A main contribution is the development of a sequential convex optimization algorithm, where at each iteration step, a convex subproblem with linear matrix inequality (LMI) constraints is solved. The set of feasible points of the LMIs is a convex inner approximation of the set of feasible points ...
Application of the sequential parametric convex approximation method to the design of robust trusses Article 01 September 2016 Quadratic Multipoint Exponential Approximation: Surrogate Model for Large-Scale Optimization Chapter © 2018 An Algorithm for Constrained Optimization with Applications to the ...
concept, which aims to achieve a flexible and efficient recursive approximation solution of the output weights by employing ingenious partition solving strategy and approximate calculation method, thus significantly improving the learning efficiency of the algorithm while preserving satisfactory learning accuracy...
In this section, we first provide the optimality conditions of the nonlinear optimization problem P1 in the context of interior point methods and then prove all the basic SCP (Algorithm 1), Newton-type rSCP (Algorithm 2), and inexact Newton-type rSCP (Algorithm 3) can solve for a local opt...
At each iteration of the algorithm, a convex subproblem is constructed by forming a nonlinear, convex approximation of the penalized compliance based on a linearization of the stiffness matrix. Subsequent solutions of the convex subproblems form a non-increasing sequence of compliance values. The ...
A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. In other words: Supervised Learning learns from a set of labeled examples. From the instances and the labels, supervised learning models try to find the correla...
A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. In other words: Supervised Learning learns from a set of labeled examples. From the instances and the labels, supervised learning models try to find the correla...
We describe an efficient algorithm that finds an optimal agenda in the important special case when the revenue of each auction is guaranteed to be strictly positive. We also show that the seller can increase his revenue by canceling one or more auctions, even if the number of bidders exceeds ...
The MPC strategy for driving a chaser to a non-cooperative satellite is investigated with OCP enforced by a spherical obstacle constraint in [6], where the quadratic constraint is approximated as a time-varying linear inequality and the relaxed OCP is solved by QP algorithm. Linear approximations...
2.1.1Finite Element Approximation and Error Estimates Consider the 1D piecewise linear nodal basis functions\phi _j^Kdefined as follows, for mesh\{z^K_i = i / (K+1) \}_{i=0}^{K+1}, and forj=1,\dots ,K, \begin{aligned} \phi ^K_j(z) = {\left\{ \begin{array}{ll} \fr...