Optimization Software Benchmark

A Computational Toolbox for Linear Optimization with Joint Affine Chance Constraints

Categories Constrained Nonlinear Optimization, Linear Programming, Optimization Software Benchmark Tags , , , ,

We present a Julia computational toolbox for linear optimization problems with joint affine chance constraints under elliptically symmetric uncertainty. The toolbox combines a spherical–radial oracle for estimating the joint probability and its gradient with three structured optimization methods: Proximal, Feasible, and Penalty. The oracle supports several elliptically symmetric distributions and is integrated with these methods … Read more

Unifying nonlinearly constrained nonconvex optimization

Categories Constrained Nonlinear Optimization, Optimization Software Benchmark Tags , , , , , , , , ,

Derivative-based iterative methods for nonlinearly constrained non-convex optimization usually share common algorithmic components, such as strategies for computing a descent direction and mechanisms that promote global convergence. Based on this observation, we introduce an abstract framework based on four common ingredients that describes most derivative-based iterative methods and unifies their workflows. We then present Uno, … Read more

BOBILib: Bilevel Optimization (Benchmark) Instance Library

Categories (Mixed) Integer Linear Programming, Bilevel Optimization, Optimization Software Benchmark Tags , , , ,

In this report, we present the BOBILib, a collection of more than 2600 instances of mixed integer bilevel linear optimization problems (MIBLPs). The goal of this library is to provide a large and well-curated set of test instances freely available for the research community so that new and existing algorithms in bilevel optimization can be … Read more

S2MPJ and CUTEst optimization problems for Matlab, Python and Julia

Categories Nonlinear Optimization, Optimization Software Benchmark Tags , , , , ,

A new decoder for the SIF test problems of the \cutest\ collection is described, which produces problem files allowing the computation of values and derivatives of the objective function and constraints of most \cutest\ problems directly within “native” Matlab, Python or Julia, without any additional installation or interfacing with MEX files or Fortran programs. When … Read more

solar: A solar thermal power plant simulator for blackbox optimization benchmarking

Categories Constrained Nonlinear Optimization, Energy, Optimization Software Benchmark Tags , , ,

This work introduces solar, a collection of  ten optimization problem instances for benchmarking blackbox optimization solvers. The instances present different design aspects of a concentrated solar power plant simulated by blackbox numerical models. The type of variables (discrete or continuous), dimensionality, and number and types of constraints (including hidden constraints)  differ across instances. Some are deterministic, others are stochastic … Read more

Nonconvex optimization problems involving the Euclidean norm: Challenges, progress, and opportunities

Categories Global Optimization, Optimization Software Benchmark Tags , , ,

The field of global optimization has advanced significantly over the past three decades. Yet, the solution of even small instances of many nonconvex optimization problems involving the Euclidean norm to global optimality remains beyond the reach of modern global optimization methods. These problems include numerous well-known and high-impact open research questions from a diverse collection … Read more

A Sequential Benders-based Mixed-Integer Quadratic Programming Algorithm

Categories (Mixed) Integer Nonlinear Programming, Applications - Science and Engineering, Optimization Software Benchmark Tags , , , ,

For continuous decision spaces, nonlinear programs (NLPs) can be efficiently solved via sequential quadratic programming (SQP) and, more generally, sequential convex programming (SCP). These algorithms linearize only the nonlinear equality constraints and keep the outer convex structure of the problem intact, such as (conic) inequality constraints or convex objective terms. The aim of the presented … Read more

Solving low-rank semidefinite programs via manifold optimization

Categories Constrained Nonlinear Optimization, Optimization Software Benchmark, Semi-definite Programming Tags , , , , ,

We propose a manifold optimization approach to solve linear semidefinite programs (SDP) with low-rank solutions. This approach incorporates the augmented Lagrangian method and the Burer-Monteiro factorization, and features the adaptive strategies for updating the factorization size and the penalty parameter. We prove that the present algorithm can solve SDPs to global optimality, despite of the … Read more

Effective matrix adaptation strategy for noisy derivative-free optimization

Categories Global Optimization Applications, Nonlinear Optimization, Optimization Software Benchmark Tags

In this paper, we introduce a new effective matrix adaptation evolution strategy (MADFO) for noisy derivative-free optimization problems. Like every MAES solver, MADFO consists of three phases: mutation, selection and recombination. MADFO improves the mutation phase by generating good step sizes, neither too small nor too large, that increase the probability of selecting mutation points … Read more

Orbital Crossover

Categories Convex Optimization, Linear Programming, Optimization Software Benchmark Tags , ,

Symmetry in optimization has been known to wreak havoc in optimization algorithms. Often, some of the hardest instances are highly symmetric. This is not the case in linear programming, as symmetry allows one to reduce the size of the problem, possibly dramatically, while still maintaining the same optimal objective value. This is done by aggregating … Read more