundamental t...
Due to the limitation of space rover onboard computing resources and energy, there is an urgent need for high-quality drive trajectories in complex environments, which can be provided by delicately designed motion optimization methods. The nonconvexi...
This paper presents new techniques for the trajectory design and control of nonlinear dynamical systems. The technique uses a convex polytope to bound the range of the nonlinear function and associates with each vertex an auxiliary linear system. Pro...
This paper proposes a Distributed Adaptive Regularization Algorithm (DARN) for solving composite non-convex and non-smooth optimization problems in multi-agent systems. The algorithm employs a three-phase iterative framework to achieve efficient coll...
Space telescopes with large diameters or with wide fields require convex secondary mirrors which are mounted at the front of telescope. In this paper, a convex aspheric lightweight SiC mirror and Hindle detection method are presented. A parameter opt...
In real-world applications, multiple robots need to be dynamically deployed to their appropriate locations as teams while the distance cost between robots and goals is minimized, which is known to be an NP-hard problem. In this paper, a new framework...
A novel approach called the nonlinear convex decreasing weights golden eagle optimization technique based on a global optimization strategy is proposed to overcome the limitations of the original golden eagle algorithm, which include slow convergence...
Due to the advantages of fewer energy conversion stages and a simple structure, direct current (DC) microgrids are being increasingly studied and applied. To minimize distribution loss in DC microgrids, a systematic optimal control framework is propo...
The real-time guidance algorithm is the key technology of the powered landing. Given the lack of real-time performance of the convex optimization algorithm with free final time, a lossless convex optimization (LCvx) algorithm based on the deep neural...
There has been increasing interest in the Moon for deep space exploration missions in the last few decades. To accommodate fuel-optimal lunar landing missions, it is essential to develop a fast trajectory planning algorithm considering constrained si...
The paper is devoted to the theoretical and numerical analysis of the two-step method, constructed as a modification of Polyak’s heavy ball method with the inclusion of an additional momentum parameter. For the quadratic case, the convergence c...
The infrared search and track (IRST) system has been widely used, and the field of infrared small target detection has also received much attention. Based on this background, this paper proposes a novel infrared small target detection method based on...
To address the efficiency problem of traditional sequential convex optimization (SCO) methods with uncertain terminal time for long-range hypersonic vehicle reentry, this paper proposes an improved method with neural network-based terminal time predi...
As a multichannel signal processing method based on data-driven, multivariate empirical mode decomposition (MEMD) has attracted much attention due to its potential ability in self-adaption and multi-scale decomposition for multivariate data. Commonly...
Optimizing the operation of active distribution networks (ADNs) has become more challenging because of the uncertainty created by the high penetration level of distributed photovoltaic (PV). From the convex optimization perspective, this paper propos...
Sparse-array synthesis can considerably reduce the number of sensor elements while optimizing the beam-pattern response performance. The sparsity of an array is related to the degrees of freedom of the array elements. A sparse-array method based on i...
Future in-orbit servicing missions, such as spacecraft capture, repair, and assembly, demand robotic systems capable of autonomously computing dynamically feasible, constrained trajectories in real time. Sequential Convex Programming (SCP) has emerge...
Due to the high fog concentration in sea fog images, serious loss of image details is an existing problem, which reduces the reliability of aerial visual-based sensing platforms such as unmanned aerial vehicles. Moreover, the reflection of water surf...
This paper presents a distributed computational framework for stochastic convex optimization problems using the so-called scenario approach. Such a problem arises, for example, in a large-scale network of interconnected linear systems with local and...
The quality of underwater images is an important problem for resource detection. However, the light scattering and plankton in water can impact the quality of underwater images. In this paper, a novel underwater image restoration based on non-convex,...
The alternating direction method is one of the attractive approaches for solving convex optimization problems with linear constraints and separable objective functions. Experience with applications has shown that the number of iterations depends sign...
In this paper, a hybrid optimization method utilizing a pseudo-random algorithm and convex optimization is proposed to avoid grating lobe and achieve lower side lobe level (SLL) of a planar sparse array when the minimum inter-element distance is one...
This paper addresses the economic–environmental dispatch (EED) problem in DC power grids integrating thermoelectric and photovoltaic generation. A multi-objective optimization model is developed to minimize both fuel costs and CO2 emissions whi...
This paper presents the synthesis of a 64 × 64 thinned array, using the branch and bound technique with convex optimization and a low sidelobe level for satellite communication. The branch and bound technology decomposes optimization into several sub...
This paper proposes a comparison study of energy management methods for a parallel plug-in hybrid electric vehicle (PHEV). Based on detailed analysis of the vehicle driveline, quadratic convex functions are presented to describe the nonlinear relatio...
In this paper, we compute a (local) optimal reduced order model that matches a prescribed set of moments of a stable linear time-invariant system of high dimension. We fix the interpolation points and parametrize the models achieving moment-matching...
A well-known and powerful convex optimization strategy is exploited to enhance the electromagnetic performance of the Square Kilometer Array Low-Frequency radio telescope. The proposed method minimizes the peak sidelobe level while ensuring full cont...
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