Ye, "On some efficient interior point methods for nonlinear convex programming," Linear Algebra and its Applications 152 ( 1991 ) 169-189.Kortanek K O,Potra F,Ye Y. On some efficient interior point methods for
integer programmingpolygon fillingThis paper first presents an algorithm for enumerating all the integer-grid points in a given convex m-gon in O(K + m + log n) time where K is the number of such grid points and n is the dimension of the m-gon, i.e., the shorter length of the ...
We consider the constrained vector optimization problem minCf(x),g(x) ∈ −K, wheref: ℝn→ ℝmandg: ℝn→ ℝpare given functions andC∈ ℝmandK∈ ℝpare closed convex cones. Two type of solutions are important for our considerations, namelyi-minimizers (isolated minimizers) ...
Collection of recent methods on (deep) neural network compression and acceleration. Topics deep-neural-networks deep-learning knowledge-distillation model-compression network-pruning efficient-deep-learning Resources Readme License MIT license Activity Stars 947 stars Watchers 52 watching Forks 131...
Several researchers proposed quantum computing methods, especially quantum interior point methods (QIPMs), to solve convex conic optimization problems. Most of them have applied a quantum linear system algorithm at each iteration to compute a Newton step. However, using quantum linear solvers in Q...
Current methods for differentiating optimization problems typically rely on implicit differentiation, which necessitates costly computations on the Jacobian matrices, resulting in low efficiency. In this paper, we introduce BPQP, a differentiable convex optimization framewor...
Considering that there are some efficient methods to deal with second-order cone programming (SOCP) problem, the Lp-norm minimization problem in [2] has been reformulated as a convex SOCP problem [7]. Moreover, by loosening the power constraint, a SOCP problem of mismatched filter design has...
Current methods for differentiating optimization problems typically rely on implicit differentiation, which necessitates costly computations on the Jacobian matrices, resulting in low efficiency. In this paper, we introduce BPQP, a differentiable convex optimization ...
Recently, a new constrained weighted least squares algorithm is proposed in [23] to circumvent the ill-conditioning problem of the Lagrange multiplier methods. However, because of the non-convex nature of the CWLS problem, the Lagrange multiplier method is not able to guarantee the global optimal...
Advanced Computational Methods for Knowledge Engineering, Studies in Computational Intelligence 479, Springer.Efficient Algorithms for Feature Selection in Multi-class Support Vector Machine. Le Thi H A,Nguyen M C. . 2003An, L.T.H., Cuong, N.M.: Efficient algorithms for feature selection in ...