A method called PAINT is introduced for computationally expensive multiobjective optimization problems. The method interpolates between a given set of Pareto optimal outcomes. The interpolation provided by the PAINT method implies a mixed integer linear surrogate problem for the original problem which can be optimized with any interactive method to make decisions concerning the original problem...

In distributed object systems, one generates local surrogate objects to achieve transparent remote method invocations. These surrogates intercept method invocations, transfer the invocations to the actual (remote) object, and invoke the respective method by using so-called stub code. We describe a method which automatically generates surrogate and stub code. The actual generation is delayed unt...

Abstract. In this paper, we propose a family of surrogate maximization (SM) algorithms for multi-class logistic regression models (also called conditional exponential models). An SM algorithm aims at turning an otherwise intractable maximization problem into a tractable one by iterating two steps. The S-step computes a tractable surrogate function to substitute the original objective function, ...

A surrogate model is a black box model that reproduces the output of another more complex model at a single time point. This is to be distinguished from the method of surrogate data, used in time series. The purpose of a surrogate is to reduce the time necessary for a computation at the cost of rigor and generality. We describe a method of constructing surrogates in the form of support vector m...

A machine-learning-based framework for estimating the error introduced by surrogate models of parameterized dynamical systems is proposed. The framework entails the use of high-dimensional regression techniques (e.g., random forests, LASSO) to map a large set of inexpensively computed ‘error indicators’ (i.e., features) produced by the surrogate model at a given time instance to a prediction of...

Optimization of nonlinear (or linear state-dependent) dynamic systems often requires system simulation. In many cases the associated state derivative evaluations are computationally expensive, resulting in simulations that are significantly slower than real-time. This makes the use of optimization techniques in the design of such systems impractical. Optimization of these systems is particularl...

A determinism test is proposed based on the well-known method of the surrogate data. Assuming predictability to be a signature of determinism, the proposed method checks for intracycle (e.g., short-term) determinism in the pseudoperiodic time series for which standard methods of surrogate analysis do not apply. The approach presented is composed of two steps. First, the data are preprocessed to...

In this supplementary section, we describe the Iterated Amplitude Adjusted Fourier Transform (IAAFT) method. This method was adapted for the construction of surrogate glial activities (see ‘Surrogate method’ section in the main text). The IAAFT is known for its applications in physics [1, 2] as well as econometrics [3, 4]. Described below is the procedure for a single loop of IAAFT. Let {G(t)}t...

A p-norm surrogate constraint method is proposed in this paper for integer programming. A single surrogate constraint can be always constructed using p-norm such that the feasible sets in a surrogate relaxation and the primal problem match exactly. The p-norm surro-gate constraint method is thus guaranteed to succeed in identifying the optimal solution of the primal problem with zero duality ga...